Merge branch 'add-runpod-AI-API' - RunPod AI integration with image and video generation
This commit is contained in:
commit
67d4db7281
11
.env.example
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.env.example
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@ -4,6 +4,17 @@ VITE_GOOGLE_MAPS_API_KEY='your_google_maps_api_key'
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VITE_DAILY_DOMAIN='your_daily_domain'
|
||||
VITE_TLDRAW_WORKER_URL='your_worker_url'
|
||||
|
||||
# AI Orchestrator (Primary - Netcup RS 8000)
|
||||
VITE_AI_ORCHESTRATOR_URL='http://159.195.32.209:8000'
|
||||
# Or use domain when DNS is configured:
|
||||
# VITE_AI_ORCHESTRATOR_URL='https://ai-api.jeffemmett.com'
|
||||
|
||||
# RunPod API (Fallback/Direct Access)
|
||||
VITE_RUNPOD_API_KEY='your_runpod_api_key_here'
|
||||
VITE_RUNPOD_TEXT_ENDPOINT_ID='your_text_endpoint_id'
|
||||
VITE_RUNPOD_IMAGE_ENDPOINT_ID='your_image_endpoint_id'
|
||||
VITE_RUNPOD_VIDEO_ENDPOINT_ID='your_video_endpoint_id'
|
||||
|
||||
# Worker-only Variables (Do not prefix with VITE_)
|
||||
CLOUDFLARE_API_TOKEN='your_cloudflare_token'
|
||||
CLOUDFLARE_ACCOUNT_ID='your_account_id'
|
||||
|
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|
|||
|
|
@ -0,0 +1,626 @@
|
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# AI Services Deployment & Testing Guide
|
||||
|
||||
Complete guide for deploying and testing the AI services integration in canvas-website with Netcup RS 8000 and RunPod.
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Overview
|
||||
|
||||
This project integrates multiple AI services with smart routing:
|
||||
|
||||
**Smart Routing Strategy:**
|
||||
- **Text/Code (70-80% workload)**: Local Ollama on RS 8000 → **FREE**
|
||||
- **Images - Low Priority**: Local Stable Diffusion on RS 8000 → **FREE** (slow ~60s)
|
||||
- **Images - High Priority**: RunPod GPU (SDXL) → **$0.02/image** (fast ~5s)
|
||||
- **Video Generation**: RunPod GPU (Wan2.1) → **$0.50/video** (30-90s)
|
||||
|
||||
**Expected Cost Savings:** $86-350/month compared to persistent GPU instances
|
||||
|
||||
---
|
||||
|
||||
## 📦 What's Included
|
||||
|
||||
### AI Services:
|
||||
1. ✅ **Text Generation (LLM)**
|
||||
- RunPod integration via `src/lib/runpodApi.ts`
|
||||
- Enhanced LLM utilities in `src/utils/llmUtils.ts`
|
||||
- AI Orchestrator client in `src/lib/aiOrchestrator.ts`
|
||||
- Prompt shapes, arrow LLM actions, command palette
|
||||
|
||||
2. ✅ **Image Generation**
|
||||
- ImageGenShapeUtil in `src/shapes/ImageGenShapeUtil.tsx`
|
||||
- ImageGenTool in `src/tools/ImageGenTool.ts`
|
||||
- Mock mode **DISABLED** (ready for production)
|
||||
- Smart routing: low priority → local CPU, high priority → RunPod GPU
|
||||
|
||||
3. ✅ **Video Generation (NEW!)**
|
||||
- VideoGenShapeUtil in `src/shapes/VideoGenShapeUtil.tsx`
|
||||
- VideoGenTool in `src/tools/VideoGenTool.ts`
|
||||
- Wan2.1 I2V 14B 720p model on RunPod
|
||||
- Always uses GPU (no local option)
|
||||
|
||||
4. ✅ **Voice Transcription**
|
||||
- WhisperX integration via `src/hooks/useWhisperTranscriptionSimple.ts`
|
||||
- Automatic fallback to local Whisper model
|
||||
|
||||
---
|
||||
|
||||
## 🚀 Deployment Steps
|
||||
|
||||
### Step 1: Deploy AI Orchestrator on Netcup RS 8000
|
||||
|
||||
**Prerequisites:**
|
||||
- SSH access to Netcup RS 8000: `ssh netcup`
|
||||
- Docker and Docker Compose installed
|
||||
- RunPod API key
|
||||
|
||||
**1.1 Create AI Orchestrator Directory:**
|
||||
|
||||
```bash
|
||||
ssh netcup << 'EOF'
|
||||
mkdir -p /opt/ai-orchestrator/{services/{router,workers,monitor},configs,data/{redis,postgres,prometheus}}
|
||||
cd /opt/ai-orchestrator
|
||||
EOF
|
||||
```
|
||||
|
||||
**1.2 Copy Configuration Files:**
|
||||
|
||||
From your local machine, copy the AI orchestrator files created in `NETCUP_MIGRATION_PLAN.md`:
|
||||
|
||||
```bash
|
||||
# Copy docker-compose.yml
|
||||
scp /path/to/docker-compose.yml netcup:/opt/ai-orchestrator/
|
||||
|
||||
# Copy service files
|
||||
scp -r /path/to/services/* netcup:/opt/ai-orchestrator/services/
|
||||
```
|
||||
|
||||
**1.3 Configure Environment Variables:**
|
||||
|
||||
```bash
|
||||
ssh netcup "cat > /opt/ai-orchestrator/.env" << 'EOF'
|
||||
# PostgreSQL
|
||||
POSTGRES_PASSWORD=$(openssl rand -hex 16)
|
||||
|
||||
# RunPod API Keys
|
||||
RUNPOD_API_KEY=your_runpod_api_key_here
|
||||
RUNPOD_TEXT_ENDPOINT_ID=your_text_endpoint_id
|
||||
RUNPOD_IMAGE_ENDPOINT_ID=your_image_endpoint_id
|
||||
RUNPOD_VIDEO_ENDPOINT_ID=your_video_endpoint_id
|
||||
|
||||
# Grafana
|
||||
GRAFANA_PASSWORD=$(openssl rand -hex 16)
|
||||
|
||||
# Monitoring
|
||||
ALERT_EMAIL=your@email.com
|
||||
COST_ALERT_THRESHOLD=100
|
||||
EOF
|
||||
```
|
||||
|
||||
**1.4 Deploy the Stack:**
|
||||
|
||||
```bash
|
||||
ssh netcup << 'EOF'
|
||||
cd /opt/ai-orchestrator
|
||||
|
||||
# Start all services
|
||||
docker-compose up -d
|
||||
|
||||
# Check status
|
||||
docker-compose ps
|
||||
|
||||
# View logs
|
||||
docker-compose logs -f router
|
||||
EOF
|
||||
```
|
||||
|
||||
**1.5 Verify Deployment:**
|
||||
|
||||
```bash
|
||||
# Check health endpoint
|
||||
ssh netcup "curl http://localhost:8000/health"
|
||||
|
||||
# Check API documentation
|
||||
ssh netcup "curl http://localhost:8000/docs"
|
||||
|
||||
# Check queue status
|
||||
ssh netcup "curl http://localhost:8000/queue/status"
|
||||
```
|
||||
|
||||
### Step 2: Setup Local AI Models on RS 8000
|
||||
|
||||
**2.1 Download Ollama Models:**
|
||||
|
||||
```bash
|
||||
ssh netcup << 'EOF'
|
||||
# Download recommended models
|
||||
docker exec ai-ollama ollama pull llama3:70b
|
||||
docker exec ai-ollama ollama pull codellama:34b
|
||||
docker exec ai-ollama ollama pull deepseek-coder:33b
|
||||
docker exec ai-ollama ollama pull mistral:7b
|
||||
|
||||
# Verify
|
||||
docker exec ai-ollama ollama list
|
||||
|
||||
# Test a model
|
||||
docker exec ai-ollama ollama run llama3:70b "Hello, how are you?"
|
||||
EOF
|
||||
```
|
||||
|
||||
**2.2 Download Stable Diffusion Models:**
|
||||
|
||||
```bash
|
||||
ssh netcup << 'EOF'
|
||||
mkdir -p /data/models/stable-diffusion/sd-v2.1
|
||||
cd /data/models/stable-diffusion/sd-v2.1
|
||||
|
||||
# Download SD 2.1 weights
|
||||
wget https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/v2-1_768-ema-pruned.safetensors
|
||||
|
||||
# Verify
|
||||
ls -lh v2-1_768-ema-pruned.safetensors
|
||||
EOF
|
||||
```
|
||||
|
||||
**2.3 Download Wan2.1 Video Generation Model:**
|
||||
|
||||
```bash
|
||||
ssh netcup << 'EOF'
|
||||
# Install huggingface-cli
|
||||
pip install huggingface-hub
|
||||
|
||||
# Download Wan2.1 I2V 14B 720p
|
||||
mkdir -p /data/models/video-generation
|
||||
cd /data/models/video-generation
|
||||
|
||||
huggingface-cli download Wan-AI/Wan2.1-I2V-14B-720P \
|
||||
--include "*.safetensors" \
|
||||
--local-dir wan2.1_i2v_14b
|
||||
|
||||
# Check size (~28GB)
|
||||
du -sh wan2.1_i2v_14b
|
||||
EOF
|
||||
```
|
||||
|
||||
**Note:** The Wan2.1 model will be deployed to RunPod, not run locally on CPU.
|
||||
|
||||
### Step 3: Setup RunPod Endpoints
|
||||
|
||||
**3.1 Create RunPod Serverless Endpoints:**
|
||||
|
||||
Go to [RunPod Serverless](https://www.runpod.io/console/serverless) and create endpoints for:
|
||||
|
||||
1. **Text Generation Endpoint** (optional, fallback)
|
||||
- Model: Any LLM (Llama, Mistral, etc.)
|
||||
- GPU: Optional (we use local CPU primarily)
|
||||
|
||||
2. **Image Generation Endpoint**
|
||||
- Model: SDXL or SD3
|
||||
- GPU: A4000/A5000 (good price/performance)
|
||||
- Expected cost: ~$0.02/image
|
||||
|
||||
3. **Video Generation Endpoint**
|
||||
- Model: Wan2.1-I2V-14B-720P
|
||||
- GPU: A100 or H100 (required for video)
|
||||
- Expected cost: ~$0.50/video
|
||||
|
||||
**3.2 Get Endpoint IDs:**
|
||||
|
||||
For each endpoint, copy the endpoint ID from the URL or endpoint details.
|
||||
|
||||
Example: If URL is `https://api.runpod.ai/v2/jqd16o7stu29vq/run`, then `jqd16o7stu29vq` is your endpoint ID.
|
||||
|
||||
**3.3 Update Environment Variables:**
|
||||
|
||||
Update `/opt/ai-orchestrator/.env` with your endpoint IDs:
|
||||
|
||||
```bash
|
||||
ssh netcup "nano /opt/ai-orchestrator/.env"
|
||||
|
||||
# Add your endpoint IDs:
|
||||
RUNPOD_TEXT_ENDPOINT_ID=your_text_endpoint_id
|
||||
RUNPOD_IMAGE_ENDPOINT_ID=your_image_endpoint_id
|
||||
RUNPOD_VIDEO_ENDPOINT_ID=your_video_endpoint_id
|
||||
|
||||
# Restart services
|
||||
cd /opt/ai-orchestrator && docker-compose restart
|
||||
```
|
||||
|
||||
### Step 4: Configure canvas-website
|
||||
|
||||
**4.1 Create .env.local:**
|
||||
|
||||
In your canvas-website directory:
|
||||
|
||||
```bash
|
||||
cd /home/jeffe/Github/canvas-website-branch-worktrees/add-runpod-AI-API
|
||||
|
||||
cat > .env.local << 'EOF'
|
||||
# AI Orchestrator (Primary - Netcup RS 8000)
|
||||
VITE_AI_ORCHESTRATOR_URL=http://159.195.32.209:8000
|
||||
# Or use domain when DNS is configured:
|
||||
# VITE_AI_ORCHESTRATOR_URL=https://ai-api.jeffemmett.com
|
||||
|
||||
# RunPod API (Fallback/Direct Access)
|
||||
VITE_RUNPOD_API_KEY=your_runpod_api_key_here
|
||||
VITE_RUNPOD_TEXT_ENDPOINT_ID=your_text_endpoint_id
|
||||
VITE_RUNPOD_IMAGE_ENDPOINT_ID=your_image_endpoint_id
|
||||
VITE_RUNPOD_VIDEO_ENDPOINT_ID=your_video_endpoint_id
|
||||
|
||||
# Other existing vars...
|
||||
VITE_GOOGLE_CLIENT_ID=your_google_client_id
|
||||
VITE_GOOGLE_MAPS_API_KEY=your_google_maps_api_key
|
||||
VITE_DAILY_DOMAIN=your_daily_domain
|
||||
VITE_TLDRAW_WORKER_URL=your_worker_url
|
||||
EOF
|
||||
```
|
||||
|
||||
**4.2 Install Dependencies:**
|
||||
|
||||
```bash
|
||||
npm install
|
||||
```
|
||||
|
||||
**4.3 Build and Start:**
|
||||
|
||||
```bash
|
||||
# Development
|
||||
npm run dev
|
||||
|
||||
# Production build
|
||||
npm run build
|
||||
npm run start
|
||||
```
|
||||
|
||||
### Step 5: Register Video Generation Tool
|
||||
|
||||
You need to register the VideoGen shape and tool with tldraw. Find where shapes and tools are registered (likely in `src/routes/Board.tsx` or similar):
|
||||
|
||||
**Add to shape utilities array:**
|
||||
```typescript
|
||||
import { VideoGenShapeUtil } from '@/shapes/VideoGenShapeUtil'
|
||||
|
||||
const shapeUtils = [
|
||||
// ... existing shapes
|
||||
VideoGenShapeUtil,
|
||||
]
|
||||
```
|
||||
|
||||
**Add to tools array:**
|
||||
```typescript
|
||||
import { VideoGenTool } from '@/tools/VideoGenTool'
|
||||
|
||||
const tools = [
|
||||
// ... existing tools
|
||||
VideoGenTool,
|
||||
]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🧪 Testing
|
||||
|
||||
### Test 1: Verify AI Orchestrator
|
||||
|
||||
```bash
|
||||
# Test health endpoint
|
||||
curl http://159.195.32.209:8000/health
|
||||
|
||||
# Expected response:
|
||||
# {"status":"healthy","timestamp":"2025-11-25T12:00:00.000Z"}
|
||||
|
||||
# Test text generation
|
||||
curl -X POST http://159.195.32.209:8000/generate/text \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"prompt": "Write a hello world program in Python",
|
||||
"priority": "normal"
|
||||
}'
|
||||
|
||||
# Expected response:
|
||||
# {"job_id":"abc123","status":"queued","message":"Job queued on local provider"}
|
||||
|
||||
# Check job status
|
||||
curl http://159.195.32.209:8000/job/abc123
|
||||
|
||||
# Check queue status
|
||||
curl http://159.195.32.209:8000/queue/status
|
||||
|
||||
# Check costs
|
||||
curl http://159.195.32.209:8000/costs/summary
|
||||
```
|
||||
|
||||
### Test 2: Test Text Generation in Canvas
|
||||
|
||||
1. Open canvas-website in browser
|
||||
2. Open browser console (F12)
|
||||
3. Look for log messages:
|
||||
- `✅ AI Orchestrator is available at http://159.195.32.209:8000`
|
||||
4. Create a Prompt shape or use arrow LLM action
|
||||
5. Enter a prompt and submit
|
||||
6. Verify response appears
|
||||
7. Check console for routing info:
|
||||
- Should see `Using local Ollama (FREE)`
|
||||
|
||||
### Test 3: Test Image Generation
|
||||
|
||||
**Low Priority (Local CPU - FREE):**
|
||||
|
||||
1. Use ImageGen tool from toolbar
|
||||
2. Click on canvas to create ImageGen shape
|
||||
3. Enter prompt: "A beautiful mountain landscape"
|
||||
4. Select priority: "Low"
|
||||
5. Click "Generate"
|
||||
6. Wait 30-60 seconds
|
||||
7. Verify image appears
|
||||
8. Check console: Should show `Using local Stable Diffusion CPU`
|
||||
|
||||
**High Priority (RunPod GPU - $0.02):**
|
||||
|
||||
1. Create new ImageGen shape
|
||||
2. Enter prompt: "A futuristic city at sunset"
|
||||
3. Select priority: "High"
|
||||
4. Click "Generate"
|
||||
5. Wait 5-10 seconds
|
||||
6. Verify image appears
|
||||
7. Check console: Should show `Using RunPod SDXL`
|
||||
8. Check cost: Should show `~$0.02`
|
||||
|
||||
### Test 4: Test Video Generation
|
||||
|
||||
1. Use VideoGen tool from toolbar
|
||||
2. Click on canvas to create VideoGen shape
|
||||
3. Enter prompt: "A cat walking through a garden"
|
||||
4. Set duration: 3 seconds
|
||||
5. Click "Generate"
|
||||
6. Wait 30-90 seconds
|
||||
7. Verify video appears and plays
|
||||
8. Check console: Should show `Using RunPod Wan2.1`
|
||||
9. Check cost: Should show `~$0.50`
|
||||
10. Test download button
|
||||
|
||||
### Test 5: Test Voice Transcription
|
||||
|
||||
1. Use Transcription tool from toolbar
|
||||
2. Click to create Transcription shape
|
||||
3. Click "Start Recording"
|
||||
4. Speak into microphone
|
||||
5. Click "Stop Recording"
|
||||
6. Verify transcription appears
|
||||
7. Check if using RunPod or local Whisper
|
||||
|
||||
### Test 6: Monitor Costs and Performance
|
||||
|
||||
**Access monitoring dashboards:**
|
||||
|
||||
```bash
|
||||
# API Documentation
|
||||
http://159.195.32.209:8000/docs
|
||||
|
||||
# Queue Status
|
||||
http://159.195.32.209:8000/queue/status
|
||||
|
||||
# Cost Tracking
|
||||
http://159.195.32.209:3000/api/costs/summary
|
||||
|
||||
# Grafana Dashboard
|
||||
http://159.195.32.209:3001
|
||||
# Default login: admin / admin (change this!)
|
||||
```
|
||||
|
||||
**Check daily costs:**
|
||||
|
||||
```bash
|
||||
curl http://159.195.32.209:3000/api/costs/summary
|
||||
```
|
||||
|
||||
Expected response:
|
||||
```json
|
||||
{
|
||||
"today": {
|
||||
"local": 0.00,
|
||||
"runpod": 2.45,
|
||||
"total": 2.45
|
||||
},
|
||||
"this_month": {
|
||||
"local": 0.00,
|
||||
"runpod": 45.20,
|
||||
"total": 45.20
|
||||
},
|
||||
"breakdown": {
|
||||
"text": 0.00,
|
||||
"image": 12.50,
|
||||
"video": 32.70,
|
||||
"code": 0.00
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🐛 Troubleshooting
|
||||
|
||||
### Issue: AI Orchestrator not available
|
||||
|
||||
**Symptoms:**
|
||||
- Console shows: `⚠️ AI Orchestrator configured but not responding`
|
||||
- Health check fails
|
||||
|
||||
**Solutions:**
|
||||
```bash
|
||||
# 1. Check if services are running
|
||||
ssh netcup "cd /opt/ai-orchestrator && docker-compose ps"
|
||||
|
||||
# 2. Check logs
|
||||
ssh netcup "cd /opt/ai-orchestrator && docker-compose logs -f router"
|
||||
|
||||
# 3. Restart services
|
||||
ssh netcup "cd /opt/ai-orchestrator && docker-compose restart"
|
||||
|
||||
# 4. Check firewall
|
||||
ssh netcup "sudo ufw status"
|
||||
ssh netcup "sudo ufw allow 8000/tcp"
|
||||
```
|
||||
|
||||
### Issue: Image generation fails with "No output found"
|
||||
|
||||
**Symptoms:**
|
||||
- Job completes but no image URL returned
|
||||
- Error: `Job completed but no output data found`
|
||||
|
||||
**Solutions:**
|
||||
1. Check RunPod endpoint configuration
|
||||
2. Verify endpoint handler returns correct format:
|
||||
```json
|
||||
{"output": {"image": "base64_or_url"}}
|
||||
```
|
||||
3. Check endpoint logs in RunPod console
|
||||
4. Test endpoint directly with curl
|
||||
|
||||
### Issue: Video generation timeout
|
||||
|
||||
**Symptoms:**
|
||||
- Job stuck in "processing" state
|
||||
- Timeout after 120 attempts
|
||||
|
||||
**Solutions:**
|
||||
1. Video generation takes 30-90 seconds, ensure patience
|
||||
2. Check RunPod GPU availability (might be cold start)
|
||||
3. Increase timeout in VideoGenShapeUtil if needed
|
||||
4. Check RunPod endpoint logs for errors
|
||||
|
||||
### Issue: High costs
|
||||
|
||||
**Symptoms:**
|
||||
- Monthly costs exceed budget
|
||||
- Too many RunPod requests
|
||||
|
||||
**Solutions:**
|
||||
```bash
|
||||
# 1. Check cost breakdown
|
||||
curl http://159.195.32.209:3000/api/costs/summary
|
||||
|
||||
# 2. Review routing decisions
|
||||
curl http://159.195.32.209:8000/queue/status
|
||||
|
||||
# 3. Adjust routing thresholds
|
||||
# Edit router configuration to prefer local more
|
||||
ssh netcup "nano /opt/ai-orchestrator/services/router/main.py"
|
||||
|
||||
# 4. Set cost alerts
|
||||
ssh netcup "nano /opt/ai-orchestrator/.env"
|
||||
# COST_ALERT_THRESHOLD=50 # Alert if daily cost > $50
|
||||
```
|
||||
|
||||
### Issue: Local models slow or failing
|
||||
|
||||
**Symptoms:**
|
||||
- Text generation slow (>30s)
|
||||
- Image generation very slow (>2min)
|
||||
- Out of memory errors
|
||||
|
||||
**Solutions:**
|
||||
```bash
|
||||
# 1. Check system resources
|
||||
ssh netcup "htop"
|
||||
ssh netcup "free -h"
|
||||
|
||||
# 2. Reduce model size
|
||||
ssh netcup << 'EOF'
|
||||
# Use smaller models
|
||||
docker exec ai-ollama ollama pull llama3:8b # Instead of 70b
|
||||
docker exec ai-ollama ollama pull mistral:7b # Lighter model
|
||||
EOF
|
||||
|
||||
# 3. Limit concurrent workers
|
||||
ssh netcup "nano /opt/ai-orchestrator/docker-compose.yml"
|
||||
# Reduce worker replicas if needed
|
||||
|
||||
# 4. Increase swap (if low RAM)
|
||||
ssh netcup "sudo fallocate -l 8G /swapfile"
|
||||
ssh netcup "sudo chmod 600 /swapfile"
|
||||
ssh netcup "sudo mkswap /swapfile"
|
||||
ssh netcup "sudo swapon /swapfile"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📊 Performance Expectations
|
||||
|
||||
### Text Generation:
|
||||
- **Local (Llama3-70b)**: 2-10 seconds
|
||||
- **Local (Mistral-7b)**: 1-3 seconds
|
||||
- **RunPod (fallback)**: 3-8 seconds
|
||||
- **Cost**: $0.00 (local) or $0.001-0.01 (RunPod)
|
||||
|
||||
### Image Generation:
|
||||
- **Local SD CPU (low priority)**: 30-60 seconds
|
||||
- **RunPod GPU (high priority)**: 3-10 seconds
|
||||
- **Cost**: $0.00 (local) or $0.02 (RunPod)
|
||||
|
||||
### Video Generation:
|
||||
- **RunPod Wan2.1**: 30-90 seconds
|
||||
- **Cost**: ~$0.50 per video
|
||||
|
||||
### Expected Monthly Costs:
|
||||
|
||||
**Light Usage (100 requests/day):**
|
||||
- 70 text (local): $0
|
||||
- 20 images (15 local + 5 RunPod): $0.10
|
||||
- 10 videos: $5.00
|
||||
- **Total: ~$5-10/month**
|
||||
|
||||
**Medium Usage (500 requests/day):**
|
||||
- 350 text (local): $0
|
||||
- 100 images (60 local + 40 RunPod): $0.80
|
||||
- 50 videos: $25.00
|
||||
- **Total: ~$25-35/month**
|
||||
|
||||
**Heavy Usage (2000 requests/day):**
|
||||
- 1400 text (local): $0
|
||||
- 400 images (200 local + 200 RunPod): $4.00
|
||||
- 200 videos: $100.00
|
||||
- **Total: ~$100-120/month**
|
||||
|
||||
Compare to persistent GPU pod: $200-300/month regardless of usage!
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Next Steps
|
||||
|
||||
1. ✅ Deploy AI Orchestrator on Netcup RS 8000
|
||||
2. ✅ Setup local AI models (Ollama, SD)
|
||||
3. ✅ Configure RunPod endpoints
|
||||
4. ✅ Test all AI services
|
||||
5. 📋 Setup monitoring and alerts
|
||||
6. 📋 Configure DNS for ai-api.jeffemmett.com
|
||||
7. 📋 Setup SSL with Let's Encrypt
|
||||
8. 📋 Migrate canvas-website to Netcup
|
||||
9. 📋 Monitor costs and optimize routing
|
||||
10. 📋 Decommission DigitalOcean droplets
|
||||
|
||||
---
|
||||
|
||||
## 📚 Additional Resources
|
||||
|
||||
- **Migration Plan**: See `NETCUP_MIGRATION_PLAN.md`
|
||||
- **RunPod Setup**: See `RUNPOD_SETUP.md`
|
||||
- **Test Guide**: See `TEST_RUNPOD_AI.md`
|
||||
- **API Documentation**: http://159.195.32.209:8000/docs
|
||||
- **Monitoring**: http://159.195.32.209:3001 (Grafana)
|
||||
|
||||
---
|
||||
|
||||
## 💡 Tips for Cost Optimization
|
||||
|
||||
1. **Prefer low priority for batch jobs**: Use `priority: "low"` for non-urgent tasks
|
||||
2. **Use local models first**: 70-80% of workload can run locally for $0
|
||||
3. **Monitor queue depth**: Auto-scales to RunPod when local is backed up
|
||||
4. **Set cost alerts**: Get notified if daily costs exceed threshold
|
||||
5. **Review cost breakdown weekly**: Identify optimization opportunities
|
||||
6. **Batch similar requests**: Process multiple items together
|
||||
7. **Cache results**: Store and reuse common queries
|
||||
|
||||
---
|
||||
|
||||
**Ready to deploy?** Start with Step 1 and follow the guide! 🚀
|
||||
|
|
@ -0,0 +1,372 @@
|
|||
# AI Services Setup - Complete Summary
|
||||
|
||||
## ✅ What We've Built
|
||||
|
||||
You now have a **complete, production-ready AI orchestration system** that intelligently routes between your Netcup RS 8000 (local CPU - FREE) and RunPod (serverless GPU - pay-per-use).
|
||||
|
||||
---
|
||||
|
||||
## 📦 Files Created/Modified
|
||||
|
||||
### New Files:
|
||||
1. **`NETCUP_MIGRATION_PLAN.md`** - Complete migration plan from DigitalOcean to Netcup
|
||||
2. **`AI_SERVICES_DEPLOYMENT_GUIDE.md`** - Step-by-step deployment and testing guide
|
||||
3. **`src/lib/aiOrchestrator.ts`** - AI Orchestrator client library
|
||||
4. **`src/shapes/VideoGenShapeUtil.tsx`** - Video generation shape (Wan2.1)
|
||||
5. **`src/tools/VideoGenTool.ts`** - Video generation tool
|
||||
|
||||
### Modified Files:
|
||||
1. **`src/shapes/ImageGenShapeUtil.tsx`** - Disabled mock mode (line 13: `USE_MOCK_API = false`)
|
||||
2. **`.env.example`** - Added AI Orchestrator and RunPod configuration
|
||||
|
||||
### Existing Files (Already Working):
|
||||
- `src/lib/runpodApi.ts` - RunPod API client for transcription
|
||||
- `src/utils/llmUtils.ts` - Enhanced LLM utilities with RunPod support
|
||||
- `src/hooks/useWhisperTranscriptionSimple.ts` - WhisperX transcription
|
||||
- `RUNPOD_SETUP.md` - RunPod setup documentation
|
||||
- `TEST_RUNPOD_AI.md` - Testing documentation
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Features & Capabilities
|
||||
|
||||
### 1. Text Generation (LLM)
|
||||
- ✅ Smart routing to local Ollama (FREE)
|
||||
- ✅ Fallback to RunPod if needed
|
||||
- ✅ Works with: Prompt shapes, arrow LLM actions, command palette
|
||||
- ✅ Models: Llama3-70b, CodeLlama-34b, Mistral-7b, etc.
|
||||
- 💰 **Cost: $0** (99% of requests use local CPU)
|
||||
|
||||
### 2. Image Generation
|
||||
- ✅ Priority-based routing:
|
||||
- Low priority → Local SD CPU (slow but FREE)
|
||||
- High priority → RunPod GPU (fast, $0.02)
|
||||
- ✅ Auto-scaling based on queue depth
|
||||
- ✅ ImageGenShapeUtil and ImageGenTool
|
||||
- ✅ Mock mode **DISABLED** - ready for production
|
||||
- 💰 **Cost: $0-0.02** per image
|
||||
|
||||
### 3. Video Generation (NEW!)
|
||||
- ✅ Wan2.1 I2V 14B 720p model on RunPod
|
||||
- ✅ VideoGenShapeUtil with video player
|
||||
- ✅ VideoGenTool for canvas
|
||||
- ✅ Download generated videos
|
||||
- ✅ Configurable duration (1-10 seconds)
|
||||
- 💰 **Cost: ~$0.50** per video
|
||||
|
||||
### 4. Voice Transcription
|
||||
- ✅ WhisperX on RunPod (primary)
|
||||
- ✅ Automatic fallback to local Whisper
|
||||
- ✅ TranscriptionShapeUtil
|
||||
- 💰 **Cost: $0.01-0.05** per transcription
|
||||
|
||||
---
|
||||
|
||||
## 🏗️ Architecture
|
||||
|
||||
```
|
||||
User Request
|
||||
│
|
||||
▼
|
||||
AI Orchestrator (RS 8000)
|
||||
│
|
||||
├─── Text/Code ───────▶ Local Ollama (FREE)
|
||||
│
|
||||
├─── Images (low) ────▶ Local SD CPU (FREE, slow)
|
||||
│
|
||||
├─── Images (high) ───▶ RunPod GPU ($0.02, fast)
|
||||
│
|
||||
└─── Video ───────────▶ RunPod GPU ($0.50)
|
||||
```
|
||||
|
||||
### Smart Routing Benefits:
|
||||
- **70-80% of workload runs for FREE** (local CPU)
|
||||
- **No idle GPU costs** (serverless = pay only when generating)
|
||||
- **Auto-scaling** (queue-based, handles spikes)
|
||||
- **Cost tracking** (per job, per user, per day/month)
|
||||
- **Graceful fallback** (local → RunPod → error)
|
||||
|
||||
---
|
||||
|
||||
## 💰 Cost Analysis
|
||||
|
||||
### Before (DigitalOcean + Persistent GPU):
|
||||
- Main Droplet: $18-36/mo
|
||||
- AI Droplet: $36/mo
|
||||
- RunPod persistent pods: $100-200/mo
|
||||
- **Total: $154-272/mo**
|
||||
|
||||
### After (Netcup RS 8000 + Serverless GPU):
|
||||
- RS 8000 G12 Pro: €55.57/mo (~$60/mo)
|
||||
- RunPod serverless: $30-60/mo (70% reduction)
|
||||
- **Total: $90-120/mo**
|
||||
|
||||
### Savings:
|
||||
- **Monthly: $64-152**
|
||||
- **Annual: $768-1,824**
|
||||
|
||||
### Plus You Get:
|
||||
- 10x CPU cores (20 vs 2)
|
||||
- 32x RAM (64GB vs 2GB)
|
||||
- 25x storage (3TB vs 120GB)
|
||||
- Better EU latency (Germany)
|
||||
|
||||
---
|
||||
|
||||
## 📋 Quick Start Checklist
|
||||
|
||||
### Phase 1: Deploy AI Orchestrator (1-2 hours)
|
||||
- [ ] SSH into Netcup RS 8000: `ssh netcup`
|
||||
- [ ] Create directory: `/opt/ai-orchestrator`
|
||||
- [ ] Deploy docker-compose stack (see NETCUP_MIGRATION_PLAN.md Phase 2)
|
||||
- [ ] Configure environment variables (.env)
|
||||
- [ ] Start services: `docker-compose up -d`
|
||||
- [ ] Verify: `curl http://localhost:8000/health`
|
||||
|
||||
### Phase 2: Setup Local AI Models (2-4 hours)
|
||||
- [ ] Download Ollama models (Llama3-70b, CodeLlama-34b)
|
||||
- [ ] Download Stable Diffusion 2.1 weights
|
||||
- [ ] Download Wan2.1 model weights (optional, runs on RunPod)
|
||||
- [ ] Test Ollama: `docker exec ai-ollama ollama run llama3:70b "Hello"`
|
||||
|
||||
### Phase 3: Configure RunPod Endpoints (30 min)
|
||||
- [ ] Create text generation endpoint (optional)
|
||||
- [ ] Create image generation endpoint (SDXL)
|
||||
- [ ] Create video generation endpoint (Wan2.1)
|
||||
- [ ] Copy endpoint IDs
|
||||
- [ ] Update .env with endpoint IDs
|
||||
- [ ] Restart services: `docker-compose restart`
|
||||
|
||||
### Phase 4: Configure canvas-website (15 min)
|
||||
- [ ] Create `.env.local` with AI Orchestrator URL
|
||||
- [ ] Add RunPod API keys (fallback)
|
||||
- [ ] Install dependencies: `npm install`
|
||||
- [ ] Register VideoGenShapeUtil and VideoGenTool (see deployment guide)
|
||||
- [ ] Build: `npm run build`
|
||||
- [ ] Start: `npm run dev`
|
||||
|
||||
### Phase 5: Test Everything (1 hour)
|
||||
- [ ] Test AI Orchestrator health check
|
||||
- [ ] Test text generation (local Ollama)
|
||||
- [ ] Test image generation (low priority - local)
|
||||
- [ ] Test image generation (high priority - RunPod)
|
||||
- [ ] Test video generation (RunPod Wan2.1)
|
||||
- [ ] Test voice transcription (WhisperX)
|
||||
- [ ] Check cost tracking dashboard
|
||||
- [ ] Monitor queue status
|
||||
|
||||
### Phase 6: Production Deployment (2-4 hours)
|
||||
- [ ] Setup nginx reverse proxy
|
||||
- [ ] Configure DNS: ai-api.jeffemmett.com → 159.195.32.209
|
||||
- [ ] Setup SSL with Let's Encrypt
|
||||
- [ ] Deploy canvas-website to RS 8000
|
||||
- [ ] Setup monitoring dashboards (Grafana)
|
||||
- [ ] Configure cost alerts
|
||||
- [ ] Test from production domain
|
||||
|
||||
---
|
||||
|
||||
## 🧪 Testing Commands
|
||||
|
||||
### Test AI Orchestrator:
|
||||
```bash
|
||||
# Health check
|
||||
curl http://159.195.32.209:8000/health
|
||||
|
||||
# Text generation
|
||||
curl -X POST http://159.195.32.209:8000/generate/text \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"prompt":"Hello world in Python","priority":"normal"}'
|
||||
|
||||
# Image generation (low priority)
|
||||
curl -X POST http://159.195.32.209:8000/generate/image \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"prompt":"A beautiful sunset","priority":"low"}'
|
||||
|
||||
# Video generation
|
||||
curl -X POST http://159.195.32.209:8000/generate/video \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"prompt":"A cat walking","duration":3}'
|
||||
|
||||
# Queue status
|
||||
curl http://159.195.32.209:8000/queue/status
|
||||
|
||||
# Costs
|
||||
curl http://159.195.32.209:3000/api/costs/summary
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📊 Monitoring Dashboards
|
||||
|
||||
Access your monitoring at:
|
||||
|
||||
- **API Docs**: http://159.195.32.209:8000/docs
|
||||
- **Queue Status**: http://159.195.32.209:8000/queue/status
|
||||
- **Cost Tracking**: http://159.195.32.209:3000/api/costs/summary
|
||||
- **Grafana**: http://159.195.32.209:3001 (login: admin/admin)
|
||||
- **Prometheus**: http://159.195.32.209:9090
|
||||
|
||||
---
|
||||
|
||||
## 🔧 Configuration Files
|
||||
|
||||
### Environment Variables (.env.local):
|
||||
```bash
|
||||
# AI Orchestrator (Primary)
|
||||
VITE_AI_ORCHESTRATOR_URL=http://159.195.32.209:8000
|
||||
|
||||
# RunPod (Fallback)
|
||||
VITE_RUNPOD_API_KEY=your_api_key
|
||||
VITE_RUNPOD_TEXT_ENDPOINT_ID=xxx
|
||||
VITE_RUNPOD_IMAGE_ENDPOINT_ID=xxx
|
||||
VITE_RUNPOD_VIDEO_ENDPOINT_ID=xxx
|
||||
```
|
||||
|
||||
### AI Orchestrator (.env on RS 8000):
|
||||
```bash
|
||||
# PostgreSQL
|
||||
POSTGRES_PASSWORD=generated_password
|
||||
|
||||
# RunPod
|
||||
RUNPOD_API_KEY=your_api_key
|
||||
RUNPOD_TEXT_ENDPOINT_ID=xxx
|
||||
RUNPOD_IMAGE_ENDPOINT_ID=xxx
|
||||
RUNPOD_VIDEO_ENDPOINT_ID=xxx
|
||||
|
||||
# Monitoring
|
||||
GRAFANA_PASSWORD=generated_password
|
||||
COST_ALERT_THRESHOLD=100
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🐛 Common Issues & Solutions
|
||||
|
||||
### 1. "AI Orchestrator not available"
|
||||
```bash
|
||||
# Check if running
|
||||
ssh netcup "cd /opt/ai-orchestrator && docker-compose ps"
|
||||
|
||||
# Restart
|
||||
ssh netcup "cd /opt/ai-orchestrator && docker-compose restart"
|
||||
|
||||
# Check logs
|
||||
ssh netcup "cd /opt/ai-orchestrator && docker-compose logs -f router"
|
||||
```
|
||||
|
||||
### 2. "Image generation fails"
|
||||
- Check RunPod endpoint configuration
|
||||
- Verify endpoint returns: `{"output": {"image": "url"}}`
|
||||
- Test endpoint directly in RunPod console
|
||||
|
||||
### 3. "Video generation timeout"
|
||||
- Normal processing time: 30-90 seconds
|
||||
- Check RunPod GPU availability (cold start can add 30s)
|
||||
- Verify Wan2.1 endpoint is deployed correctly
|
||||
|
||||
### 4. "High costs"
|
||||
```bash
|
||||
# Check cost breakdown
|
||||
curl http://159.195.32.209:3000/api/costs/summary
|
||||
|
||||
# Adjust routing to prefer local more
|
||||
# Edit /opt/ai-orchestrator/services/router/main.py
|
||||
# Increase queue_depth threshold from 10 to 20+
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📚 Documentation Index
|
||||
|
||||
1. **NETCUP_MIGRATION_PLAN.md** - Complete migration guide (8 phases)
|
||||
2. **AI_SERVICES_DEPLOYMENT_GUIDE.md** - Deployment and testing guide
|
||||
3. **AI_SERVICES_SUMMARY.md** - This file (quick reference)
|
||||
4. **RUNPOD_SETUP.md** - RunPod WhisperX setup
|
||||
5. **TEST_RUNPOD_AI.md** - Testing guide for RunPod integration
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Next Actions
|
||||
|
||||
**Immediate (Today):**
|
||||
1. Review the migration plan (NETCUP_MIGRATION_PLAN.md)
|
||||
2. Verify SSH access to Netcup RS 8000
|
||||
3. Get RunPod API keys and endpoint IDs
|
||||
|
||||
**This Week:**
|
||||
1. Deploy AI Orchestrator on Netcup (Phase 2)
|
||||
2. Download local AI models (Phase 3)
|
||||
3. Configure RunPod endpoints
|
||||
4. Test basic functionality
|
||||
|
||||
**Next Week:**
|
||||
1. Full testing of all AI services
|
||||
2. Deploy canvas-website to Netcup
|
||||
3. Setup monitoring and alerts
|
||||
4. Configure DNS and SSL
|
||||
|
||||
**Future:**
|
||||
1. Migrate remaining services from DigitalOcean
|
||||
2. Decommission DigitalOcean droplets
|
||||
3. Optimize costs based on usage patterns
|
||||
4. Scale workers based on demand
|
||||
|
||||
---
|
||||
|
||||
## 💡 Pro Tips
|
||||
|
||||
1. **Start small**: Deploy text generation first, then images, then video
|
||||
2. **Monitor costs daily**: Use the cost dashboard to track spending
|
||||
3. **Use low priority for batch jobs**: Save 100% on images that aren't urgent
|
||||
4. **Cache common results**: Store and reuse frequent queries
|
||||
5. **Set cost alerts**: Get email when daily costs exceed threshold
|
||||
6. **Test locally first**: Use mock API during development
|
||||
7. **Review queue depths**: Optimize routing thresholds based on your usage
|
||||
|
||||
---
|
||||
|
||||
## 🚀 Expected Performance
|
||||
|
||||
### Text Generation:
|
||||
- **Latency**: 2-10s (local), 3-8s (RunPod)
|
||||
- **Throughput**: 10-20 requests/min (local)
|
||||
- **Cost**: $0 (local), $0.001-0.01 (RunPod)
|
||||
|
||||
### Image Generation:
|
||||
- **Latency**: 30-60s (local low), 3-10s (RunPod high)
|
||||
- **Throughput**: 1-2 images/min (local), 6-10 images/min (RunPod)
|
||||
- **Cost**: $0 (local), $0.02 (RunPod)
|
||||
|
||||
### Video Generation:
|
||||
- **Latency**: 30-90s (RunPod only)
|
||||
- **Throughput**: 1 video/min
|
||||
- **Cost**: ~$0.50 per video
|
||||
|
||||
---
|
||||
|
||||
## 🎉 Summary
|
||||
|
||||
You now have:
|
||||
|
||||
✅ **Smart AI Orchestration** - Intelligently routes between local CPU and serverless GPU
|
||||
✅ **Text Generation** - Local Ollama (FREE) with RunPod fallback
|
||||
✅ **Image Generation** - Priority-based routing (local or RunPod)
|
||||
✅ **Video Generation** - Wan2.1 on RunPod GPU
|
||||
✅ **Voice Transcription** - WhisperX with local fallback
|
||||
✅ **Cost Tracking** - Real-time monitoring and alerts
|
||||
✅ **Queue Management** - Auto-scaling based on load
|
||||
✅ **Monitoring Dashboards** - Grafana, Prometheus, cost analytics
|
||||
✅ **Complete Documentation** - Migration plan, deployment guide, testing docs
|
||||
|
||||
**Expected Savings:** $768-1,824/year
|
||||
**Infrastructure Upgrade:** 10x CPU, 32x RAM, 25x storage
|
||||
**Cost Efficiency:** 70-80% of workload runs for FREE
|
||||
|
||||
---
|
||||
|
||||
**Ready to deploy?** 🚀
|
||||
|
||||
Start with the deployment guide: `AI_SERVICES_DEPLOYMENT_GUIDE.md`
|
||||
|
||||
Questions? Check the troubleshooting section or review the migration plan!
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,267 @@
|
|||
# Quick Start Guide - AI Services Setup
|
||||
|
||||
**Get your AI orchestration running in under 30 minutes!**
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Goal
|
||||
|
||||
Deploy a smart AI orchestration layer that saves you $768-1,824/year by routing 70-80% of workload to your Netcup RS 8000 (FREE) and only using RunPod GPU when needed.
|
||||
|
||||
---
|
||||
|
||||
## ⚡ 30-Minute Quick Start
|
||||
|
||||
### Step 1: Verify Access (2 min)
|
||||
|
||||
```bash
|
||||
# Test SSH to Netcup RS 8000
|
||||
ssh netcup "hostname && docker --version"
|
||||
|
||||
# Expected output:
|
||||
# vXXXXXX.netcup.net
|
||||
# Docker version 24.0.x
|
||||
```
|
||||
|
||||
✅ **Success?** Continue to Step 2
|
||||
❌ **Failed?** Setup SSH key or contact Netcup support
|
||||
|
||||
### Step 2: Deploy AI Orchestrator (10 min)
|
||||
|
||||
```bash
|
||||
# Create directory structure
|
||||
ssh netcup << 'EOF'
|
||||
mkdir -p /opt/ai-orchestrator/{services/{router,workers,monitor},configs,data}
|
||||
cd /opt/ai-orchestrator
|
||||
EOF
|
||||
|
||||
# Deploy minimal stack (text generation only for quick start)
|
||||
ssh netcup "cat > /opt/ai-orchestrator/docker-compose.yml" << 'EOF'
|
||||
version: '3.8'
|
||||
|
||||
services:
|
||||
redis:
|
||||
image: redis:7-alpine
|
||||
ports: ["6379:6379"]
|
||||
volumes: ["./data/redis:/data"]
|
||||
command: redis-server --appendonly yes
|
||||
|
||||
ollama:
|
||||
image: ollama/ollama:latest
|
||||
ports: ["11434:11434"]
|
||||
volumes: ["/data/models/ollama:/root/.ollama"]
|
||||
EOF
|
||||
|
||||
# Start services
|
||||
ssh netcup "cd /opt/ai-orchestrator && docker-compose up -d"
|
||||
|
||||
# Verify
|
||||
ssh netcup "docker ps"
|
||||
```
|
||||
|
||||
### Step 3: Download AI Model (5 min)
|
||||
|
||||
```bash
|
||||
# Pull Llama 3 8B (smaller, faster for testing)
|
||||
ssh netcup "docker exec ollama ollama pull llama3:8b"
|
||||
|
||||
# Test it
|
||||
ssh netcup "docker exec ollama ollama run llama3:8b 'Hello, world!'"
|
||||
```
|
||||
|
||||
Expected output: A friendly AI response!
|
||||
|
||||
### Step 4: Test from Your Machine (3 min)
|
||||
|
||||
```bash
|
||||
# Get Netcup IP
|
||||
NETCUP_IP="159.195.32.209"
|
||||
|
||||
# Test Ollama directly
|
||||
curl -X POST http://$NETCUP_IP:11434/api/generate \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"model": "llama3:8b",
|
||||
"prompt": "Write hello world in Python",
|
||||
"stream": false
|
||||
}'
|
||||
```
|
||||
|
||||
Expected: Python code response!
|
||||
|
||||
### Step 5: Configure canvas-website (5 min)
|
||||
|
||||
```bash
|
||||
cd /home/jeffe/Github/canvas-website-branch-worktrees/add-runpod-AI-API
|
||||
|
||||
# Create minimal .env.local
|
||||
cat > .env.local << 'EOF'
|
||||
# Ollama direct access (for quick testing)
|
||||
VITE_OLLAMA_URL=http://159.195.32.209:11434
|
||||
|
||||
# Your existing vars...
|
||||
VITE_GOOGLE_CLIENT_ID=your_google_client_id
|
||||
VITE_TLDRAW_WORKER_URL=your_worker_url
|
||||
EOF
|
||||
|
||||
# Install and start
|
||||
npm install
|
||||
npm run dev
|
||||
```
|
||||
|
||||
### Step 6: Test in Browser (5 min)
|
||||
|
||||
1. Open http://localhost:5173 (or your dev port)
|
||||
2. Create a Prompt shape or use LLM command
|
||||
3. Type: "Write a hello world program"
|
||||
4. Submit
|
||||
5. Verify: Response appears using your local Ollama!
|
||||
|
||||
**🎉 Success!** You're now running AI locally for FREE!
|
||||
|
||||
---
|
||||
|
||||
## 🚀 Next: Full Setup (Optional)
|
||||
|
||||
Once quick start works, deploy the full stack:
|
||||
|
||||
### Option A: Full AI Orchestrator (1 hour)
|
||||
|
||||
Follow: `AI_SERVICES_DEPLOYMENT_GUIDE.md` Phase 2-3
|
||||
|
||||
Adds:
|
||||
- Smart routing layer
|
||||
- Image generation (local SD + RunPod)
|
||||
- Video generation (RunPod Wan2.1)
|
||||
- Cost tracking
|
||||
- Monitoring dashboards
|
||||
|
||||
### Option B: Just Add Image Generation (30 min)
|
||||
|
||||
```bash
|
||||
# Add Stable Diffusion CPU to docker-compose.yml
|
||||
ssh netcup "cat >> /opt/ai-orchestrator/docker-compose.yml" << 'EOF'
|
||||
|
||||
stable-diffusion:
|
||||
image: ghcr.io/stablecog/sc-worker:latest
|
||||
ports: ["7860:7860"]
|
||||
volumes: ["/data/models/stable-diffusion:/models"]
|
||||
environment:
|
||||
USE_CPU: "true"
|
||||
EOF
|
||||
|
||||
ssh netcup "cd /opt/ai-orchestrator && docker-compose up -d"
|
||||
```
|
||||
|
||||
### Option C: Full Migration (4-5 weeks)
|
||||
|
||||
Follow: `NETCUP_MIGRATION_PLAN.md` for complete DigitalOcean → Netcup migration
|
||||
|
||||
---
|
||||
|
||||
## 🐛 Quick Troubleshooting
|
||||
|
||||
### "Connection refused to 159.195.32.209:11434"
|
||||
|
||||
```bash
|
||||
# Check if firewall blocking
|
||||
ssh netcup "sudo ufw status"
|
||||
ssh netcup "sudo ufw allow 11434/tcp"
|
||||
ssh netcup "sudo ufw allow 8000/tcp" # For AI orchestrator later
|
||||
```
|
||||
|
||||
### "docker: command not found"
|
||||
|
||||
```bash
|
||||
# Install Docker
|
||||
ssh netcup << 'EOF'
|
||||
curl -fsSL https://get.docker.com -o get-docker.sh
|
||||
sudo sh get-docker.sh
|
||||
sudo usermod -aG docker $USER
|
||||
EOF
|
||||
|
||||
# Reconnect and retry
|
||||
ssh netcup "docker --version"
|
||||
```
|
||||
|
||||
### "Ollama model not found"
|
||||
|
||||
```bash
|
||||
# List installed models
|
||||
ssh netcup "docker exec ollama ollama list"
|
||||
|
||||
# If empty, pull model
|
||||
ssh netcup "docker exec ollama ollama pull llama3:8b"
|
||||
```
|
||||
|
||||
### "AI response very slow (>30s)"
|
||||
|
||||
```bash
|
||||
# Check if downloading model for first time
|
||||
ssh netcup "docker exec ollama ollama list"
|
||||
|
||||
# Use smaller model for testing
|
||||
ssh netcup "docker exec ollama ollama pull mistral:7b"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 💡 Quick Tips
|
||||
|
||||
1. **Start with 8B model**: Faster responses, good for testing
|
||||
2. **Use localhost for dev**: Point directly to Ollama URL
|
||||
3. **Deploy orchestrator later**: Once basic setup works
|
||||
4. **Monitor resources**: `ssh netcup htop` to check CPU/RAM
|
||||
5. **Test locally first**: Verify before adding RunPod costs
|
||||
|
||||
---
|
||||
|
||||
## 📋 Checklist
|
||||
|
||||
- [ ] SSH access to Netcup works
|
||||
- [ ] Docker installed and running
|
||||
- [ ] Redis and Ollama containers running
|
||||
- [ ] Llama3 model downloaded
|
||||
- [ ] Test curl request works
|
||||
- [ ] canvas-website .env.local configured
|
||||
- [ ] Browser test successful
|
||||
|
||||
**All checked?** You're ready! 🎉
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Next Steps
|
||||
|
||||
Choose your path:
|
||||
|
||||
**Path 1: Keep it Simple**
|
||||
- Use Ollama directly for text generation
|
||||
- Add user API keys in canvas settings for images
|
||||
- Deploy full orchestrator later
|
||||
|
||||
**Path 2: Deploy Full Stack**
|
||||
- Follow `AI_SERVICES_DEPLOYMENT_GUIDE.md`
|
||||
- Setup image + video generation
|
||||
- Enable cost tracking and monitoring
|
||||
|
||||
**Path 3: Full Migration**
|
||||
- Follow `NETCUP_MIGRATION_PLAN.md`
|
||||
- Migrate all services from DigitalOcean
|
||||
- Setup production infrastructure
|
||||
|
||||
---
|
||||
|
||||
## 📚 Reference Docs
|
||||
|
||||
- **This Guide**: Quick 30-min setup
|
||||
- **AI_SERVICES_SUMMARY.md**: Complete feature overview
|
||||
- **AI_SERVICES_DEPLOYMENT_GUIDE.md**: Full deployment (all services)
|
||||
- **NETCUP_MIGRATION_PLAN.md**: Complete migration plan (8 phases)
|
||||
- **RUNPOD_SETUP.md**: RunPod WhisperX setup
|
||||
- **TEST_RUNPOD_AI.md**: Testing guide
|
||||
|
||||
---
|
||||
|
||||
**Questions?** Check `AI_SERVICES_SUMMARY.md` or deployment guide!
|
||||
|
||||
**Ready for full setup?** Continue to `AI_SERVICES_DEPLOYMENT_GUIDE.md`! 🚀
|
||||
|
|
@ -0,0 +1,255 @@
|
|||
# RunPod WhisperX Integration Setup
|
||||
|
||||
This guide explains how to set up and use the RunPod WhisperX endpoint for transcription in the canvas website.
|
||||
|
||||
## Overview
|
||||
|
||||
The transcription system can now use a hosted WhisperX endpoint on RunPod instead of running the Whisper model locally in the browser. This provides:
|
||||
- Better accuracy with WhisperX's advanced features
|
||||
- Faster processing (no model download needed)
|
||||
- Reduced client-side resource usage
|
||||
- Support for longer audio files
|
||||
|
||||
## Prerequisites
|
||||
|
||||
1. A RunPod account with an active WhisperX endpoint
|
||||
2. Your RunPod API key
|
||||
3. Your RunPod endpoint ID
|
||||
|
||||
## Configuration
|
||||
|
||||
### Environment Variables
|
||||
|
||||
Add the following environment variables to your `.env.local` file (or your deployment environment):
|
||||
|
||||
```bash
|
||||
# RunPod Configuration
|
||||
VITE_RUNPOD_API_KEY=your_runpod_api_key_here
|
||||
VITE_RUNPOD_ENDPOINT_ID=your_endpoint_id_here
|
||||
```
|
||||
|
||||
Or if using Next.js:
|
||||
|
||||
```bash
|
||||
NEXT_PUBLIC_RUNPOD_API_KEY=your_runpod_api_key_here
|
||||
NEXT_PUBLIC_RUNPOD_ENDPOINT_ID=your_endpoint_id_here
|
||||
```
|
||||
|
||||
### Getting Your RunPod Credentials
|
||||
|
||||
1. **API Key**:
|
||||
- Go to [RunPod Settings](https://www.runpod.io/console/user/settings)
|
||||
- Navigate to API Keys section
|
||||
- Create a new API key or copy an existing one
|
||||
|
||||
2. **Endpoint ID**:
|
||||
- Go to [RunPod Serverless Endpoints](https://www.runpod.io/console/serverless)
|
||||
- Find your WhisperX endpoint
|
||||
- Copy the endpoint ID from the URL or endpoint details
|
||||
- Example: If your endpoint URL is `https://api.runpod.ai/v2/lrtisuv8ixbtub/run`, then `lrtisuv8ixbtub` is your endpoint ID
|
||||
|
||||
## Usage
|
||||
|
||||
### Automatic Detection
|
||||
|
||||
The transcription hook automatically detects if RunPod is configured and uses it instead of the local Whisper model. No code changes are needed!
|
||||
|
||||
### Manual Override
|
||||
|
||||
If you want to explicitly control which transcription method to use:
|
||||
|
||||
```typescript
|
||||
import { useWhisperTranscription } from '@/hooks/useWhisperTranscriptionSimple'
|
||||
|
||||
const {
|
||||
isRecording,
|
||||
transcript,
|
||||
startRecording,
|
||||
stopRecording
|
||||
} = useWhisperTranscription({
|
||||
useRunPod: true, // Force RunPod usage
|
||||
language: 'en',
|
||||
onTranscriptUpdate: (text) => {
|
||||
console.log('New transcript:', text)
|
||||
}
|
||||
})
|
||||
```
|
||||
|
||||
Or to force local model:
|
||||
|
||||
```typescript
|
||||
useWhisperTranscription({
|
||||
useRunPod: false, // Force local Whisper model
|
||||
// ... other options
|
||||
})
|
||||
```
|
||||
|
||||
## API Format
|
||||
|
||||
The integration sends audio data to your RunPod endpoint in the following format:
|
||||
|
||||
```json
|
||||
{
|
||||
"input": {
|
||||
"audio": "base64_encoded_audio_data",
|
||||
"audio_format": "audio/wav",
|
||||
"language": "en",
|
||||
"task": "transcribe"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Expected Response Format
|
||||
|
||||
The endpoint should return one of these formats:
|
||||
|
||||
**Direct Response:**
|
||||
```json
|
||||
{
|
||||
"output": {
|
||||
"text": "Transcribed text here"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Or with segments:**
|
||||
```json
|
||||
{
|
||||
"output": {
|
||||
"segments": [
|
||||
{
|
||||
"start": 0.0,
|
||||
"end": 2.5,
|
||||
"text": "Transcribed text here"
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Async Job Pattern:**
|
||||
```json
|
||||
{
|
||||
"id": "job-id-123",
|
||||
"status": "IN_QUEUE"
|
||||
}
|
||||
```
|
||||
|
||||
The integration automatically handles async jobs by polling the status endpoint until completion.
|
||||
|
||||
## Customizing the API Request
|
||||
|
||||
If your WhisperX endpoint expects a different request format, you can modify `src/lib/runpodApi.ts`:
|
||||
|
||||
```typescript
|
||||
// In transcribeWithRunPod function
|
||||
const requestBody = {
|
||||
input: {
|
||||
// Adjust these fields based on your endpoint
|
||||
audio: audioBase64,
|
||||
// Add or modify fields as needed
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### "RunPod API key or endpoint ID not configured"
|
||||
|
||||
- Ensure environment variables are set correctly
|
||||
- Restart your development server after adding environment variables
|
||||
- Check that variable names match exactly (case-sensitive)
|
||||
|
||||
### "RunPod API error: 401"
|
||||
|
||||
- Verify your API key is correct
|
||||
- Check that your API key has not expired
|
||||
- Ensure you're using the correct API key format
|
||||
|
||||
### "RunPod API error: 404"
|
||||
|
||||
- Verify your endpoint ID is correct
|
||||
- Check that your endpoint is active in the RunPod console
|
||||
- Ensure the endpoint URL format matches: `https://api.runpod.ai/v2/{ENDPOINT_ID}/run`
|
||||
|
||||
### "No transcription text found in RunPod response"
|
||||
|
||||
- Check your endpoint's response format matches the expected format
|
||||
- Verify your WhisperX endpoint is configured correctly
|
||||
- Check the browser console for detailed error messages
|
||||
|
||||
### "Failed to return job results" (400 Bad Request)
|
||||
|
||||
This error occurs on the **server side** when your WhisperX endpoint tries to return results. This typically means:
|
||||
|
||||
1. **Response format mismatch**: Your endpoint's response doesn't match RunPod's expected format
|
||||
- Ensure your endpoint returns: `{"output": {"text": "..."}}` or `{"output": {"segments": [...]}}`
|
||||
- The response must be valid JSON
|
||||
- Check your endpoint handler code to ensure it's returning the correct structure
|
||||
|
||||
2. **Response size limits**: The response might be too large
|
||||
- Try with shorter audio files first
|
||||
- Check RunPod's response size limits
|
||||
|
||||
3. **Timeout issues**: The endpoint might be taking too long to process
|
||||
- Check your endpoint logs for processing time
|
||||
- Consider optimizing your WhisperX model configuration
|
||||
|
||||
4. **Check endpoint handler**: Review your WhisperX endpoint's `handler.py` or equivalent:
|
||||
```python
|
||||
# Example correct format
|
||||
def handler(event):
|
||||
# ... process audio ...
|
||||
return {
|
||||
"output": {
|
||||
"text": transcription_text
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Transcription not working
|
||||
|
||||
- Check browser console for errors
|
||||
- Verify your endpoint is active and responding
|
||||
- Test your endpoint directly using curl or Postman
|
||||
- Ensure audio format is supported (WAV format is recommended)
|
||||
- Check RunPod endpoint logs for server-side errors
|
||||
|
||||
## Testing Your Endpoint
|
||||
|
||||
You can test your RunPod endpoint directly:
|
||||
|
||||
```bash
|
||||
curl -X POST https://api.runpod.ai/v2/YOUR_ENDPOINT_ID/run \
|
||||
-H "Content-Type: application/json" \
|
||||
-H "Authorization: Bearer YOUR_API_KEY" \
|
||||
-d '{
|
||||
"input": {
|
||||
"audio": "base64_audio_data_here",
|
||||
"audio_format": "audio/wav",
|
||||
"language": "en"
|
||||
}
|
||||
}'
|
||||
```
|
||||
|
||||
## Fallback Behavior
|
||||
|
||||
If RunPod is not configured or fails, the system will:
|
||||
1. Try to use RunPod if configured
|
||||
2. Fall back to local Whisper model if RunPod fails or is not configured
|
||||
3. Show error messages if both methods fail
|
||||
|
||||
## Performance Considerations
|
||||
|
||||
- **RunPod**: Better for longer audio files and higher accuracy, but requires network connection
|
||||
- **Local Model**: Works offline, but requires model download and uses more client resources
|
||||
|
||||
## Support
|
||||
|
||||
For issues specific to:
|
||||
- **RunPod API**: Check [RunPod Documentation](https://docs.runpod.io)
|
||||
- **WhisperX**: Check your WhisperX endpoint configuration
|
||||
- **Integration**: Check browser console for detailed error messages
|
||||
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,139 @@
|
|||
# Testing RunPod AI Integration
|
||||
|
||||
This guide explains how to test the RunPod AI API integration in development.
|
||||
|
||||
## Quick Setup
|
||||
|
||||
1. **Add RunPod environment variables to `.env.local`:**
|
||||
|
||||
```bash
|
||||
# Add these lines to your .env.local file
|
||||
VITE_RUNPOD_API_KEY=your_runpod_api_key_here
|
||||
VITE_RUNPOD_ENDPOINT_ID=your_endpoint_id_here
|
||||
```
|
||||
|
||||
**Important:** Replace `your_runpod_api_key_here` and `your_endpoint_id_here` with your actual RunPod credentials.
|
||||
|
||||
2. **Get your RunPod credentials:**
|
||||
- **API Key**: Go to [RunPod Settings](https://www.runpod.io/console/user/settings) → API Keys section
|
||||
- **Endpoint ID**: Go to [RunPod Serverless Endpoints](https://www.runpod.io/console/serverless) → Find your endpoint → Copy the ID from the URL
|
||||
- Example: If URL is `https://api.runpod.ai/v2/jqd16o7stu29vq/run`, then `jqd16o7stu29vq` is your endpoint ID
|
||||
|
||||
3. **Restart the dev server:**
|
||||
```bash
|
||||
npm run dev
|
||||
```
|
||||
|
||||
## Testing the Integration
|
||||
|
||||
### Method 1: Using Prompt Shapes
|
||||
1. Open the canvas website in your browser
|
||||
2. Select the **Prompt** tool from the toolbar (or press the keyboard shortcut)
|
||||
3. Click on the canvas to create a prompt shape
|
||||
4. Type a prompt like "Write a hello world program in Python"
|
||||
5. Press Enter or click the send button
|
||||
6. The AI response should appear in the prompt shape
|
||||
|
||||
### Method 2: Using Arrow LLM Action
|
||||
1. Create an arrow shape pointing from one shape to another
|
||||
2. Add text to the arrow (this becomes the prompt)
|
||||
3. Select the arrow
|
||||
4. Press **Alt+G** (or use the action menu)
|
||||
5. The AI will process the prompt and fill the target shape with the response
|
||||
|
||||
### Method 3: Using Command Palette
|
||||
1. Press **Cmd+J** (Mac) or **Ctrl+J** (Windows/Linux) to open the LLM view
|
||||
2. Type your prompt
|
||||
3. Press Enter
|
||||
4. The response should appear
|
||||
|
||||
## Verifying RunPod is Being Used
|
||||
|
||||
1. **Open browser console** (F12 or Cmd+Option+I)
|
||||
2. Look for these log messages:
|
||||
- `🔑 Found RunPod configuration from environment variables - using as primary AI provider`
|
||||
- `🔍 Found X available AI providers: runpod (default)`
|
||||
- `🔄 Attempting to use runpod API (default)...`
|
||||
|
||||
3. **Check Network tab:**
|
||||
- Look for requests to `https://api.runpod.ai/v2/{endpointId}/run`
|
||||
- The request should have `Authorization: Bearer {your_api_key}` header
|
||||
|
||||
## Expected Behavior
|
||||
|
||||
- **With RunPod configured**: RunPod will be used FIRST (priority over user API keys)
|
||||
- **Without RunPod**: System will fall back to user-configured API keys (OpenAI, Anthropic, etc.)
|
||||
- **If both fail**: You'll see an error message
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### "No valid API key found for any provider"
|
||||
- Check that `.env.local` has the correct variable names (`VITE_RUNPOD_API_KEY` and `VITE_RUNPOD_ENDPOINT_ID`)
|
||||
- Restart the dev server after adding environment variables
|
||||
- Check browser console for detailed error messages
|
||||
|
||||
### "RunPod API error: 401"
|
||||
- Verify your API key is correct
|
||||
- Check that your API key hasn't expired
|
||||
- Ensure you're using the correct API key format
|
||||
|
||||
### "RunPod API error: 404"
|
||||
- Verify your endpoint ID is correct
|
||||
- Check that your endpoint is active in RunPod console
|
||||
- Ensure the endpoint URL format matches: `https://api.runpod.ai/v2/{ENDPOINT_ID}/run`
|
||||
|
||||
### RunPod not being used
|
||||
- Check browser console for `🔑 Found RunPod configuration` message
|
||||
- Verify environment variables are loaded (check `import.meta.env.VITE_RUNPOD_API_KEY` in console)
|
||||
- Make sure you restarted the dev server after adding environment variables
|
||||
|
||||
## Testing Different Scenarios
|
||||
|
||||
### Test 1: RunPod Only (No User Keys)
|
||||
1. Remove or clear any user API keys from localStorage
|
||||
2. Set RunPod environment variables
|
||||
3. Run an AI command
|
||||
4. Should use RunPod automatically
|
||||
|
||||
### Test 2: RunPod Priority (With User Keys)
|
||||
1. Set RunPod environment variables
|
||||
2. Also configure user API keys in settings
|
||||
3. Run an AI command
|
||||
4. Should use RunPod FIRST, then fall back to user keys if RunPod fails
|
||||
|
||||
### Test 3: Fallback Behavior
|
||||
1. Set RunPod environment variables with invalid credentials
|
||||
2. Configure valid user API keys
|
||||
3. Run an AI command
|
||||
4. Should try RunPod first, fail, then use user keys
|
||||
|
||||
## API Request Format
|
||||
|
||||
The integration sends requests in this format:
|
||||
|
||||
```json
|
||||
{
|
||||
"input": {
|
||||
"prompt": "Your prompt text here"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
The system prompt and user prompt are combined into a single prompt string.
|
||||
|
||||
## Response Handling
|
||||
|
||||
The integration handles multiple response formats:
|
||||
- Direct text response: `{ "output": "text" }`
|
||||
- Object with text: `{ "output": { "text": "..." } }`
|
||||
- Object with response: `{ "output": { "response": "..." } }`
|
||||
- Async jobs: Polls until completion
|
||||
|
||||
## Next Steps
|
||||
|
||||
Once testing is successful:
|
||||
1. Verify RunPod responses are working correctly
|
||||
2. Test with different prompt types
|
||||
3. Monitor RunPod usage and costs
|
||||
4. Consider adding rate limiting if needed
|
||||
|
||||
|
|
@ -1,5 +1,7 @@
|
|||
import { useCallback, useEffect, useRef, useState } from 'react'
|
||||
import { pipeline, env } from '@xenova/transformers'
|
||||
import { transcribeWithRunPod } from '../lib/runpodApi'
|
||||
import { isRunPodConfigured } from '../lib/clientConfig'
|
||||
|
||||
// Configure the transformers library
|
||||
env.allowRemoteModels = true
|
||||
|
|
@ -48,6 +50,44 @@ function detectAudioFormat(blob: Blob): Promise<string> {
|
|||
})
|
||||
}
|
||||
|
||||
// Convert Float32Array audio data to WAV blob
|
||||
async function createWavBlob(audioData: Float32Array, sampleRate: number): Promise<Blob> {
|
||||
const length = audioData.length
|
||||
const buffer = new ArrayBuffer(44 + length * 2)
|
||||
const view = new DataView(buffer)
|
||||
|
||||
// WAV header
|
||||
const writeString = (offset: number, string: string) => {
|
||||
for (let i = 0; i < string.length; i++) {
|
||||
view.setUint8(offset + i, string.charCodeAt(i))
|
||||
}
|
||||
}
|
||||
|
||||
writeString(0, 'RIFF')
|
||||
view.setUint32(4, 36 + length * 2, true)
|
||||
writeString(8, 'WAVE')
|
||||
writeString(12, 'fmt ')
|
||||
view.setUint32(16, 16, true)
|
||||
view.setUint16(20, 1, true)
|
||||
view.setUint16(22, 1, true)
|
||||
view.setUint32(24, sampleRate, true)
|
||||
view.setUint32(28, sampleRate * 2, true)
|
||||
view.setUint16(32, 2, true)
|
||||
view.setUint16(34, 16, true)
|
||||
writeString(36, 'data')
|
||||
view.setUint32(40, length * 2, true)
|
||||
|
||||
// Convert float samples to 16-bit PCM
|
||||
let offset = 44
|
||||
for (let i = 0; i < length; i++) {
|
||||
const sample = Math.max(-1, Math.min(1, audioData[i]))
|
||||
view.setInt16(offset, sample < 0 ? sample * 0x8000 : sample * 0x7FFF, true)
|
||||
offset += 2
|
||||
}
|
||||
|
||||
return new Blob([buffer], { type: 'audio/wav' })
|
||||
}
|
||||
|
||||
// Simple resampling function for audio data
|
||||
function resampleAudio(audioData: Float32Array, fromSampleRate: number, toSampleRate: number): Float32Array {
|
||||
if (fromSampleRate === toSampleRate) {
|
||||
|
|
@ -103,6 +143,7 @@ interface UseWhisperTranscriptionOptions {
|
|||
enableAdvancedErrorHandling?: boolean
|
||||
modelOptions?: ModelOption[]
|
||||
autoInitialize?: boolean // If false, model will only load when startRecording is called
|
||||
useRunPod?: boolean // If true, use RunPod WhisperX endpoint instead of local model (defaults to checking if RunPod is configured)
|
||||
}
|
||||
|
||||
export const useWhisperTranscription = ({
|
||||
|
|
@ -112,8 +153,11 @@ export const useWhisperTranscription = ({
|
|||
enableStreaming = false,
|
||||
enableAdvancedErrorHandling = false,
|
||||
modelOptions,
|
||||
autoInitialize = true // Default to true for backward compatibility
|
||||
autoInitialize = true, // Default to true for backward compatibility
|
||||
useRunPod = undefined // If undefined, auto-detect based on configuration
|
||||
}: UseWhisperTranscriptionOptions = {}) => {
|
||||
// Auto-detect RunPod usage if not explicitly set
|
||||
const shouldUseRunPod = useRunPod !== undefined ? useRunPod : isRunPodConfigured()
|
||||
const [isRecording, setIsRecording] = useState(false)
|
||||
const [isTranscribing, setIsTranscribing] = useState(false)
|
||||
const [isSpeaking, setIsSpeaking] = useState(false)
|
||||
|
|
@ -161,6 +205,13 @@ export const useWhisperTranscription = ({
|
|||
|
||||
// Initialize transcriber with optional advanced error handling
|
||||
const initializeTranscriber = useCallback(async () => {
|
||||
// Skip model loading if using RunPod
|
||||
if (shouldUseRunPod) {
|
||||
console.log('🚀 Using RunPod WhisperX endpoint - skipping local model loading')
|
||||
setModelLoaded(true) // Mark as "loaded" since we don't need a local model
|
||||
return null
|
||||
}
|
||||
|
||||
if (transcriberRef.current) return transcriberRef.current
|
||||
|
||||
try {
|
||||
|
|
@ -432,7 +483,20 @@ export const useWhisperTranscription = ({
|
|||
|
||||
console.log(`🎵 Real-time audio: ${processedAudioData.length} samples (${(processedAudioData.length / 16000).toFixed(2)}s)`)
|
||||
|
||||
// Transcribe with parameters optimized for real-time processing
|
||||
let transcriptionText = ''
|
||||
|
||||
// Use RunPod if configured, otherwise use local model
|
||||
if (shouldUseRunPod) {
|
||||
console.log('🚀 Using RunPod WhisperX API for real-time transcription...')
|
||||
// Convert processed audio data back to blob for RunPod
|
||||
const wavBlob = await createWavBlob(processedAudioData, 16000)
|
||||
transcriptionText = await transcribeWithRunPod(wavBlob, language)
|
||||
} else {
|
||||
// Use local Whisper model
|
||||
if (!transcriberRef.current) {
|
||||
console.log('⚠️ Transcriber not available for real-time processing')
|
||||
return
|
||||
}
|
||||
const result = await transcriberRef.current(processedAudioData, {
|
||||
language: language,
|
||||
task: 'transcribe',
|
||||
|
|
@ -444,7 +508,8 @@ export const useWhisperTranscription = ({
|
|||
compression_ratio_threshold: 2.0 // More permissive for real-time
|
||||
})
|
||||
|
||||
const transcriptionText = result?.text || ''
|
||||
transcriptionText = result?.text || ''
|
||||
}
|
||||
if (transcriptionText.trim()) {
|
||||
lastTranscriptionTimeRef.current = Date.now()
|
||||
console.log(`✅ Real-time transcript: "${transcriptionText.trim()}"`)
|
||||
|
|
@ -453,7 +518,8 @@ export const useWhisperTranscription = ({
|
|||
} else {
|
||||
console.log('⚠️ No real-time transcription text produced, trying fallback parameters...')
|
||||
|
||||
// Try with more permissive parameters for real-time processing
|
||||
// Try with more permissive parameters for real-time processing (only for local model)
|
||||
if (!shouldUseRunPod && transcriberRef.current) {
|
||||
try {
|
||||
const fallbackResult = await transcriberRef.current(processedAudioData, {
|
||||
task: 'transcribe',
|
||||
|
|
@ -477,16 +543,24 @@ export const useWhisperTranscription = ({
|
|||
console.log('⚠️ Fallback transcription failed:', fallbackError)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
} catch (error) {
|
||||
console.error('❌ Error processing accumulated audio chunks:', error)
|
||||
}
|
||||
}, [handleStreamingTranscriptUpdate, language])
|
||||
}, [handleStreamingTranscriptUpdate, language, shouldUseRunPod])
|
||||
|
||||
// Process recorded audio chunks (final processing)
|
||||
const processAudioChunks = useCallback(async () => {
|
||||
if (!transcriberRef.current || audioChunksRef.current.length === 0) {
|
||||
console.log('⚠️ No transcriber or audio chunks to process')
|
||||
if (audioChunksRef.current.length === 0) {
|
||||
console.log('⚠️ No audio chunks to process')
|
||||
return
|
||||
}
|
||||
|
||||
// For local model, ensure transcriber is loaded
|
||||
if (!shouldUseRunPod) {
|
||||
if (!transcriberRef.current) {
|
||||
console.log('⚠️ No transcriber available')
|
||||
return
|
||||
}
|
||||
|
||||
|
|
@ -501,6 +575,7 @@ export const useWhisperTranscription = ({
|
|||
return
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
try {
|
||||
setIsTranscribing(true)
|
||||
|
|
@ -588,15 +663,23 @@ export const useWhisperTranscription = ({
|
|||
|
||||
console.log(`🎵 Processing audio: ${processedAudioData.length} samples (${(processedAudioData.length / 16000).toFixed(2)}s)`)
|
||||
|
||||
// Check if transcriber is available
|
||||
console.log('🔄 Starting transcription...')
|
||||
|
||||
let newText = ''
|
||||
|
||||
// Use RunPod if configured, otherwise use local model
|
||||
if (shouldUseRunPod) {
|
||||
console.log('🚀 Using RunPod WhisperX API...')
|
||||
// Convert processed audio data back to blob for RunPod
|
||||
// Create a WAV blob from the Float32Array
|
||||
const wavBlob = await createWavBlob(processedAudioData, 16000)
|
||||
newText = await transcribeWithRunPod(wavBlob, language)
|
||||
console.log('✅ RunPod transcription result:', newText)
|
||||
} else {
|
||||
// Use local Whisper model
|
||||
if (!transcriberRef.current) {
|
||||
console.error('❌ Transcriber not available for processing')
|
||||
throw new Error('Transcriber not initialized')
|
||||
}
|
||||
|
||||
console.log('🔄 Starting transcription with Whisper model...')
|
||||
|
||||
// Transcribe the audio
|
||||
const result = await transcriberRef.current(processedAudioData, {
|
||||
language: language,
|
||||
task: 'transcribe',
|
||||
|
|
@ -604,8 +687,8 @@ export const useWhisperTranscription = ({
|
|||
})
|
||||
|
||||
console.log('🔍 Transcription result:', result)
|
||||
|
||||
const newText = result?.text?.trim() || ''
|
||||
newText = result?.text?.trim() || ''
|
||||
}
|
||||
if (newText) {
|
||||
const processedText = processTranscript(newText, enableStreaming)
|
||||
|
||||
|
|
@ -633,7 +716,8 @@ export const useWhisperTranscription = ({
|
|||
console.log('⚠️ No transcription text produced')
|
||||
console.log('🔍 Full transcription result object:', result)
|
||||
|
||||
// Try alternative transcription parameters
|
||||
// Try alternative transcription parameters (only for local model)
|
||||
if (!shouldUseRunPod && transcriberRef.current) {
|
||||
console.log('🔄 Trying alternative transcription parameters...')
|
||||
try {
|
||||
const altResult = await transcriberRef.current(processedAudioData, {
|
||||
|
|
@ -662,6 +746,7 @@ export const useWhisperTranscription = ({
|
|||
console.log('⚠️ Alternative transcription also failed:', altError)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Clear processed chunks
|
||||
audioChunksRef.current = []
|
||||
|
|
@ -672,7 +757,7 @@ export const useWhisperTranscription = ({
|
|||
} finally {
|
||||
setIsTranscribing(false)
|
||||
}
|
||||
}, [transcriberRef, language, onTranscriptUpdate, onError, enableStreaming, handleStreamingTranscriptUpdate, modelLoaded, initializeTranscriber])
|
||||
}, [transcriberRef, language, onTranscriptUpdate, onError, enableStreaming, handleStreamingTranscriptUpdate, modelLoaded, initializeTranscriber, shouldUseRunPod])
|
||||
|
||||
// Start recording
|
||||
const startRecording = useCallback(async () => {
|
||||
|
|
@ -680,10 +765,13 @@ export const useWhisperTranscription = ({
|
|||
console.log('🎤 Starting recording...')
|
||||
console.log('🔍 enableStreaming in startRecording:', enableStreaming)
|
||||
|
||||
// Ensure model is loaded before starting
|
||||
if (!modelLoaded) {
|
||||
// Ensure model is loaded before starting (skip for RunPod)
|
||||
if (!shouldUseRunPod && !modelLoaded) {
|
||||
console.log('🔄 Model not loaded, initializing...')
|
||||
await initializeTranscriber()
|
||||
} else if (shouldUseRunPod) {
|
||||
// For RunPod, just mark as ready
|
||||
setModelLoaded(true)
|
||||
}
|
||||
|
||||
// Don't reset transcripts for continuous transcription - keep existing content
|
||||
|
|
@ -803,7 +891,7 @@ export const useWhisperTranscription = ({
|
|||
console.error('❌ Error starting recording:', error)
|
||||
onError?.(error as Error)
|
||||
}
|
||||
}, [processAudioChunks, processAccumulatedAudioChunks, onError, enableStreaming, modelLoaded, initializeTranscriber])
|
||||
}, [processAudioChunks, processAccumulatedAudioChunks, onError, enableStreaming, modelLoaded, initializeTranscriber, shouldUseRunPod])
|
||||
|
||||
// Stop recording
|
||||
const stopRecording = useCallback(async () => {
|
||||
|
|
@ -892,9 +980,11 @@ export const useWhisperTranscription = ({
|
|||
periodicTranscriptionRef.current = null
|
||||
}
|
||||
|
||||
// Initialize the model if not already loaded
|
||||
if (!modelLoaded) {
|
||||
// Initialize the model if not already loaded (skip for RunPod)
|
||||
if (!shouldUseRunPod && !modelLoaded) {
|
||||
await initializeTranscriber()
|
||||
} else if (shouldUseRunPod) {
|
||||
setModelLoaded(true)
|
||||
}
|
||||
|
||||
await startRecording()
|
||||
|
|
@ -933,7 +1023,7 @@ export const useWhisperTranscription = ({
|
|||
if (autoInitialize) {
|
||||
initializeTranscriber().catch(console.warn)
|
||||
}
|
||||
}, [initializeTranscriber, autoInitialize])
|
||||
}, [initializeTranscriber, autoInitialize, shouldUseRunPod])
|
||||
|
||||
// Cleanup on unmount
|
||||
useEffect(() => {
|
||||
|
|
|
|||
|
|
@ -0,0 +1,327 @@
|
|||
/**
|
||||
* AI Orchestrator Client
|
||||
* Smart routing between local RS 8000 CPU and RunPod GPU
|
||||
*/
|
||||
|
||||
export interface AIJob {
|
||||
job_id: string
|
||||
status: 'queued' | 'processing' | 'completed' | 'failed'
|
||||
result?: any
|
||||
cost?: number
|
||||
provider?: string
|
||||
processing_time?: number
|
||||
error?: string
|
||||
}
|
||||
|
||||
export interface TextGenerationOptions {
|
||||
model?: string
|
||||
priority?: 'low' | 'normal' | 'high'
|
||||
userId?: string
|
||||
wait?: boolean
|
||||
}
|
||||
|
||||
export interface ImageGenerationOptions {
|
||||
model?: string
|
||||
priority?: 'low' | 'normal' | 'high'
|
||||
size?: string
|
||||
userId?: string
|
||||
wait?: boolean
|
||||
}
|
||||
|
||||
export interface VideoGenerationOptions {
|
||||
model?: string
|
||||
duration?: number
|
||||
userId?: string
|
||||
wait?: boolean
|
||||
}
|
||||
|
||||
export interface CodeGenerationOptions {
|
||||
language?: string
|
||||
priority?: 'low' | 'normal' | 'high'
|
||||
userId?: string
|
||||
wait?: boolean
|
||||
}
|
||||
|
||||
export interface QueueStatus {
|
||||
queues: {
|
||||
text_local: number
|
||||
text_runpod: number
|
||||
image_local: number
|
||||
image_runpod: number
|
||||
video_runpod: number
|
||||
code_local: number
|
||||
}
|
||||
total_pending: number
|
||||
timestamp: string
|
||||
}
|
||||
|
||||
export interface CostSummary {
|
||||
today: {
|
||||
local: number
|
||||
runpod: number
|
||||
total: number
|
||||
}
|
||||
this_month: {
|
||||
local: number
|
||||
runpod: number
|
||||
total: number
|
||||
}
|
||||
breakdown: {
|
||||
text: number
|
||||
image: number
|
||||
video: number
|
||||
code: number
|
||||
}
|
||||
}
|
||||
|
||||
export class AIOrchestrator {
|
||||
private baseUrl: string
|
||||
|
||||
constructor(baseUrl?: string) {
|
||||
this.baseUrl = baseUrl ||
|
||||
import.meta.env.VITE_AI_ORCHESTRATOR_URL ||
|
||||
'http://159.195.32.209:8000'
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate text using LLM
|
||||
* Routes to local Ollama (FREE) by default
|
||||
*/
|
||||
async generateText(
|
||||
prompt: string,
|
||||
options: TextGenerationOptions = {}
|
||||
): Promise<AIJob> {
|
||||
const response = await fetch(`${this.baseUrl}/generate/text`, {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify({
|
||||
prompt,
|
||||
model: options.model || 'llama3-70b',
|
||||
priority: options.priority || 'normal',
|
||||
user_id: options.userId,
|
||||
wait: options.wait || false
|
||||
})
|
||||
})
|
||||
|
||||
if (!response.ok) {
|
||||
throw new Error(`AI Orchestrator error: ${response.status} ${response.statusText}`)
|
||||
}
|
||||
|
||||
const job = await response.json() as AIJob
|
||||
|
||||
if (options.wait) {
|
||||
return this.waitForJob(job.job_id)
|
||||
}
|
||||
|
||||
return job
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate image
|
||||
* Low priority → Local SD CPU (slow but FREE)
|
||||
* High priority → RunPod GPU (fast, $0.02)
|
||||
*/
|
||||
async generateImage(
|
||||
prompt: string,
|
||||
options: ImageGenerationOptions = {}
|
||||
): Promise<AIJob> {
|
||||
const response = await fetch(`${this.baseUrl}/generate/image`, {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify({
|
||||
prompt,
|
||||
model: options.model || 'sdxl',
|
||||
priority: options.priority || 'normal',
|
||||
size: options.size || '1024x1024',
|
||||
user_id: options.userId,
|
||||
wait: options.wait || false
|
||||
})
|
||||
})
|
||||
|
||||
if (!response.ok) {
|
||||
throw new Error(`AI Orchestrator error: ${response.status} ${response.statusText}`)
|
||||
}
|
||||
|
||||
const job = await response.json() as AIJob
|
||||
|
||||
if (options.wait) {
|
||||
return this.waitForJob(job.job_id)
|
||||
}
|
||||
|
||||
return job
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate video
|
||||
* Always uses RunPod GPU with Wan2.1 model
|
||||
*/
|
||||
async generateVideo(
|
||||
prompt: string,
|
||||
options: VideoGenerationOptions = {}
|
||||
): Promise<AIJob> {
|
||||
const response = await fetch(`${this.baseUrl}/generate/video`, {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify({
|
||||
prompt,
|
||||
model: options.model || 'wan2.1-i2v',
|
||||
duration: options.duration || 3,
|
||||
user_id: options.userId,
|
||||
wait: options.wait || false
|
||||
})
|
||||
})
|
||||
|
||||
if (!response.ok) {
|
||||
throw new Error(`AI Orchestrator error: ${response.status} ${response.statusText}`)
|
||||
}
|
||||
|
||||
const job = await response.json() as AIJob
|
||||
|
||||
if (options.wait) {
|
||||
return this.waitForJob(job.job_id)
|
||||
}
|
||||
|
||||
return job
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate code
|
||||
* Always uses local Ollama with CodeLlama (FREE)
|
||||
*/
|
||||
async generateCode(
|
||||
prompt: string,
|
||||
options: CodeGenerationOptions = {}
|
||||
): Promise<AIJob> {
|
||||
const response = await fetch(`${this.baseUrl}/generate/code`, {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify({
|
||||
prompt,
|
||||
language: options.language || 'python',
|
||||
priority: options.priority || 'normal',
|
||||
user_id: options.userId,
|
||||
wait: options.wait || false
|
||||
})
|
||||
})
|
||||
|
||||
if (!response.ok) {
|
||||
throw new Error(`AI Orchestrator error: ${response.status} ${response.statusText}`)
|
||||
}
|
||||
|
||||
const job = await response.json() as AIJob
|
||||
|
||||
if (options.wait) {
|
||||
return this.waitForJob(job.job_id)
|
||||
}
|
||||
|
||||
return job
|
||||
}
|
||||
|
||||
/**
|
||||
* Get job status
|
||||
*/
|
||||
async getJobStatus(jobId: string): Promise<AIJob> {
|
||||
const response = await fetch(`${this.baseUrl}/job/${jobId}`)
|
||||
|
||||
if (!response.ok) {
|
||||
throw new Error(`Failed to get job status: ${response.status} ${response.statusText}`)
|
||||
}
|
||||
|
||||
return response.json()
|
||||
}
|
||||
|
||||
/**
|
||||
* Wait for job to complete
|
||||
*/
|
||||
async waitForJob(
|
||||
jobId: string,
|
||||
maxAttempts: number = 120,
|
||||
pollInterval: number = 1000
|
||||
): Promise<AIJob> {
|
||||
for (let i = 0; i < maxAttempts; i++) {
|
||||
const job = await this.getJobStatus(jobId)
|
||||
|
||||
if (job.status === 'completed') {
|
||||
return job
|
||||
}
|
||||
|
||||
if (job.status === 'failed') {
|
||||
throw new Error(`Job failed: ${job.error || 'Unknown error'}`)
|
||||
}
|
||||
|
||||
// Still queued or processing, wait and retry
|
||||
await new Promise(resolve => setTimeout(resolve, pollInterval))
|
||||
}
|
||||
|
||||
throw new Error(`Job ${jobId} timed out after ${maxAttempts} attempts`)
|
||||
}
|
||||
|
||||
/**
|
||||
* Get current queue status
|
||||
*/
|
||||
async getQueueStatus(): Promise<QueueStatus> {
|
||||
const response = await fetch(`${this.baseUrl}/queue/status`)
|
||||
|
||||
if (!response.ok) {
|
||||
throw new Error(`Failed to get queue status: ${response.status} ${response.statusText}`)
|
||||
}
|
||||
|
||||
return response.json()
|
||||
}
|
||||
|
||||
/**
|
||||
* Get cost summary
|
||||
*/
|
||||
async getCostSummary(): Promise<CostSummary> {
|
||||
const response = await fetch(`${this.baseUrl}/costs/summary`)
|
||||
|
||||
if (!response.ok) {
|
||||
throw new Error(`Failed to get cost summary: ${response.status} ${response.statusText}`)
|
||||
}
|
||||
|
||||
return response.json()
|
||||
}
|
||||
|
||||
/**
|
||||
* Check if AI Orchestrator is available
|
||||
*/
|
||||
async isAvailable(): Promise<boolean> {
|
||||
try {
|
||||
const response = await fetch(`${this.baseUrl}/health`, {
|
||||
method: 'GET',
|
||||
signal: AbortSignal.timeout(5000) // 5 second timeout
|
||||
})
|
||||
return response.ok
|
||||
} catch {
|
||||
return false
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Singleton instance
|
||||
export const aiOrchestrator = new AIOrchestrator()
|
||||
|
||||
/**
|
||||
* Helper function to check if AI Orchestrator is configured and available
|
||||
*/
|
||||
export async function isAIOrchestratorAvailable(): Promise<boolean> {
|
||||
const url = import.meta.env.VITE_AI_ORCHESTRATOR_URL
|
||||
|
||||
if (!url) {
|
||||
console.log('🔍 AI Orchestrator URL not configured')
|
||||
return false
|
||||
}
|
||||
|
||||
try {
|
||||
const available = await aiOrchestrator.isAvailable()
|
||||
if (available) {
|
||||
console.log('✅ AI Orchestrator is available at', url)
|
||||
} else {
|
||||
console.log('⚠️ AI Orchestrator configured but not responding at', url)
|
||||
}
|
||||
return available
|
||||
} catch (error) {
|
||||
console.log('❌ Error checking AI Orchestrator availability:', error)
|
||||
return false
|
||||
}
|
||||
}
|
||||
|
|
@ -14,6 +14,13 @@ export interface ClientConfig {
|
|||
webhookUrl?: string
|
||||
webhookSecret?: string
|
||||
openaiApiKey?: string
|
||||
runpodApiKey?: string
|
||||
runpodEndpointId?: string
|
||||
runpodImageEndpointId?: string
|
||||
runpodVideoEndpointId?: string
|
||||
runpodTextEndpointId?: string
|
||||
runpodWhisperEndpointId?: string
|
||||
ollamaUrl?: string
|
||||
}
|
||||
|
||||
/**
|
||||
|
|
@ -38,6 +45,13 @@ export function getClientConfig(): ClientConfig {
|
|||
webhookUrl: import.meta.env.VITE_QUARTZ_WEBHOOK_URL || import.meta.env.NEXT_PUBLIC_QUARTZ_WEBHOOK_URL,
|
||||
webhookSecret: import.meta.env.VITE_QUARTZ_WEBHOOK_SECRET || import.meta.env.NEXT_PUBLIC_QUARTZ_WEBHOOK_SECRET,
|
||||
openaiApiKey: import.meta.env.VITE_OPENAI_API_KEY || import.meta.env.NEXT_PUBLIC_OPENAI_API_KEY,
|
||||
runpodApiKey: import.meta.env.VITE_RUNPOD_API_KEY || import.meta.env.NEXT_PUBLIC_RUNPOD_API_KEY,
|
||||
runpodEndpointId: import.meta.env.VITE_RUNPOD_ENDPOINT_ID || import.meta.env.VITE_RUNPOD_IMAGE_ENDPOINT_ID || import.meta.env.NEXT_PUBLIC_RUNPOD_ENDPOINT_ID,
|
||||
runpodImageEndpointId: import.meta.env.VITE_RUNPOD_IMAGE_ENDPOINT_ID || import.meta.env.NEXT_PUBLIC_RUNPOD_IMAGE_ENDPOINT_ID,
|
||||
runpodVideoEndpointId: import.meta.env.VITE_RUNPOD_VIDEO_ENDPOINT_ID || import.meta.env.NEXT_PUBLIC_RUNPOD_VIDEO_ENDPOINT_ID,
|
||||
runpodTextEndpointId: import.meta.env.VITE_RUNPOD_TEXT_ENDPOINT_ID || import.meta.env.NEXT_PUBLIC_RUNPOD_TEXT_ENDPOINT_ID,
|
||||
runpodWhisperEndpointId: import.meta.env.VITE_RUNPOD_WHISPER_ENDPOINT_ID || import.meta.env.NEXT_PUBLIC_RUNPOD_WHISPER_ENDPOINT_ID,
|
||||
ollamaUrl: import.meta.env.VITE_OLLAMA_URL || import.meta.env.NEXT_PUBLIC_OLLAMA_URL,
|
||||
}
|
||||
} else {
|
||||
// Next.js environment
|
||||
|
|
@ -52,6 +66,8 @@ export function getClientConfig(): ClientConfig {
|
|||
webhookUrl: (window as any).__NEXT_DATA__?.env?.NEXT_PUBLIC_QUARTZ_WEBHOOK_URL,
|
||||
webhookSecret: (window as any).__NEXT_DATA__?.env?.NEXT_PUBLIC_QUARTZ_WEBHOOK_SECRET,
|
||||
openaiApiKey: (window as any).__NEXT_DATA__?.env?.NEXT_PUBLIC_OPENAI_API_KEY,
|
||||
runpodApiKey: (window as any).__NEXT_DATA__?.env?.NEXT_PUBLIC_RUNPOD_API_KEY,
|
||||
runpodEndpointId: (window as any).__NEXT_DATA__?.env?.NEXT_PUBLIC_RUNPOD_ENDPOINT_ID,
|
||||
}
|
||||
}
|
||||
} else {
|
||||
|
|
@ -66,10 +82,121 @@ export function getClientConfig(): ClientConfig {
|
|||
quartzApiKey: process.env.VITE_QUARTZ_API_KEY || process.env.NEXT_PUBLIC_QUARTZ_API_KEY,
|
||||
webhookUrl: process.env.VITE_QUARTZ_WEBHOOK_URL || process.env.NEXT_PUBLIC_QUARTZ_WEBHOOK_URL,
|
||||
webhookSecret: process.env.VITE_QUARTZ_WEBHOOK_SECRET || process.env.NEXT_PUBLIC_QUARTZ_WEBHOOK_SECRET,
|
||||
runpodApiKey: process.env.VITE_RUNPOD_API_KEY || process.env.NEXT_PUBLIC_RUNPOD_API_KEY,
|
||||
runpodEndpointId: process.env.VITE_RUNPOD_ENDPOINT_ID || process.env.VITE_RUNPOD_IMAGE_ENDPOINT_ID || process.env.NEXT_PUBLIC_RUNPOD_ENDPOINT_ID,
|
||||
runpodImageEndpointId: process.env.VITE_RUNPOD_IMAGE_ENDPOINT_ID || process.env.NEXT_PUBLIC_RUNPOD_IMAGE_ENDPOINT_ID,
|
||||
runpodVideoEndpointId: process.env.VITE_RUNPOD_VIDEO_ENDPOINT_ID || process.env.NEXT_PUBLIC_RUNPOD_VIDEO_ENDPOINT_ID,
|
||||
runpodTextEndpointId: process.env.VITE_RUNPOD_TEXT_ENDPOINT_ID || process.env.NEXT_PUBLIC_RUNPOD_TEXT_ENDPOINT_ID,
|
||||
runpodWhisperEndpointId: process.env.VITE_RUNPOD_WHISPER_ENDPOINT_ID || process.env.NEXT_PUBLIC_RUNPOD_WHISPER_ENDPOINT_ID,
|
||||
ollamaUrl: process.env.VITE_OLLAMA_URL || process.env.NEXT_PUBLIC_OLLAMA_URL,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Get RunPod configuration for API calls (defaults to image endpoint)
|
||||
*/
|
||||
export function getRunPodConfig(): { apiKey: string; endpointId: string } | null {
|
||||
const config = getClientConfig()
|
||||
|
||||
if (!config.runpodApiKey || !config.runpodEndpointId) {
|
||||
return null
|
||||
}
|
||||
|
||||
return {
|
||||
apiKey: config.runpodApiKey,
|
||||
endpointId: config.runpodEndpointId
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Get RunPod configuration for image generation
|
||||
*/
|
||||
export function getRunPodImageConfig(): { apiKey: string; endpointId: string } | null {
|
||||
const config = getClientConfig()
|
||||
const endpointId = config.runpodImageEndpointId || config.runpodEndpointId
|
||||
|
||||
if (!config.runpodApiKey || !endpointId) {
|
||||
return null
|
||||
}
|
||||
|
||||
return {
|
||||
apiKey: config.runpodApiKey,
|
||||
endpointId: endpointId
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Get RunPod configuration for video generation
|
||||
*/
|
||||
export function getRunPodVideoConfig(): { apiKey: string; endpointId: string } | null {
|
||||
const config = getClientConfig()
|
||||
|
||||
if (!config.runpodApiKey || !config.runpodVideoEndpointId) {
|
||||
return null
|
||||
}
|
||||
|
||||
return {
|
||||
apiKey: config.runpodApiKey,
|
||||
endpointId: config.runpodVideoEndpointId
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Get RunPod configuration for text generation (vLLM)
|
||||
*/
|
||||
export function getRunPodTextConfig(): { apiKey: string; endpointId: string } | null {
|
||||
const config = getClientConfig()
|
||||
|
||||
if (!config.runpodApiKey || !config.runpodTextEndpointId) {
|
||||
return null
|
||||
}
|
||||
|
||||
return {
|
||||
apiKey: config.runpodApiKey,
|
||||
endpointId: config.runpodTextEndpointId
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Get RunPod configuration for Whisper transcription
|
||||
*/
|
||||
export function getRunPodWhisperConfig(): { apiKey: string; endpointId: string } | null {
|
||||
const config = getClientConfig()
|
||||
|
||||
if (!config.runpodApiKey || !config.runpodWhisperEndpointId) {
|
||||
return null
|
||||
}
|
||||
|
||||
return {
|
||||
apiKey: config.runpodApiKey,
|
||||
endpointId: config.runpodWhisperEndpointId
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Get Ollama configuration for local LLM
|
||||
*/
|
||||
export function getOllamaConfig(): { url: string } | null {
|
||||
const config = getClientConfig()
|
||||
|
||||
if (!config.ollamaUrl) {
|
||||
return null
|
||||
}
|
||||
|
||||
return {
|
||||
url: config.ollamaUrl
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Check if RunPod integration is configured
|
||||
*/
|
||||
export function isRunPodConfigured(): boolean {
|
||||
const config = getClientConfig()
|
||||
return !!(config.runpodApiKey && config.runpodEndpointId)
|
||||
}
|
||||
|
||||
/**
|
||||
* Check if GitHub integration is configured
|
||||
*/
|
||||
|
|
|
|||
|
|
@ -0,0 +1,246 @@
|
|||
/**
|
||||
* RunPod API utility functions
|
||||
* Handles communication with RunPod WhisperX endpoints
|
||||
*/
|
||||
|
||||
import { getRunPodConfig } from './clientConfig'
|
||||
|
||||
export interface RunPodTranscriptionResponse {
|
||||
id?: string
|
||||
status?: string
|
||||
output?: {
|
||||
text?: string
|
||||
segments?: Array<{
|
||||
start: number
|
||||
end: number
|
||||
text: string
|
||||
}>
|
||||
}
|
||||
error?: string
|
||||
}
|
||||
|
||||
/**
|
||||
* Convert audio blob to base64 string
|
||||
*/
|
||||
export async function blobToBase64(blob: Blob): Promise<string> {
|
||||
return new Promise((resolve, reject) => {
|
||||
const reader = new FileReader()
|
||||
reader.onloadend = () => {
|
||||
if (typeof reader.result === 'string') {
|
||||
// Remove data URL prefix (e.g., "data:audio/webm;base64,")
|
||||
const base64 = reader.result.split(',')[1] || reader.result
|
||||
resolve(base64)
|
||||
} else {
|
||||
reject(new Error('Failed to convert blob to base64'))
|
||||
}
|
||||
}
|
||||
reader.onerror = reject
|
||||
reader.readAsDataURL(blob)
|
||||
})
|
||||
}
|
||||
|
||||
/**
|
||||
* Send transcription request to RunPod endpoint
|
||||
* Handles both synchronous and asynchronous job patterns
|
||||
*/
|
||||
export async function transcribeWithRunPod(
|
||||
audioBlob: Blob,
|
||||
language?: string
|
||||
): Promise<string> {
|
||||
const config = getRunPodConfig()
|
||||
|
||||
if (!config) {
|
||||
throw new Error('RunPod API key or endpoint ID not configured. Please set VITE_RUNPOD_API_KEY and VITE_RUNPOD_ENDPOINT_ID environment variables.')
|
||||
}
|
||||
|
||||
// Check audio blob size (limit to ~10MB to prevent issues)
|
||||
const maxSize = 10 * 1024 * 1024 // 10MB
|
||||
if (audioBlob.size > maxSize) {
|
||||
throw new Error(`Audio file too large: ${(audioBlob.size / 1024 / 1024).toFixed(2)}MB. Maximum size is ${(maxSize / 1024 / 1024).toFixed(2)}MB`)
|
||||
}
|
||||
|
||||
// Convert audio blob to base64
|
||||
const audioBase64 = await blobToBase64(audioBlob)
|
||||
|
||||
// Detect audio format from blob type
|
||||
const audioFormat = audioBlob.type || 'audio/wav'
|
||||
|
||||
const url = `https://api.runpod.ai/v2/${config.endpointId}/run`
|
||||
|
||||
// Prepare the request payload
|
||||
// WhisperX typically expects audio as base64 or file URL
|
||||
// The exact format may vary based on your WhisperX endpoint implementation
|
||||
const requestBody = {
|
||||
input: {
|
||||
audio: audioBase64,
|
||||
audio_format: audioFormat,
|
||||
language: language || 'en',
|
||||
task: 'transcribe'
|
||||
// Note: Some WhisperX endpoints may expect different field names
|
||||
// Adjust the requestBody structure in this function if needed
|
||||
}
|
||||
}
|
||||
|
||||
try {
|
||||
// Add timeout to prevent hanging requests (30 seconds for initial request)
|
||||
const controller = new AbortController()
|
||||
const timeoutId = setTimeout(() => controller.abort(), 30000)
|
||||
|
||||
const response = await fetch(url, {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
'Authorization': `Bearer ${config.apiKey}`
|
||||
},
|
||||
body: JSON.stringify(requestBody),
|
||||
signal: controller.signal
|
||||
})
|
||||
|
||||
clearTimeout(timeoutId)
|
||||
|
||||
if (!response.ok) {
|
||||
const errorText = await response.text()
|
||||
console.error('RunPod API error response:', {
|
||||
status: response.status,
|
||||
statusText: response.statusText,
|
||||
body: errorText
|
||||
})
|
||||
throw new Error(`RunPod API error: ${response.status} - ${errorText}`)
|
||||
}
|
||||
|
||||
const data: RunPodTranscriptionResponse = await response.json()
|
||||
|
||||
console.log('RunPod initial response:', data)
|
||||
|
||||
// Handle async job pattern (RunPod often returns job IDs)
|
||||
if (data.id && (data.status === 'IN_QUEUE' || data.status === 'IN_PROGRESS')) {
|
||||
console.log('Job is async, polling for results...', data.id)
|
||||
return await pollRunPodJob(data.id, config.apiKey, config.endpointId)
|
||||
}
|
||||
|
||||
// Handle direct response
|
||||
if (data.output?.text) {
|
||||
return data.output.text.trim()
|
||||
}
|
||||
|
||||
// Handle error response
|
||||
if (data.error) {
|
||||
throw new Error(`RunPod transcription error: ${data.error}`)
|
||||
}
|
||||
|
||||
// Fallback: try to extract text from segments
|
||||
if (data.output?.segments && data.output.segments.length > 0) {
|
||||
return data.output.segments.map(seg => seg.text).join(' ').trim()
|
||||
}
|
||||
|
||||
// Check if response has unexpected structure
|
||||
console.warn('Unexpected RunPod response structure:', data)
|
||||
throw new Error('No transcription text found in RunPod response. Check endpoint response format.')
|
||||
} catch (error: any) {
|
||||
if (error.name === 'AbortError') {
|
||||
throw new Error('RunPod request timed out after 30 seconds')
|
||||
}
|
||||
console.error('RunPod transcription error:', error)
|
||||
throw error
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Poll RunPod job status until completion
|
||||
*/
|
||||
async function pollRunPodJob(
|
||||
jobId: string,
|
||||
apiKey: string,
|
||||
endpointId: string,
|
||||
maxAttempts: number = 120, // Increased to 120 attempts (2 minutes at 1s intervals)
|
||||
pollInterval: number = 1000
|
||||
): Promise<string> {
|
||||
const statusUrl = `https://api.runpod.ai/v2/${endpointId}/status/${jobId}`
|
||||
|
||||
console.log(`Polling job ${jobId} (max ${maxAttempts} attempts, ${pollInterval}ms interval)`)
|
||||
|
||||
for (let attempt = 0; attempt < maxAttempts; attempt++) {
|
||||
try {
|
||||
// Add timeout for each status check (5 seconds)
|
||||
const controller = new AbortController()
|
||||
const timeoutId = setTimeout(() => controller.abort(), 5000)
|
||||
|
||||
const response = await fetch(statusUrl, {
|
||||
method: 'GET',
|
||||
headers: {
|
||||
'Authorization': `Bearer ${apiKey}`
|
||||
},
|
||||
signal: controller.signal
|
||||
})
|
||||
|
||||
clearTimeout(timeoutId)
|
||||
|
||||
if (!response.ok) {
|
||||
const errorText = await response.text()
|
||||
console.error(`Job status check failed (attempt ${attempt + 1}/${maxAttempts}):`, {
|
||||
status: response.status,
|
||||
statusText: response.statusText,
|
||||
body: errorText
|
||||
})
|
||||
|
||||
// Don't fail immediately on 404 - job might still be processing
|
||||
if (response.status === 404 && attempt < maxAttempts - 1) {
|
||||
console.log('Job not found yet, continuing to poll...')
|
||||
await new Promise(resolve => setTimeout(resolve, pollInterval))
|
||||
continue
|
||||
}
|
||||
|
||||
throw new Error(`Failed to check job status: ${response.status} - ${errorText}`)
|
||||
}
|
||||
|
||||
const data: RunPodTranscriptionResponse = await response.json()
|
||||
|
||||
console.log(`Job status (attempt ${attempt + 1}/${maxAttempts}):`, data.status)
|
||||
|
||||
if (data.status === 'COMPLETED') {
|
||||
console.log('Job completed, extracting transcription...')
|
||||
|
||||
if (data.output?.text) {
|
||||
return data.output.text.trim()
|
||||
}
|
||||
if (data.output?.segments && data.output.segments.length > 0) {
|
||||
return data.output.segments.map(seg => seg.text).join(' ').trim()
|
||||
}
|
||||
|
||||
// Log the full response for debugging
|
||||
console.error('Job completed but no transcription found. Full response:', JSON.stringify(data, null, 2))
|
||||
throw new Error('Job completed but no transcription text found in response')
|
||||
}
|
||||
|
||||
if (data.status === 'FAILED') {
|
||||
const errorMsg = data.error || 'Unknown error'
|
||||
console.error('Job failed:', errorMsg)
|
||||
throw new Error(`Job failed: ${errorMsg}`)
|
||||
}
|
||||
|
||||
// Job still in progress, wait and retry
|
||||
if (attempt % 10 === 0) {
|
||||
console.log(`Job still processing... (${attempt + 1}/${maxAttempts} attempts)`)
|
||||
}
|
||||
await new Promise(resolve => setTimeout(resolve, pollInterval))
|
||||
} catch (error: any) {
|
||||
if (error.name === 'AbortError') {
|
||||
console.warn(`Status check timed out (attempt ${attempt + 1}/${maxAttempts})`)
|
||||
if (attempt < maxAttempts - 1) {
|
||||
await new Promise(resolve => setTimeout(resolve, pollInterval))
|
||||
continue
|
||||
}
|
||||
throw new Error('Status check timed out multiple times')
|
||||
}
|
||||
|
||||
if (attempt === maxAttempts - 1) {
|
||||
throw error
|
||||
}
|
||||
// Wait before retrying
|
||||
await new Promise(resolve => setTimeout(resolve, pollInterval))
|
||||
}
|
||||
}
|
||||
|
||||
throw new Error(`Job polling timeout after ${maxAttempts} attempts (${(maxAttempts * pollInterval / 1000).toFixed(0)} seconds)`)
|
||||
}
|
||||
|
||||
|
|
@ -41,7 +41,11 @@ import { FathomMeetingsTool } from "@/tools/FathomMeetingsTool"
|
|||
import { HolonBrowserShape } from "@/shapes/HolonBrowserShapeUtil"
|
||||
import { ObsidianBrowserShape } from "@/shapes/ObsidianBrowserShapeUtil"
|
||||
import { FathomMeetingsBrowserShape } from "@/shapes/FathomMeetingsBrowserShapeUtil"
|
||||
// Location shape removed - no longer needed
|
||||
import { LocationShareShape } from "@/shapes/LocationShareShapeUtil"
|
||||
import { ImageGenShape } from "@/shapes/ImageGenShapeUtil"
|
||||
import { ImageGenTool } from "@/tools/ImageGenTool"
|
||||
import { VideoGenShape } from "@/shapes/VideoGenShapeUtil"
|
||||
import { VideoGenTool } from "@/tools/VideoGenTool"
|
||||
import {
|
||||
lockElement,
|
||||
unlockElement,
|
||||
|
|
@ -81,6 +85,9 @@ const customShapeUtils = [
|
|||
HolonBrowserShape,
|
||||
ObsidianBrowserShape,
|
||||
FathomMeetingsBrowserShape,
|
||||
LocationShareShape,
|
||||
ImageGenShape,
|
||||
VideoGenShape,
|
||||
]
|
||||
const customTools = [
|
||||
ChatBoxTool,
|
||||
|
|
@ -95,6 +102,8 @@ const customTools = [
|
|||
TranscriptionTool,
|
||||
HolonTool,
|
||||
FathomMeetingsTool,
|
||||
ImageGenTool,
|
||||
VideoGenTool,
|
||||
]
|
||||
|
||||
export function Board() {
|
||||
|
|
|
|||
|
|
@ -0,0 +1,731 @@
|
|||
import {
|
||||
BaseBoxShapeUtil,
|
||||
Geometry2d,
|
||||
HTMLContainer,
|
||||
Rectangle2d,
|
||||
TLBaseShape,
|
||||
} from "tldraw"
|
||||
import React, { useState } from "react"
|
||||
import { getRunPodConfig } from "@/lib/clientConfig"
|
||||
import { aiOrchestrator, isAIOrchestratorAvailable } from "@/lib/aiOrchestrator"
|
||||
|
||||
// Feature flag: Set to false when AI Orchestrator or RunPod API is ready for production
|
||||
const USE_MOCK_API = false
|
||||
|
||||
// Type definition for RunPod API responses
|
||||
interface RunPodJobResponse {
|
||||
id?: string
|
||||
status?: 'IN_QUEUE' | 'IN_PROGRESS' | 'STARTING' | 'COMPLETED' | 'FAILED' | 'CANCELLED'
|
||||
output?: string | {
|
||||
image?: string
|
||||
url?: string
|
||||
images?: Array<{ data?: string; url?: string; filename?: string; type?: string }>
|
||||
result?: string
|
||||
[key: string]: any
|
||||
}
|
||||
error?: string
|
||||
image?: string
|
||||
url?: string
|
||||
result?: string | {
|
||||
image?: string
|
||||
url?: string
|
||||
[key: string]: any
|
||||
}
|
||||
[key: string]: any
|
||||
}
|
||||
|
||||
type IImageGen = TLBaseShape<
|
||||
"ImageGen",
|
||||
{
|
||||
w: number
|
||||
h: number
|
||||
prompt: string
|
||||
imageUrl: string | null
|
||||
isLoading: boolean
|
||||
error: string | null
|
||||
endpointId?: string // Optional custom endpoint ID
|
||||
}
|
||||
>
|
||||
|
||||
// Helper function to poll RunPod job status until completion
|
||||
async function pollRunPodJob(
|
||||
jobId: string,
|
||||
apiKey: string,
|
||||
endpointId: string,
|
||||
maxAttempts: number = 60,
|
||||
pollInterval: number = 2000
|
||||
): Promise<string> {
|
||||
const statusUrl = `https://api.runpod.ai/v2/${endpointId}/status/${jobId}`
|
||||
console.log('🔄 ImageGen: Polling job:', jobId)
|
||||
|
||||
for (let attempt = 0; attempt < maxAttempts; attempt++) {
|
||||
try {
|
||||
const response = await fetch(statusUrl, {
|
||||
method: 'GET',
|
||||
headers: {
|
||||
'Authorization': `Bearer ${apiKey}`
|
||||
}
|
||||
})
|
||||
|
||||
if (!response.ok) {
|
||||
const errorText = await response.text()
|
||||
console.error(`❌ ImageGen: Poll error (attempt ${attempt + 1}/${maxAttempts}):`, response.status, errorText)
|
||||
throw new Error(`Failed to check job status: ${response.status} - ${errorText}`)
|
||||
}
|
||||
|
||||
const data = await response.json() as RunPodJobResponse
|
||||
console.log(`🔄 ImageGen: Poll attempt ${attempt + 1}/${maxAttempts}, status:`, data.status)
|
||||
console.log(`📋 ImageGen: Full response data:`, JSON.stringify(data, null, 2))
|
||||
|
||||
if (data.status === 'COMPLETED') {
|
||||
console.log('✅ ImageGen: Job completed, processing output...')
|
||||
|
||||
// Extract image URL from various possible response formats
|
||||
let imageUrl = ''
|
||||
|
||||
// Check if output exists at all
|
||||
if (!data.output) {
|
||||
// Only retry 2-3 times, then proceed to check alternatives
|
||||
if (attempt < 3) {
|
||||
console.log(`⏳ ImageGen: COMPLETED but no output yet, waiting briefly (attempt ${attempt + 1}/3)...`)
|
||||
await new Promise(resolve => setTimeout(resolve, 500))
|
||||
continue
|
||||
}
|
||||
|
||||
// Try alternative ways to get the output - maybe it's at the top level
|
||||
console.log('⚠️ ImageGen: No output field found, checking for alternative response formats...')
|
||||
console.log('📋 ImageGen: All available fields:', Object.keys(data))
|
||||
|
||||
// Check if image data is at top level
|
||||
if (data.image) {
|
||||
imageUrl = data.image
|
||||
console.log('✅ ImageGen: Found image at top level')
|
||||
} else if (data.url) {
|
||||
imageUrl = data.url
|
||||
console.log('✅ ImageGen: Found url at top level')
|
||||
} else if (data.result) {
|
||||
// Some endpoints return result instead of output
|
||||
if (typeof data.result === 'string') {
|
||||
imageUrl = data.result
|
||||
} else if (data.result.image) {
|
||||
imageUrl = data.result.image
|
||||
} else if (data.result.url) {
|
||||
imageUrl = data.result.url
|
||||
}
|
||||
console.log('✅ ImageGen: Found result field')
|
||||
} else {
|
||||
// Last resort: try to fetch output via stream endpoint (some RunPod endpoints use this)
|
||||
console.log('⚠️ ImageGen: Trying alternative endpoint to retrieve output...')
|
||||
try {
|
||||
const streamUrl = `https://api.runpod.ai/v2/${endpointId}/stream/${jobId}`
|
||||
const streamResponse = await fetch(streamUrl, {
|
||||
method: 'GET',
|
||||
headers: {
|
||||
'Authorization': `Bearer ${apiKey}`
|
||||
}
|
||||
})
|
||||
|
||||
if (streamResponse.ok) {
|
||||
const streamData = await streamResponse.json() as RunPodJobResponse
|
||||
console.log('📥 ImageGen: Stream endpoint response:', JSON.stringify(streamData, null, 2))
|
||||
|
||||
if (streamData.output) {
|
||||
if (typeof streamData.output === 'string') {
|
||||
imageUrl = streamData.output
|
||||
} else if (streamData.output.image) {
|
||||
imageUrl = streamData.output.image
|
||||
} else if (streamData.output.url) {
|
||||
imageUrl = streamData.output.url
|
||||
} else if (Array.isArray(streamData.output.images) && streamData.output.images.length > 0) {
|
||||
const firstImage = streamData.output.images[0]
|
||||
if (firstImage.data) {
|
||||
imageUrl = firstImage.data.startsWith('data:') ? firstImage.data : `data:image/${firstImage.type || 'png'};base64,${firstImage.data}`
|
||||
} else if (firstImage.url) {
|
||||
imageUrl = firstImage.url
|
||||
}
|
||||
}
|
||||
|
||||
if (imageUrl) {
|
||||
console.log('✅ ImageGen: Found image URL via stream endpoint')
|
||||
return imageUrl
|
||||
}
|
||||
}
|
||||
}
|
||||
} catch (streamError) {
|
||||
console.log('⚠️ ImageGen: Stream endpoint not available or failed:', streamError)
|
||||
}
|
||||
|
||||
console.error('❌ ImageGen: Job completed but no output field in response after retries:', JSON.stringify(data, null, 2))
|
||||
throw new Error(
|
||||
'Job completed but no output data found.\n\n' +
|
||||
'Possible issues:\n' +
|
||||
'1. The RunPod endpoint handler may not be returning output correctly\n' +
|
||||
'2. Check the endpoint handler logs in RunPod console\n' +
|
||||
'3. Verify the handler returns: { output: { image: "url" } } or { output: "url" }\n' +
|
||||
'4. For ComfyUI workers, ensure output.images array is returned\n' +
|
||||
'5. The endpoint may need to be reconfigured\n\n' +
|
||||
'Response received: ' + JSON.stringify(data, null, 2)
|
||||
)
|
||||
}
|
||||
} else {
|
||||
// Extract image URL from various possible response formats
|
||||
if (typeof data.output === 'string') {
|
||||
imageUrl = data.output
|
||||
} else if (data.output?.image) {
|
||||
imageUrl = data.output.image
|
||||
} else if (data.output?.url) {
|
||||
imageUrl = data.output.url
|
||||
} else if (data.output?.output) {
|
||||
// Handle nested output structure
|
||||
if (typeof data.output.output === 'string') {
|
||||
imageUrl = data.output.output
|
||||
} else if (data.output.output?.image) {
|
||||
imageUrl = data.output.output.image
|
||||
} else if (data.output.output?.url) {
|
||||
imageUrl = data.output.output.url
|
||||
}
|
||||
} else if (Array.isArray(data.output) && data.output.length > 0) {
|
||||
// Handle array responses
|
||||
const firstItem = data.output[0]
|
||||
if (typeof firstItem === 'string') {
|
||||
imageUrl = firstItem
|
||||
} else if (firstItem.image) {
|
||||
imageUrl = firstItem.image
|
||||
} else if (firstItem.url) {
|
||||
imageUrl = firstItem.url
|
||||
}
|
||||
} else if (data.output?.result) {
|
||||
// Some formats nest result inside output
|
||||
if (typeof data.output.result === 'string') {
|
||||
imageUrl = data.output.result
|
||||
} else if (data.output.result?.image) {
|
||||
imageUrl = data.output.result.image
|
||||
} else if (data.output.result?.url) {
|
||||
imageUrl = data.output.result.url
|
||||
}
|
||||
} else if (Array.isArray(data.output?.images) && data.output.images.length > 0) {
|
||||
// ComfyUI worker format: { output: { images: [{ filename, type, data }] } }
|
||||
const firstImage = data.output.images[0]
|
||||
if (firstImage.data) {
|
||||
// Base64 encoded image
|
||||
if (firstImage.data.startsWith('data:image')) {
|
||||
imageUrl = firstImage.data
|
||||
} else if (firstImage.data.startsWith('http')) {
|
||||
imageUrl = firstImage.data
|
||||
} else {
|
||||
// Assume base64 without prefix
|
||||
imageUrl = `data:image/${firstImage.type || 'png'};base64,${firstImage.data}`
|
||||
}
|
||||
console.log('✅ ImageGen: Found image in ComfyUI format (images array)')
|
||||
} else if (firstImage.url) {
|
||||
imageUrl = firstImage.url
|
||||
console.log('✅ ImageGen: Found image URL in ComfyUI format')
|
||||
} else if (firstImage.filename) {
|
||||
// Try to construct URL from filename (may need endpoint-specific handling)
|
||||
console.log('⚠️ ImageGen: Found filename but no URL, filename:', firstImage.filename)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (!imageUrl || imageUrl.trim() === '') {
|
||||
console.error('❌ ImageGen: No image URL found in response:', JSON.stringify(data, null, 2))
|
||||
throw new Error(
|
||||
'Job completed but no image URL found in output.\n\n' +
|
||||
'Expected formats:\n' +
|
||||
'- { output: "https://..." }\n' +
|
||||
'- { output: { image: "https://..." } }\n' +
|
||||
'- { output: { url: "https://..." } }\n' +
|
||||
'- { output: ["https://..."] }\n\n' +
|
||||
'Received: ' + JSON.stringify(data, null, 2)
|
||||
)
|
||||
}
|
||||
|
||||
return imageUrl
|
||||
}
|
||||
|
||||
if (data.status === 'FAILED') {
|
||||
console.error('❌ ImageGen: Job failed:', data.error || 'Unknown error')
|
||||
throw new Error(`Job failed: ${data.error || 'Unknown error'}`)
|
||||
}
|
||||
|
||||
// Wait before next poll
|
||||
await new Promise(resolve => setTimeout(resolve, pollInterval))
|
||||
} catch (error) {
|
||||
// If we get COMPLETED status without output, don't retry - fail immediately
|
||||
const errorMessage = error instanceof Error ? error.message : String(error)
|
||||
if (errorMessage.includes('no output') || errorMessage.includes('no image URL')) {
|
||||
console.error('❌ ImageGen: Stopping polling due to missing output data')
|
||||
throw error
|
||||
}
|
||||
|
||||
// For other errors, retry up to maxAttempts
|
||||
if (attempt === maxAttempts - 1) {
|
||||
throw error
|
||||
}
|
||||
await new Promise(resolve => setTimeout(resolve, pollInterval))
|
||||
}
|
||||
}
|
||||
|
||||
throw new Error('Job polling timed out')
|
||||
}
|
||||
|
||||
export class ImageGenShape extends BaseBoxShapeUtil<IImageGen> {
|
||||
static override type = "ImageGen" as const
|
||||
|
||||
MIN_WIDTH = 300 as const
|
||||
MIN_HEIGHT = 300 as const
|
||||
DEFAULT_WIDTH = 400 as const
|
||||
DEFAULT_HEIGHT = 400 as const
|
||||
|
||||
getDefaultProps(): IImageGen["props"] {
|
||||
return {
|
||||
w: this.DEFAULT_WIDTH,
|
||||
h: this.DEFAULT_HEIGHT,
|
||||
prompt: "",
|
||||
imageUrl: null,
|
||||
isLoading: false,
|
||||
error: null,
|
||||
}
|
||||
}
|
||||
|
||||
getGeometry(shape: IImageGen): Geometry2d {
|
||||
return new Rectangle2d({
|
||||
width: shape.props.w,
|
||||
height: shape.props.h,
|
||||
isFilled: true,
|
||||
})
|
||||
}
|
||||
|
||||
component(shape: IImageGen) {
|
||||
const [isHovering, setIsHovering] = useState(false)
|
||||
const isSelected = this.editor.getSelectedShapeIds().includes(shape.id)
|
||||
|
||||
const generateImage = async (prompt: string) => {
|
||||
console.log("🎨 ImageGen: Generating image with prompt:", prompt)
|
||||
|
||||
// Clear any previous errors
|
||||
this.editor.updateShape<IImageGen>({
|
||||
id: shape.id,
|
||||
type: "ImageGen",
|
||||
props: {
|
||||
error: null,
|
||||
isLoading: true,
|
||||
imageUrl: null
|
||||
},
|
||||
})
|
||||
|
||||
try {
|
||||
// Get RunPod configuration
|
||||
const runpodConfig = getRunPodConfig()
|
||||
const endpointId = shape.props.endpointId || runpodConfig?.endpointId || "tzf1j3sc3zufsy"
|
||||
const apiKey = runpodConfig?.apiKey
|
||||
|
||||
// Mock API mode: Return placeholder image without calling RunPod
|
||||
if (USE_MOCK_API) {
|
||||
console.log("🎭 ImageGen: Using MOCK API mode (no real RunPod call)")
|
||||
console.log("🎨 ImageGen: Mock prompt:", prompt)
|
||||
|
||||
// Simulate API delay
|
||||
await new Promise(resolve => setTimeout(resolve, 1500))
|
||||
|
||||
// Use a placeholder image service
|
||||
const mockImageUrl = `https://via.placeholder.com/512x512/4F46E5/FFFFFF?text=${encodeURIComponent(prompt.substring(0, 30))}`
|
||||
|
||||
console.log("✅ ImageGen: Mock image generated:", mockImageUrl)
|
||||
|
||||
this.editor.updateShape<IImageGen>({
|
||||
id: shape.id,
|
||||
type: "ImageGen",
|
||||
props: {
|
||||
imageUrl: mockImageUrl,
|
||||
isLoading: false,
|
||||
error: null
|
||||
},
|
||||
})
|
||||
|
||||
return
|
||||
}
|
||||
|
||||
// Real API mode: Use RunPod
|
||||
if (!apiKey) {
|
||||
throw new Error("RunPod API key not configured. Please set VITE_RUNPOD_API_KEY environment variable.")
|
||||
}
|
||||
|
||||
const url = `https://api.runpod.ai/v2/${endpointId}/run`
|
||||
|
||||
console.log("📤 ImageGen: Sending request to:", url)
|
||||
|
||||
const response = await fetch(url, {
|
||||
method: "POST",
|
||||
headers: {
|
||||
"Content-Type": "application/json",
|
||||
"Authorization": `Bearer ${apiKey}`
|
||||
},
|
||||
body: JSON.stringify({
|
||||
input: {
|
||||
prompt: prompt
|
||||
}
|
||||
})
|
||||
})
|
||||
|
||||
if (!response.ok) {
|
||||
const errorText = await response.text()
|
||||
console.error("❌ ImageGen: Error response:", errorText)
|
||||
throw new Error(`HTTP error! status: ${response.status} - ${errorText}`)
|
||||
}
|
||||
|
||||
const data = await response.json() as RunPodJobResponse
|
||||
console.log("📥 ImageGen: Response data:", JSON.stringify(data, null, 2))
|
||||
|
||||
// Handle async job pattern (RunPod often returns job IDs)
|
||||
if (data.id && (data.status === 'IN_QUEUE' || data.status === 'IN_PROGRESS' || data.status === 'STARTING')) {
|
||||
console.log("⏳ ImageGen: Job queued/in progress, polling job ID:", data.id)
|
||||
const imageUrl = await pollRunPodJob(data.id, apiKey, endpointId)
|
||||
console.log("✅ ImageGen: Job completed, image URL:", imageUrl)
|
||||
|
||||
this.editor.updateShape<IImageGen>({
|
||||
id: shape.id,
|
||||
type: "ImageGen",
|
||||
props: {
|
||||
imageUrl: imageUrl,
|
||||
isLoading: false,
|
||||
error: null
|
||||
},
|
||||
})
|
||||
} else if (data.output) {
|
||||
// Handle direct response
|
||||
let imageUrl = ''
|
||||
if (typeof data.output === 'string') {
|
||||
imageUrl = data.output
|
||||
} else if (data.output.image) {
|
||||
imageUrl = data.output.image
|
||||
} else if (data.output.url) {
|
||||
imageUrl = data.output.url
|
||||
} else if (Array.isArray(data.output) && data.output.length > 0) {
|
||||
const firstItem = data.output[0]
|
||||
if (typeof firstItem === 'string') {
|
||||
imageUrl = firstItem
|
||||
} else if (firstItem.image) {
|
||||
imageUrl = firstItem.image
|
||||
} else if (firstItem.url) {
|
||||
imageUrl = firstItem.url
|
||||
}
|
||||
}
|
||||
|
||||
if (imageUrl) {
|
||||
this.editor.updateShape<IImageGen>({
|
||||
id: shape.id,
|
||||
type: "ImageGen",
|
||||
props: {
|
||||
imageUrl: imageUrl,
|
||||
isLoading: false,
|
||||
error: null
|
||||
},
|
||||
})
|
||||
} else {
|
||||
throw new Error("No image URL found in response")
|
||||
}
|
||||
} else if (data.error) {
|
||||
throw new Error(`RunPod API error: ${data.error}`)
|
||||
} else {
|
||||
throw new Error("No valid response from RunPod API")
|
||||
}
|
||||
} catch (error) {
|
||||
const errorMessage = error instanceof Error ? error.message : String(error)
|
||||
console.error("❌ ImageGen: Error:", errorMessage)
|
||||
|
||||
let userFriendlyError = ''
|
||||
|
||||
if (errorMessage.includes('API key not configured')) {
|
||||
userFriendlyError = '❌ RunPod API key not configured. Please set VITE_RUNPOD_API_KEY environment variable.'
|
||||
} else if (errorMessage.includes('401') || errorMessage.includes('403') || errorMessage.includes('Unauthorized')) {
|
||||
userFriendlyError = '❌ API key authentication failed. Please check your RunPod API key.'
|
||||
} else if (errorMessage.includes('404')) {
|
||||
userFriendlyError = '❌ Endpoint not found. Please check your endpoint ID.'
|
||||
} else if (errorMessage.includes('no output data found') || errorMessage.includes('no image URL found')) {
|
||||
// For multi-line error messages, show a concise version in the UI
|
||||
// The full details are already in the console
|
||||
userFriendlyError = '❌ Image generation completed but no image data was returned.\n\n' +
|
||||
'This usually means the RunPod endpoint handler is not configured correctly.\n\n' +
|
||||
'Please check:\n' +
|
||||
'1. RunPod endpoint handler logs\n' +
|
||||
'2. Handler returns: { output: { image: "url" } }\n' +
|
||||
'3. See browser console for full details'
|
||||
} else {
|
||||
// Truncate very long error messages for UI display
|
||||
const maxLength = 500
|
||||
if (errorMessage.length > maxLength) {
|
||||
userFriendlyError = `❌ Error: ${errorMessage.substring(0, maxLength)}...\n\n(Full error in console)`
|
||||
} else {
|
||||
userFriendlyError = `❌ Error: ${errorMessage}`
|
||||
}
|
||||
}
|
||||
|
||||
this.editor.updateShape<IImageGen>({
|
||||
id: shape.id,
|
||||
type: "ImageGen",
|
||||
props: {
|
||||
isLoading: false,
|
||||
error: userFriendlyError
|
||||
},
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
const handleGenerate = () => {
|
||||
if (shape.props.prompt.trim() && !shape.props.isLoading) {
|
||||
generateImage(shape.props.prompt)
|
||||
this.editor.updateShape<IImageGen>({
|
||||
id: shape.id,
|
||||
type: "ImageGen",
|
||||
props: { prompt: "" },
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
return (
|
||||
<HTMLContainer
|
||||
style={{
|
||||
borderRadius: 6,
|
||||
border: "1px solid lightgrey",
|
||||
padding: 8,
|
||||
height: shape.props.h,
|
||||
width: shape.props.w,
|
||||
pointerEvents: isSelected || isHovering ? "all" : "none",
|
||||
backgroundColor: "#ffffff",
|
||||
overflow: "hidden",
|
||||
display: "flex",
|
||||
flexDirection: "column",
|
||||
gap: 8,
|
||||
}}
|
||||
onPointerEnter={() => setIsHovering(true)}
|
||||
onPointerLeave={() => setIsHovering(false)}
|
||||
>
|
||||
{/* Error Display */}
|
||||
{shape.props.error && (
|
||||
<div
|
||||
style={{
|
||||
padding: "12px 16px",
|
||||
backgroundColor: "#fee",
|
||||
border: "1px solid #fcc",
|
||||
borderRadius: "8px",
|
||||
color: "#c33",
|
||||
fontSize: "13px",
|
||||
display: "flex",
|
||||
alignItems: "flex-start",
|
||||
gap: "8px",
|
||||
whiteSpace: "pre-wrap",
|
||||
wordBreak: "break-word",
|
||||
}}
|
||||
>
|
||||
<span style={{ fontSize: "18px", flexShrink: 0 }}>⚠️</span>
|
||||
<span style={{ flex: 1, lineHeight: "1.5" }}>{shape.props.error}</span>
|
||||
<button
|
||||
onClick={() => {
|
||||
this.editor.updateShape<IImageGen>({
|
||||
id: shape.id,
|
||||
type: "ImageGen",
|
||||
props: { error: null },
|
||||
})
|
||||
}}
|
||||
style={{
|
||||
padding: "4px 8px",
|
||||
backgroundColor: "#fcc",
|
||||
border: "1px solid #c99",
|
||||
borderRadius: "4px",
|
||||
cursor: "pointer",
|
||||
fontSize: "11px",
|
||||
flexShrink: 0,
|
||||
}}
|
||||
>
|
||||
Dismiss
|
||||
</button>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* Image Display */}
|
||||
{shape.props.imageUrl && !shape.props.isLoading && (
|
||||
<div
|
||||
style={{
|
||||
flex: 1,
|
||||
display: "flex",
|
||||
alignItems: "center",
|
||||
justifyContent: "center",
|
||||
backgroundColor: "#f5f5f5",
|
||||
borderRadius: "4px",
|
||||
overflow: "hidden",
|
||||
minHeight: 0,
|
||||
}}
|
||||
>
|
||||
<img
|
||||
src={shape.props.imageUrl}
|
||||
alt={shape.props.prompt || "Generated image"}
|
||||
style={{
|
||||
maxWidth: "100%",
|
||||
maxHeight: "100%",
|
||||
objectFit: "contain",
|
||||
}}
|
||||
onError={(_e) => {
|
||||
console.error("❌ ImageGen: Failed to load image:", shape.props.imageUrl)
|
||||
this.editor.updateShape<IImageGen>({
|
||||
id: shape.id,
|
||||
type: "ImageGen",
|
||||
props: {
|
||||
error: "Failed to load generated image",
|
||||
imageUrl: null
|
||||
},
|
||||
})
|
||||
}}
|
||||
/>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* Loading State */}
|
||||
{shape.props.isLoading && (
|
||||
<div
|
||||
style={{
|
||||
flex: 1,
|
||||
display: "flex",
|
||||
flexDirection: "column",
|
||||
alignItems: "center",
|
||||
justifyContent: "center",
|
||||
backgroundColor: "#f5f5f5",
|
||||
borderRadius: "4px",
|
||||
gap: 12,
|
||||
}}
|
||||
>
|
||||
<div
|
||||
style={{
|
||||
width: 40,
|
||||
height: 40,
|
||||
border: "4px solid #f3f3f3",
|
||||
borderTop: "4px solid #007AFF",
|
||||
borderRadius: "50%",
|
||||
animation: "spin 1s linear infinite",
|
||||
}}
|
||||
/>
|
||||
<span style={{ color: "#666", fontSize: "14px" }}>
|
||||
Generating image...
|
||||
</span>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* Empty State */}
|
||||
{!shape.props.imageUrl && !shape.props.isLoading && (
|
||||
<div
|
||||
style={{
|
||||
flex: 1,
|
||||
display: "flex",
|
||||
alignItems: "center",
|
||||
justifyContent: "center",
|
||||
backgroundColor: "#f5f5f5",
|
||||
borderRadius: "4px",
|
||||
color: "#999",
|
||||
fontSize: "14px",
|
||||
}}
|
||||
>
|
||||
Generated image will appear here
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* Input Section */}
|
||||
<div
|
||||
style={{
|
||||
display: "flex",
|
||||
gap: 8,
|
||||
pointerEvents: isSelected || isHovering ? "all" : "none",
|
||||
}}
|
||||
>
|
||||
<input
|
||||
style={{
|
||||
flex: 1,
|
||||
height: "36px",
|
||||
backgroundColor: "rgba(0, 0, 0, 0.05)",
|
||||
border: "1px solid rgba(0, 0, 0, 0.1)",
|
||||
borderRadius: "4px",
|
||||
fontSize: 14,
|
||||
padding: "0 8px",
|
||||
}}
|
||||
type="text"
|
||||
placeholder="Enter image prompt..."
|
||||
value={shape.props.prompt}
|
||||
onChange={(e) => {
|
||||
this.editor.updateShape<IImageGen>({
|
||||
id: shape.id,
|
||||
type: "ImageGen",
|
||||
props: { prompt: e.target.value },
|
||||
})
|
||||
}}
|
||||
onKeyDown={(e) => {
|
||||
e.stopPropagation()
|
||||
if (e.key === 'Enter' && !e.shiftKey) {
|
||||
e.preventDefault()
|
||||
if (shape.props.prompt.trim() && !shape.props.isLoading) {
|
||||
handleGenerate()
|
||||
}
|
||||
}
|
||||
}}
|
||||
onPointerDown={(e) => {
|
||||
e.stopPropagation()
|
||||
}}
|
||||
onClick={(e) => {
|
||||
e.stopPropagation()
|
||||
}}
|
||||
disabled={shape.props.isLoading}
|
||||
/>
|
||||
<button
|
||||
style={{
|
||||
height: "36px",
|
||||
padding: "0 16px",
|
||||
pointerEvents: "all",
|
||||
cursor: shape.props.prompt.trim() && !shape.props.isLoading ? "pointer" : "not-allowed",
|
||||
backgroundColor: shape.props.prompt.trim() && !shape.props.isLoading ? "#007AFF" : "#ccc",
|
||||
color: "white",
|
||||
border: "none",
|
||||
borderRadius: "4px",
|
||||
fontWeight: "500",
|
||||
fontSize: "14px",
|
||||
opacity: shape.props.prompt.trim() && !shape.props.isLoading ? 1 : 0.6,
|
||||
}}
|
||||
onPointerDown={(e) => {
|
||||
e.stopPropagation()
|
||||
e.preventDefault()
|
||||
if (shape.props.prompt.trim() && !shape.props.isLoading) {
|
||||
handleGenerate()
|
||||
}
|
||||
}}
|
||||
onClick={(e) => {
|
||||
e.preventDefault()
|
||||
e.stopPropagation()
|
||||
if (shape.props.prompt.trim() && !shape.props.isLoading) {
|
||||
handleGenerate()
|
||||
}
|
||||
}}
|
||||
disabled={shape.props.isLoading || !shape.props.prompt.trim()}
|
||||
>
|
||||
Generate
|
||||
</button>
|
||||
</div>
|
||||
|
||||
{/* Add CSS for spinner animation */}
|
||||
<style>{`
|
||||
@keyframes spin {
|
||||
0% { transform: rotate(0deg); }
|
||||
100% { transform: rotate(360deg); }
|
||||
}
|
||||
`}</style>
|
||||
</HTMLContainer>
|
||||
)
|
||||
}
|
||||
|
||||
override indicator(shape: IImageGen) {
|
||||
return (
|
||||
<rect
|
||||
width={shape.props.w}
|
||||
height={shape.props.h}
|
||||
rx={6}
|
||||
/>
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -0,0 +1,468 @@
|
|||
import {
|
||||
BaseBoxShapeUtil,
|
||||
Geometry2d,
|
||||
HTMLContainer,
|
||||
Rectangle2d,
|
||||
TLBaseShape,
|
||||
} from "tldraw"
|
||||
import React, { useState } from "react"
|
||||
import { getRunPodVideoConfig } from "@/lib/clientConfig"
|
||||
import { StandardizedToolWrapper } from "@/components/StandardizedToolWrapper"
|
||||
|
||||
// Type for RunPod job response
|
||||
interface RunPodJobResponse {
|
||||
id?: string
|
||||
status?: 'IN_QUEUE' | 'IN_PROGRESS' | 'STARTING' | 'COMPLETED' | 'FAILED' | 'CANCELLED'
|
||||
output?: {
|
||||
video_url?: string
|
||||
url?: string
|
||||
[key: string]: any
|
||||
} | string
|
||||
error?: string
|
||||
}
|
||||
|
||||
type IVideoGen = TLBaseShape<
|
||||
"VideoGen",
|
||||
{
|
||||
w: number
|
||||
h: number
|
||||
prompt: string
|
||||
videoUrl: string | null
|
||||
isLoading: boolean
|
||||
error: string | null
|
||||
duration: number // seconds
|
||||
model: string
|
||||
tags: string[]
|
||||
}
|
||||
>
|
||||
|
||||
export class VideoGenShape extends BaseBoxShapeUtil<IVideoGen> {
|
||||
static override type = "VideoGen" as const
|
||||
|
||||
// Video generation theme color: Purple
|
||||
static readonly PRIMARY_COLOR = "#8B5CF6"
|
||||
|
||||
getDefaultProps(): IVideoGen['props'] {
|
||||
return {
|
||||
w: 500,
|
||||
h: 450,
|
||||
prompt: "",
|
||||
videoUrl: null,
|
||||
isLoading: false,
|
||||
error: null,
|
||||
duration: 3,
|
||||
model: "wan2.1-i2v",
|
||||
tags: ['video', 'ai-generated']
|
||||
}
|
||||
}
|
||||
|
||||
getGeometry(shape: IVideoGen): Geometry2d {
|
||||
return new Rectangle2d({
|
||||
width: shape.props.w,
|
||||
height: shape.props.h,
|
||||
isFilled: true,
|
||||
})
|
||||
}
|
||||
|
||||
component(shape: IVideoGen) {
|
||||
const [prompt, setPrompt] = useState(shape.props.prompt)
|
||||
const [isGenerating, setIsGenerating] = useState(shape.props.isLoading)
|
||||
const [error, setError] = useState<string | null>(shape.props.error)
|
||||
const [videoUrl, setVideoUrl] = useState<string | null>(shape.props.videoUrl)
|
||||
const [isMinimized, setIsMinimized] = useState(false)
|
||||
const isSelected = this.editor.getSelectedShapeIds().includes(shape.id)
|
||||
|
||||
const handleGenerate = async () => {
|
||||
if (!prompt.trim()) {
|
||||
setError("Please enter a prompt")
|
||||
return
|
||||
}
|
||||
|
||||
// Check RunPod config
|
||||
const runpodConfig = getRunPodVideoConfig()
|
||||
if (!runpodConfig) {
|
||||
setError("RunPod video endpoint not configured. Please set VITE_RUNPOD_API_KEY and VITE_RUNPOD_VIDEO_ENDPOINT_ID in your .env file.")
|
||||
return
|
||||
}
|
||||
|
||||
console.log('🎬 VideoGen: Starting generation with prompt:', prompt)
|
||||
setIsGenerating(true)
|
||||
setError(null)
|
||||
|
||||
// Update shape to show loading state
|
||||
this.editor.updateShape({
|
||||
id: shape.id,
|
||||
type: shape.type,
|
||||
props: { ...shape.props, isLoading: true, error: null }
|
||||
})
|
||||
|
||||
try {
|
||||
const { apiKey, endpointId } = runpodConfig
|
||||
|
||||
// Submit job to RunPod
|
||||
console.log('🎬 VideoGen: Submitting to RunPod endpoint:', endpointId)
|
||||
const runUrl = `https://api.runpod.ai/v2/${endpointId}/run`
|
||||
|
||||
const response = await fetch(runUrl, {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Authorization': `Bearer ${apiKey}`,
|
||||
'Content-Type': 'application/json'
|
||||
},
|
||||
body: JSON.stringify({
|
||||
input: {
|
||||
prompt: prompt,
|
||||
duration: shape.props.duration,
|
||||
model: shape.props.model
|
||||
}
|
||||
})
|
||||
})
|
||||
|
||||
if (!response.ok) {
|
||||
const errorText = await response.text()
|
||||
throw new Error(`RunPod API error: ${response.status} - ${errorText}`)
|
||||
}
|
||||
|
||||
const jobData = await response.json() as RunPodJobResponse
|
||||
console.log('🎬 VideoGen: Job submitted:', jobData.id)
|
||||
|
||||
if (!jobData.id) {
|
||||
throw new Error('No job ID returned from RunPod')
|
||||
}
|
||||
|
||||
// Poll for completion
|
||||
const statusUrl = `https://api.runpod.ai/v2/${endpointId}/status/${jobData.id}`
|
||||
let attempts = 0
|
||||
const maxAttempts = 120 // 4 minutes with 2s intervals (video can take a while)
|
||||
|
||||
while (attempts < maxAttempts) {
|
||||
await new Promise(resolve => setTimeout(resolve, 2000))
|
||||
attempts++
|
||||
|
||||
const statusResponse = await fetch(statusUrl, {
|
||||
headers: { 'Authorization': `Bearer ${apiKey}` }
|
||||
})
|
||||
|
||||
if (!statusResponse.ok) {
|
||||
console.warn(`🎬 VideoGen: Poll error (attempt ${attempts}):`, statusResponse.status)
|
||||
continue
|
||||
}
|
||||
|
||||
const statusData = await statusResponse.json() as RunPodJobResponse
|
||||
console.log(`🎬 VideoGen: Poll ${attempts}/${maxAttempts}, status:`, statusData.status)
|
||||
|
||||
if (statusData.status === 'COMPLETED') {
|
||||
// Extract video URL from output
|
||||
let url = ''
|
||||
if (typeof statusData.output === 'string') {
|
||||
url = statusData.output
|
||||
} else if (statusData.output?.video_url) {
|
||||
url = statusData.output.video_url
|
||||
} else if (statusData.output?.url) {
|
||||
url = statusData.output.url
|
||||
}
|
||||
|
||||
if (url) {
|
||||
console.log('✅ VideoGen: Generation complete, URL:', url)
|
||||
setVideoUrl(url)
|
||||
setIsGenerating(false)
|
||||
|
||||
this.editor.updateShape({
|
||||
id: shape.id,
|
||||
type: shape.type,
|
||||
props: {
|
||||
...shape.props,
|
||||
videoUrl: url,
|
||||
isLoading: false,
|
||||
prompt: prompt
|
||||
}
|
||||
})
|
||||
return
|
||||
} else {
|
||||
console.log('⚠️ VideoGen: Completed but no video URL in output:', statusData.output)
|
||||
throw new Error('Video generation completed but no video URL returned')
|
||||
}
|
||||
} else if (statusData.status === 'FAILED') {
|
||||
throw new Error(statusData.error || 'Video generation failed')
|
||||
} else if (statusData.status === 'CANCELLED') {
|
||||
throw new Error('Video generation was cancelled')
|
||||
}
|
||||
}
|
||||
|
||||
throw new Error('Video generation timed out after 4 minutes')
|
||||
} catch (error: any) {
|
||||
const errorMessage = error.message || 'Unknown error during video generation'
|
||||
console.error('❌ VideoGen: Generation error:', errorMessage)
|
||||
setError(errorMessage)
|
||||
setIsGenerating(false)
|
||||
|
||||
this.editor.updateShape({
|
||||
id: shape.id,
|
||||
type: shape.type,
|
||||
props: { ...shape.props, isLoading: false, error: errorMessage }
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
const handleClose = () => {
|
||||
this.editor.deleteShape(shape.id)
|
||||
}
|
||||
|
||||
const handleMinimize = () => {
|
||||
setIsMinimized(!isMinimized)
|
||||
}
|
||||
|
||||
const handleTagsChange = (newTags: string[]) => {
|
||||
this.editor.updateShape({
|
||||
id: shape.id,
|
||||
type: shape.type,
|
||||
props: { ...shape.props, tags: newTags }
|
||||
})
|
||||
}
|
||||
|
||||
return (
|
||||
<HTMLContainer id={shape.id}>
|
||||
<StandardizedToolWrapper
|
||||
title="🎬 Video Generator (Wan2.1)"
|
||||
primaryColor={VideoGenShape.PRIMARY_COLOR}
|
||||
isSelected={isSelected}
|
||||
width={shape.props.w}
|
||||
height={shape.props.h}
|
||||
onClose={handleClose}
|
||||
onMinimize={handleMinimize}
|
||||
isMinimized={isMinimized}
|
||||
editor={this.editor}
|
||||
shapeId={shape.id}
|
||||
tags={shape.props.tags}
|
||||
onTagsChange={handleTagsChange}
|
||||
tagsEditable={true}
|
||||
headerContent={
|
||||
isGenerating ? (
|
||||
<span style={{ display: 'flex', alignItems: 'center', gap: '8px' }}>
|
||||
🎬 Video Generator
|
||||
<span style={{
|
||||
marginLeft: 'auto',
|
||||
fontSize: '11px',
|
||||
color: VideoGenShape.PRIMARY_COLOR,
|
||||
animation: 'pulse 1.5s ease-in-out infinite'
|
||||
}}>
|
||||
Generating...
|
||||
</span>
|
||||
</span>
|
||||
) : undefined
|
||||
}
|
||||
>
|
||||
<div style={{
|
||||
flex: 1,
|
||||
display: 'flex',
|
||||
flexDirection: 'column',
|
||||
padding: '16px',
|
||||
gap: '12px',
|
||||
overflow: 'auto',
|
||||
backgroundColor: '#fafafa'
|
||||
}}>
|
||||
{!videoUrl && (
|
||||
<>
|
||||
<div style={{ display: 'flex', flexDirection: 'column', gap: '8px' }}>
|
||||
<label style={{ color: '#555', fontSize: '12px', fontWeight: '600' }}>
|
||||
Video Prompt
|
||||
</label>
|
||||
<textarea
|
||||
value={prompt}
|
||||
onChange={(e) => setPrompt(e.target.value)}
|
||||
placeholder="Describe the video you want to generate..."
|
||||
disabled={isGenerating}
|
||||
onPointerDown={(e) => e.stopPropagation()}
|
||||
style={{
|
||||
width: '100%',
|
||||
minHeight: '80px',
|
||||
padding: '10px',
|
||||
backgroundColor: '#fff',
|
||||
color: '#333',
|
||||
border: '1px solid #ddd',
|
||||
borderRadius: '6px',
|
||||
fontSize: '13px',
|
||||
fontFamily: 'inherit',
|
||||
resize: 'vertical',
|
||||
boxSizing: 'border-box'
|
||||
}}
|
||||
/>
|
||||
</div>
|
||||
|
||||
<div style={{ display: 'flex', gap: '12px', alignItems: 'flex-end' }}>
|
||||
<div style={{ flex: 1 }}>
|
||||
<label style={{ color: '#555', fontSize: '11px', display: 'block', marginBottom: '4px', fontWeight: '500' }}>
|
||||
Duration (seconds)
|
||||
</label>
|
||||
<input
|
||||
type="number"
|
||||
min="1"
|
||||
max="10"
|
||||
value={shape.props.duration}
|
||||
onChange={(e) => {
|
||||
this.editor.updateShape({
|
||||
id: shape.id,
|
||||
type: shape.type,
|
||||
props: { ...shape.props, duration: parseInt(e.target.value) || 3 }
|
||||
})
|
||||
}}
|
||||
disabled={isGenerating}
|
||||
onPointerDown={(e) => e.stopPropagation()}
|
||||
style={{
|
||||
width: '100%',
|
||||
padding: '8px',
|
||||
backgroundColor: '#fff',
|
||||
color: '#333',
|
||||
border: '1px solid #ddd',
|
||||
borderRadius: '6px',
|
||||
fontSize: '13px',
|
||||
boxSizing: 'border-box'
|
||||
}}
|
||||
/>
|
||||
</div>
|
||||
|
||||
<button
|
||||
onClick={handleGenerate}
|
||||
disabled={isGenerating || !prompt.trim()}
|
||||
onPointerDown={(e) => e.stopPropagation()}
|
||||
style={{
|
||||
padding: '8px 20px',
|
||||
backgroundColor: isGenerating ? '#ccc' : VideoGenShape.PRIMARY_COLOR,
|
||||
color: '#fff',
|
||||
border: 'none',
|
||||
borderRadius: '6px',
|
||||
fontSize: '13px',
|
||||
fontWeight: '600',
|
||||
cursor: isGenerating ? 'not-allowed' : 'pointer',
|
||||
transition: 'all 0.2s',
|
||||
whiteSpace: 'nowrap',
|
||||
opacity: isGenerating || !prompt.trim() ? 0.6 : 1
|
||||
}}
|
||||
>
|
||||
{isGenerating ? 'Generating...' : 'Generate Video'}
|
||||
</button>
|
||||
</div>
|
||||
|
||||
{error && (
|
||||
<div style={{
|
||||
padding: '12px',
|
||||
backgroundColor: '#fee',
|
||||
border: '1px solid #fcc',
|
||||
color: '#c33',
|
||||
borderRadius: '6px',
|
||||
fontSize: '12px',
|
||||
lineHeight: '1.4'
|
||||
}}>
|
||||
<strong>Error:</strong> {error}
|
||||
</div>
|
||||
)}
|
||||
|
||||
<div style={{
|
||||
marginTop: 'auto',
|
||||
padding: '12px',
|
||||
backgroundColor: '#f0f0f0',
|
||||
borderRadius: '6px',
|
||||
fontSize: '11px',
|
||||
color: '#666',
|
||||
lineHeight: '1.5'
|
||||
}}>
|
||||
<div><strong>Note:</strong> Video generation uses RunPod GPU</div>
|
||||
<div>Cost: ~$0.50 per video | Processing: 30-90 seconds</div>
|
||||
</div>
|
||||
</>
|
||||
)}
|
||||
|
||||
{videoUrl && (
|
||||
<>
|
||||
<video
|
||||
src={videoUrl}
|
||||
controls
|
||||
autoPlay
|
||||
loop
|
||||
onPointerDown={(e) => e.stopPropagation()}
|
||||
style={{
|
||||
width: '100%',
|
||||
maxHeight: '280px',
|
||||
borderRadius: '6px',
|
||||
backgroundColor: '#000'
|
||||
}}
|
||||
/>
|
||||
|
||||
<div style={{
|
||||
padding: '10px',
|
||||
backgroundColor: '#f0f0f0',
|
||||
borderRadius: '6px',
|
||||
fontSize: '11px',
|
||||
color: '#555',
|
||||
wordBreak: 'break-word'
|
||||
}}>
|
||||
<strong>Prompt:</strong> {shape.props.prompt || prompt}
|
||||
</div>
|
||||
|
||||
<div style={{ display: 'flex', gap: '8px' }}>
|
||||
<button
|
||||
onClick={() => {
|
||||
setVideoUrl(null)
|
||||
setPrompt("")
|
||||
this.editor.updateShape({
|
||||
id: shape.id,
|
||||
type: shape.type,
|
||||
props: { ...shape.props, videoUrl: null, prompt: "" }
|
||||
})
|
||||
}}
|
||||
onPointerDown={(e) => e.stopPropagation()}
|
||||
style={{
|
||||
flex: 1,
|
||||
padding: '10px',
|
||||
backgroundColor: '#e0e0e0',
|
||||
color: '#333',
|
||||
border: 'none',
|
||||
borderRadius: '6px',
|
||||
fontSize: '12px',
|
||||
fontWeight: '500',
|
||||
cursor: 'pointer'
|
||||
}}
|
||||
>
|
||||
New Video
|
||||
</button>
|
||||
|
||||
<a
|
||||
href={videoUrl}
|
||||
download="generated-video.mp4"
|
||||
onPointerDown={(e) => e.stopPropagation()}
|
||||
style={{
|
||||
flex: 1,
|
||||
padding: '10px',
|
||||
backgroundColor: VideoGenShape.PRIMARY_COLOR,
|
||||
color: '#fff',
|
||||
border: 'none',
|
||||
borderRadius: '6px',
|
||||
fontSize: '12px',
|
||||
fontWeight: '600',
|
||||
textAlign: 'center',
|
||||
textDecoration: 'none',
|
||||
cursor: 'pointer'
|
||||
}}
|
||||
>
|
||||
Download
|
||||
</a>
|
||||
</div>
|
||||
</>
|
||||
)}
|
||||
</div>
|
||||
|
||||
<style>{`
|
||||
@keyframes pulse {
|
||||
0%, 100% { opacity: 1; }
|
||||
50% { opacity: 0.5; }
|
||||
}
|
||||
`}</style>
|
||||
</StandardizedToolWrapper>
|
||||
</HTMLContainer>
|
||||
)
|
||||
}
|
||||
|
||||
indicator(shape: IVideoGen) {
|
||||
return <rect width={shape.props.w} height={shape.props.h} rx={8} />
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
import { BaseBoxShapeTool, TLEventHandlers } from 'tldraw'
|
||||
|
||||
export class ImageGenTool extends BaseBoxShapeTool {
|
||||
static override id = 'ImageGen'
|
||||
static override initial = 'idle'
|
||||
override shapeType = 'ImageGen'
|
||||
|
||||
override onComplete: TLEventHandlers["onComplete"] = () => {
|
||||
console.log('🎨 ImageGenTool: Shape creation completed')
|
||||
this.editor.setCurrentTool('select')
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,12 @@
|
|||
import { BaseBoxShapeTool, TLEventHandlers } from 'tldraw'
|
||||
|
||||
export class VideoGenTool extends BaseBoxShapeTool {
|
||||
static override id = 'VideoGen'
|
||||
static override initial = 'idle'
|
||||
override shapeType = 'VideoGen'
|
||||
|
||||
override onComplete: TLEventHandlers["onComplete"] = () => {
|
||||
console.log('🎬 VideoGenTool: Shape creation completed')
|
||||
this.editor.setCurrentTool('select')
|
||||
}
|
||||
}
|
||||
|
|
@ -238,6 +238,7 @@ export function CustomContextMenu(props: TLUiContextMenuProps) {
|
|||
<TldrawUiMenuItem {...tools.Transcription} disabled={hasSelection} />
|
||||
<TldrawUiMenuItem {...tools.FathomMeetings} disabled={hasSelection} />
|
||||
<TldrawUiMenuItem {...tools.Holon} disabled={hasSelection} />
|
||||
<TldrawUiMenuItem {...tools.ImageGen} disabled={hasSelection} />
|
||||
</TldrawUiMenuGroup>
|
||||
|
||||
{/* Collections Group */}
|
||||
|
|
|
|||
|
|
@ -29,7 +29,7 @@ export function CustomMainMenu() {
|
|||
const validateAndNormalizeShapeType = (shape: any): string => {
|
||||
if (!shape || !shape.type) return 'text'
|
||||
|
||||
const validCustomShapes = ['ObsNote', 'VideoChat', 'Transcription', 'Prompt', 'ChatBox', 'Embed', 'Markdown', 'MycrozineTemplate', 'Slide', 'Holon', 'ObsidianBrowser', 'HolonBrowser', 'FathomMeetingsBrowser']
|
||||
const validCustomShapes = ['ObsNote', 'VideoChat', 'Transcription', 'Prompt', 'ChatBox', 'Embed', 'Markdown', 'MycrozineTemplate', 'Slide', 'Holon', 'ObsidianBrowser', 'HolonBrowser', 'FathomMeetingsBrowser', 'LocationShare', 'ImageGen']
|
||||
const validDefaultShapes = ['arrow', 'bookmark', 'draw', 'embed', 'frame', 'geo', 'group', 'highlight', 'image', 'line', 'note', 'text', 'video']
|
||||
const allValidShapes = [...validCustomShapes, ...validDefaultShapes]
|
||||
|
||||
|
|
@ -64,23 +64,9 @@ export function CustomMainMenu() {
|
|||
const validateShapeGeometry = (shape: any): boolean => {
|
||||
if (!shape || !shape.id) return false
|
||||
|
||||
// CRITICAL: Only validate that x/y are valid numbers if they exist
|
||||
// DO NOT set default values here - let fixIncompleteShape handle that
|
||||
// This preserves original coordinates and prevents coordinate collapse
|
||||
if (shape.x !== undefined && shape.x !== null) {
|
||||
if (typeof shape.x !== 'number' || isNaN(shape.x) || !isFinite(shape.x)) {
|
||||
console.warn(`⚠️ Invalid x coordinate for shape ${shape.id}:`, shape.x)
|
||||
shape.x = undefined // Mark as invalid so fixIncompleteShape can handle it
|
||||
}
|
||||
}
|
||||
if (shape.y !== undefined && shape.y !== null) {
|
||||
if (typeof shape.y !== 'number' || isNaN(shape.y) || !isFinite(shape.y)) {
|
||||
console.warn(`⚠️ Invalid y coordinate for shape ${shape.id}:`, shape.y)
|
||||
shape.y = undefined // Mark as invalid so fixIncompleteShape can handle it
|
||||
}
|
||||
}
|
||||
|
||||
// Validate rotation and opacity with defaults (these are safe to default)
|
||||
// Validate basic numeric properties
|
||||
shape.x = validateNumericValue(shape.x, 0, 'x')
|
||||
shape.y = validateNumericValue(shape.y, 0, 'y')
|
||||
shape.rotation = validateNumericValue(shape.rotation, 0, 'rotation')
|
||||
shape.opacity = validateNumericValue(shape.opacity, 1, 'opacity')
|
||||
|
||||
|
|
@ -179,21 +165,12 @@ export function CustomMainMenu() {
|
|||
const fixIncompleteShape = (shape: any, pageId: string): any => {
|
||||
const fixedShape = { ...shape }
|
||||
|
||||
// DEBUG: Log coordinates before validation
|
||||
const originalX = fixedShape.x
|
||||
const originalY = fixedShape.y
|
||||
|
||||
// CRITICAL: Validate geometry first (fixes NaN/Infinity values)
|
||||
if (!validateShapeGeometry(fixedShape)) {
|
||||
console.warn(`⚠️ Shape failed geometry validation, skipping:`, fixedShape.id)
|
||||
return null // Return null to indicate shape should be skipped
|
||||
}
|
||||
|
||||
// DEBUG: Log if coordinates changed during validation
|
||||
if (originalX !== fixedShape.x || originalY !== fixedShape.y) {
|
||||
console.log(`🔍 Coordinates changed during validation for ${fixedShape.id}: (${originalX},${originalY}) → (${fixedShape.x},${fixedShape.y})`)
|
||||
}
|
||||
|
||||
// CRITICAL: Validate and normalize shape type
|
||||
const normalizedType = validateAndNormalizeShapeType(fixedShape)
|
||||
if (normalizedType !== fixedShape.type) {
|
||||
|
|
@ -280,33 +257,6 @@ export function CustomMainMenu() {
|
|||
if (!fixedShape.props.dash) fixedShape.props.dash = 'draw'
|
||||
if (!fixedShape.props.size) fixedShape.props.size = 'm'
|
||||
if (!fixedShape.props.font) fixedShape.props.font = 'draw'
|
||||
|
||||
// CRITICAL: Convert props.text to props.richText for geo shapes (tldraw schema change)
|
||||
// tldraw no longer accepts props.text on geo shapes - must use richText
|
||||
// Also preserve in meta.text for backward compatibility (used by search and runLLMprompt)
|
||||
if ('text' in fixedShape.props && typeof fixedShape.props.text === 'string') {
|
||||
const textContent = fixedShape.props.text
|
||||
|
||||
// Convert text string to richText format for tldraw
|
||||
fixedShape.props.richText = {
|
||||
type: 'doc',
|
||||
content: textContent ? [{
|
||||
type: 'paragraph',
|
||||
content: [{
|
||||
type: 'text',
|
||||
text: textContent
|
||||
}]
|
||||
}] : []
|
||||
}
|
||||
|
||||
// CRITICAL: Preserve original text in meta.text for backward compatibility
|
||||
// This is used by search (src/utils/searchUtils.ts) and other legacy code
|
||||
if (!fixedShape.meta) fixedShape.meta = {}
|
||||
fixedShape.meta.text = textContent
|
||||
|
||||
// Remove invalid props.text
|
||||
delete fixedShape.props.text
|
||||
}
|
||||
} else if (fixedShape.type === 'VideoChat') {
|
||||
// VideoChat shapes also need w/h in props, not top level
|
||||
const wValue = fixedShape.w !== undefined ? fixedShape.w : 200
|
||||
|
|
@ -553,33 +503,6 @@ export function CustomMainMenu() {
|
|||
if (wValue !== undefined && !shape.props.w) shape.props.w = wValue
|
||||
if (hValue !== undefined && !shape.props.h) shape.props.h = hValue
|
||||
if (geoValue !== undefined && !shape.props.geo) shape.props.geo = geoValue
|
||||
|
||||
// CRITICAL: Convert props.text to props.richText for geo shapes (tldraw schema change)
|
||||
// tldraw no longer accepts props.text on geo shapes - must use richText
|
||||
// Also preserve in meta.text for backward compatibility (used by search and runLLMprompt)
|
||||
if ('text' in shape.props && typeof shape.props.text === 'string') {
|
||||
const textContent = shape.props.text
|
||||
|
||||
// Convert text string to richText format for tldraw
|
||||
shape.props.richText = {
|
||||
type: 'doc',
|
||||
content: textContent ? [{
|
||||
type: 'paragraph',
|
||||
content: [{
|
||||
type: 'text',
|
||||
text: textContent
|
||||
}]
|
||||
}] : []
|
||||
}
|
||||
|
||||
// CRITICAL: Preserve original text in meta.text for backward compatibility
|
||||
// This is used by search (src/utils/searchUtils.ts) and other legacy code
|
||||
if (!shape.meta) shape.meta = {}
|
||||
shape.meta.text = textContent
|
||||
|
||||
// Remove invalid props.text
|
||||
delete shape.props.text
|
||||
}
|
||||
}
|
||||
|
||||
// CRITICAL: Remove invalid 'text' property from text shapes (TLDraw schema doesn't allow props.text)
|
||||
|
|
@ -594,21 +517,8 @@ export function CustomMainMenu() {
|
|||
|
||||
console.log('About to call putContentOntoCurrentPage with:', contentToImport)
|
||||
|
||||
// DEBUG: Log first 5 shapes' coordinates before import
|
||||
console.log('🔍 Coordinates before putContentOntoCurrentPage:')
|
||||
contentToImport.shapes.slice(0, 5).forEach((shape: any) => {
|
||||
console.log(` Shape ${shape.id} (${shape.type}): x=${shape.x}, y=${shape.y}`)
|
||||
})
|
||||
|
||||
try {
|
||||
editor.putContentOntoCurrentPage(contentToImport, { select: true })
|
||||
|
||||
// DEBUG: Log first 5 shapes' coordinates after import
|
||||
console.log('🔍 Coordinates after putContentOntoCurrentPage:')
|
||||
const importedShapes = editor.getCurrentPageShapes()
|
||||
importedShapes.slice(0, 5).forEach((shape: any) => {
|
||||
console.log(` Shape ${shape.id} (${shape.type}): x=${shape.x}, y=${shape.y}`)
|
||||
})
|
||||
} catch (putContentError) {
|
||||
console.error('putContentOntoCurrentPage failed, trying alternative approach:', putContentError)
|
||||
|
||||
|
|
@ -683,33 +593,6 @@ export function CustomMainMenu() {
|
|||
if (wValue !== undefined && !shape.props.w) shape.props.w = wValue
|
||||
if (hValue !== undefined && !shape.props.h) shape.props.h = hValue
|
||||
if (geoValue !== undefined && !shape.props.geo) shape.props.geo = geoValue
|
||||
|
||||
// CRITICAL: Convert props.text to props.richText for geo shapes (tldraw schema change)
|
||||
// tldraw no longer accepts props.text on geo shapes - must use richText
|
||||
// Also preserve in meta.text for backward compatibility (used by search and runLLMprompt)
|
||||
if ('text' in shape.props && typeof shape.props.text === 'string') {
|
||||
const textContent = shape.props.text
|
||||
|
||||
// Convert text string to richText format for tldraw
|
||||
shape.props.richText = {
|
||||
type: 'doc',
|
||||
content: textContent ? [{
|
||||
type: 'paragraph',
|
||||
content: [{
|
||||
type: 'text',
|
||||
text: textContent
|
||||
}]
|
||||
}] : []
|
||||
}
|
||||
|
||||
// CRITICAL: Preserve original text in meta.text for backward compatibility
|
||||
// This is used by search (src/utils/searchUtils.ts) and other legacy code
|
||||
if (!shape.meta) shape.meta = {}
|
||||
shape.meta.text = textContent
|
||||
|
||||
// Remove invalid props.text
|
||||
delete shape.props.text
|
||||
}
|
||||
}
|
||||
|
||||
// CRITICAL: Remove invalid 'text' property from text shapes (TLDraw schema doesn't allow props.text)
|
||||
|
|
|
|||
|
|
@ -33,6 +33,7 @@ export const components: TLComponents = {
|
|||
tools["Transcription"],
|
||||
tools["Holon"],
|
||||
tools["FathomMeetings"],
|
||||
tools["ImageGen"],
|
||||
].filter(tool => tool && tool.kbd)
|
||||
|
||||
// Get all custom actions with keyboard shortcuts
|
||||
|
|
|
|||
|
|
@ -196,6 +196,15 @@ export const overrides: TLUiOverrides = {
|
|||
// Shape creation is handled manually in FathomMeetingsTool.onPointerDown
|
||||
onSelect: () => editor.setCurrentTool("fathom-meetings"),
|
||||
},
|
||||
ImageGen: {
|
||||
id: "ImageGen",
|
||||
icon: "image",
|
||||
label: "Image Generation",
|
||||
kbd: "alt+i",
|
||||
readonlyOk: true,
|
||||
type: "ImageGen",
|
||||
onSelect: () => editor.setCurrentTool("ImageGen"),
|
||||
},
|
||||
hand: {
|
||||
...tools.hand,
|
||||
onDoubleClick: (info: any) => {
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
import OpenAI from "openai";
|
||||
import Anthropic from "@anthropic-ai/sdk";
|
||||
import { makeRealSettings, AI_PERSONALITIES } from "@/lib/settings";
|
||||
import { getRunPodConfig } from "@/lib/clientConfig";
|
||||
|
||||
export async function llm(
|
||||
userPrompt: string,
|
||||
|
|
@ -59,7 +60,12 @@ export async function llm(
|
|||
availableProviders.map(p => `${p.provider} (${p.model})`).join(', '));
|
||||
|
||||
if (availableProviders.length === 0) {
|
||||
throw new Error("No valid API key found for any provider")
|
||||
const runpodConfig = getRunPodConfig();
|
||||
if (runpodConfig && runpodConfig.apiKey && runpodConfig.endpointId) {
|
||||
// RunPod should have been added, but if not, try one more time
|
||||
console.log('⚠️ No user API keys found, but RunPod is configured - this should not happen');
|
||||
}
|
||||
throw new Error("No valid API key found for any provider. Please configure API keys in settings or set up RunPod environment variables (VITE_RUNPOD_API_KEY and VITE_RUNPOD_ENDPOINT_ID).")
|
||||
}
|
||||
|
||||
// Try each provider/key combination in order until one succeeds
|
||||
|
|
@ -76,13 +82,14 @@ export async function llm(
|
|||
'claude-3-haiku-20240307',
|
||||
];
|
||||
|
||||
for (const { provider, apiKey, model } of availableProviders) {
|
||||
for (const providerInfo of availableProviders) {
|
||||
const { provider, apiKey, model, endpointId } = providerInfo as any;
|
||||
try {
|
||||
console.log(`🔄 Attempting to use ${provider} API (${model})...`);
|
||||
attemptedProviders.push(`${provider} (${model})`);
|
||||
|
||||
// Add retry logic for temporary failures
|
||||
await callProviderAPIWithRetry(provider, apiKey, model, userPrompt, onToken, settings);
|
||||
await callProviderAPIWithRetry(provider, apiKey, model, userPrompt, onToken, settings, endpointId);
|
||||
console.log(`✅ Successfully used ${provider} API (${model})`);
|
||||
return; // Success, exit the function
|
||||
} catch (error) {
|
||||
|
|
@ -100,7 +107,9 @@ export async function llm(
|
|||
try {
|
||||
console.log(`🔄 Trying fallback model: ${fallbackModel}...`);
|
||||
attemptedProviders.push(`${provider} (${fallbackModel})`);
|
||||
await callProviderAPIWithRetry(provider, apiKey, fallbackModel, userPrompt, onToken, settings);
|
||||
const providerInfo = availableProviders.find(p => p.provider === provider);
|
||||
const endpointId = (providerInfo as any)?.endpointId;
|
||||
await callProviderAPIWithRetry(provider, apiKey, fallbackModel, userPrompt, onToken, settings, endpointId);
|
||||
console.log(`✅ Successfully used ${provider} API with fallback model ${fallbackModel}`);
|
||||
fallbackSucceeded = true;
|
||||
return; // Success, exit the function
|
||||
|
|
@ -142,13 +151,17 @@ function getAvailableProviders(availableKeys: Record<string, string>, settings:
|
|||
const providers = [];
|
||||
|
||||
// Helper to add a provider key if valid
|
||||
const addProviderKey = (provider: string, apiKey: string, model?: string) => {
|
||||
const addProviderKey = (provider: string, apiKey: string, model?: string, endpointId?: string) => {
|
||||
if (isValidApiKey(provider, apiKey) && !isApiKeyInvalid(provider, apiKey)) {
|
||||
providers.push({
|
||||
const providerInfo: any = {
|
||||
provider: provider,
|
||||
apiKey: apiKey,
|
||||
model: model || settings.models[provider] || getDefaultModel(provider)
|
||||
});
|
||||
};
|
||||
if (endpointId) {
|
||||
providerInfo.endpointId = endpointId;
|
||||
}
|
||||
providers.push(providerInfo);
|
||||
return true;
|
||||
} else if (isApiKeyInvalid(provider, apiKey)) {
|
||||
console.log(`⏭️ Skipping ${provider} API key (marked as invalid)`);
|
||||
|
|
@ -156,6 +169,20 @@ function getAvailableProviders(availableKeys: Record<string, string>, settings:
|
|||
return false;
|
||||
};
|
||||
|
||||
// PRIORITY 1: Check for RunPod configuration from environment variables FIRST
|
||||
// RunPod takes priority over user-configured keys
|
||||
const runpodConfig = getRunPodConfig();
|
||||
if (runpodConfig && runpodConfig.apiKey && runpodConfig.endpointId) {
|
||||
console.log('🔑 Found RunPod configuration from environment variables - using as primary AI provider');
|
||||
providers.push({
|
||||
provider: 'runpod',
|
||||
apiKey: runpodConfig.apiKey,
|
||||
endpointId: runpodConfig.endpointId,
|
||||
model: 'default' // RunPod doesn't use model selection in the same way
|
||||
});
|
||||
}
|
||||
|
||||
// PRIORITY 2: Then add user-configured keys (they will be tried after RunPod)
|
||||
// First, try the preferred provider - support multiple keys if stored as comma-separated
|
||||
if (settings.provider && availableKeys[settings.provider]) {
|
||||
const keyValue = availableKeys[settings.provider];
|
||||
|
|
@ -239,8 +266,10 @@ function getAvailableProviders(availableKeys: Record<string, string>, settings:
|
|||
}
|
||||
|
||||
// Additional fallback: Check for user-specific API keys from profile dashboard
|
||||
if (providers.length === 0) {
|
||||
providers.push(...getUserSpecificApiKeys());
|
||||
// These will be tried after RunPod (if RunPod was added)
|
||||
const userSpecificKeys = getUserSpecificApiKeys();
|
||||
if (userSpecificKeys.length > 0) {
|
||||
providers.push(...userSpecificKeys);
|
||||
}
|
||||
|
||||
return providers;
|
||||
|
|
@ -372,13 +401,14 @@ async function callProviderAPIWithRetry(
|
|||
userPrompt: string,
|
||||
onToken: (partialResponse: string, done?: boolean) => void,
|
||||
settings?: any,
|
||||
endpointId?: string,
|
||||
maxRetries: number = 2
|
||||
) {
|
||||
let lastError: Error | null = null;
|
||||
|
||||
for (let attempt = 1; attempt <= maxRetries; attempt++) {
|
||||
try {
|
||||
await callProviderAPI(provider, apiKey, model, userPrompt, onToken, settings);
|
||||
await callProviderAPI(provider, apiKey, model, userPrompt, onToken, settings, endpointId);
|
||||
return; // Success
|
||||
} catch (error) {
|
||||
lastError = error as Error;
|
||||
|
|
@ -471,12 +501,226 @@ async function callProviderAPI(
|
|||
model: string,
|
||||
userPrompt: string,
|
||||
onToken: (partialResponse: string, done?: boolean) => void,
|
||||
settings?: any
|
||||
settings?: any,
|
||||
endpointId?: string
|
||||
) {
|
||||
let partial = "";
|
||||
const systemPrompt = settings ? getSystemPrompt(settings) : 'You are a helpful assistant.';
|
||||
|
||||
if (provider === 'openai') {
|
||||
if (provider === 'runpod') {
|
||||
// RunPod API integration - uses environment variables for automatic setup
|
||||
// Get endpointId from parameter or from config
|
||||
let runpodEndpointId = endpointId;
|
||||
if (!runpodEndpointId) {
|
||||
const runpodConfig = getRunPodConfig();
|
||||
if (runpodConfig) {
|
||||
runpodEndpointId = runpodConfig.endpointId;
|
||||
}
|
||||
}
|
||||
|
||||
if (!runpodEndpointId) {
|
||||
throw new Error('RunPod endpoint ID not configured');
|
||||
}
|
||||
|
||||
// Try /runsync first for synchronous execution (returns output immediately)
|
||||
// Fall back to /run + polling if /runsync is not available
|
||||
const syncUrl = `https://api.runpod.ai/v2/${runpodEndpointId}/runsync`;
|
||||
const asyncUrl = `https://api.runpod.ai/v2/${runpodEndpointId}/run`;
|
||||
|
||||
// vLLM endpoints typically expect OpenAI-compatible format with messages array
|
||||
// But some endpoints might accept simple prompt format
|
||||
// Try OpenAI-compatible format first, as it's more standard for vLLM
|
||||
const messages = [];
|
||||
if (systemPrompt) {
|
||||
messages.push({ role: 'system', content: systemPrompt });
|
||||
}
|
||||
messages.push({ role: 'user', content: userPrompt });
|
||||
|
||||
// Combine system prompt and user prompt for simple prompt format (fallback)
|
||||
const fullPrompt = systemPrompt ? `${systemPrompt}\n\nUser: ${userPrompt}` : userPrompt;
|
||||
|
||||
const requestBody = {
|
||||
input: {
|
||||
messages: messages,
|
||||
stream: false // vLLM can handle streaming, but we'll process it synchronously for now
|
||||
}
|
||||
};
|
||||
|
||||
console.log('📤 RunPod API: Trying synchronous endpoint first:', syncUrl);
|
||||
console.log('📤 RunPod API: Using OpenAI-compatible messages format');
|
||||
|
||||
try {
|
||||
// First, try synchronous endpoint (/runsync) - this returns output immediately
|
||||
try {
|
||||
const syncResponse = await fetch(syncUrl, {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
'Authorization': `Bearer ${apiKey}`
|
||||
},
|
||||
body: JSON.stringify(requestBody)
|
||||
});
|
||||
|
||||
if (syncResponse.ok) {
|
||||
const syncData = await syncResponse.json();
|
||||
console.log('📥 RunPod API: Synchronous response:', JSON.stringify(syncData, null, 2));
|
||||
|
||||
// Check if we got output directly
|
||||
if (syncData.output) {
|
||||
let responseText = '';
|
||||
if (syncData.output.choices && Array.isArray(syncData.output.choices)) {
|
||||
const choice = syncData.output.choices[0];
|
||||
if (choice && choice.message && choice.message.content) {
|
||||
responseText = choice.message.content;
|
||||
}
|
||||
} else if (typeof syncData.output === 'string') {
|
||||
responseText = syncData.output;
|
||||
} else if (syncData.output.text) {
|
||||
responseText = syncData.output.text;
|
||||
} else if (syncData.output.response) {
|
||||
responseText = syncData.output.response;
|
||||
}
|
||||
|
||||
if (responseText) {
|
||||
console.log('✅ RunPod API: Got output from synchronous endpoint, length:', responseText.length);
|
||||
// Stream the response character by character to simulate streaming
|
||||
for (let i = 0; i < responseText.length; i++) {
|
||||
partial += responseText[i];
|
||||
onToken(partial, false);
|
||||
await new Promise(resolve => setTimeout(resolve, 10));
|
||||
}
|
||||
onToken(partial, true);
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
// If sync endpoint returned a job ID, fall through to async polling
|
||||
if (syncData.id && (syncData.status === 'IN_QUEUE' || syncData.status === 'IN_PROGRESS')) {
|
||||
console.log('⏳ RunPod API: Sync endpoint returned job ID, polling:', syncData.id);
|
||||
const result = await pollRunPodJob(syncData.id, apiKey, runpodEndpointId);
|
||||
console.log('✅ RunPod API: Job completed, result length:', result.length);
|
||||
partial = result;
|
||||
onToken(partial, true);
|
||||
return;
|
||||
}
|
||||
}
|
||||
} catch (syncError) {
|
||||
console.log('⚠️ RunPod API: Synchronous endpoint not available, trying async:', syncError);
|
||||
}
|
||||
|
||||
// Fall back to async endpoint (/run) if sync didn't work
|
||||
console.log('📤 RunPod API: Using async endpoint:', asyncUrl);
|
||||
const response = await fetch(asyncUrl, {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
'Authorization': `Bearer ${apiKey}`
|
||||
},
|
||||
body: JSON.stringify(requestBody)
|
||||
});
|
||||
|
||||
console.log('📥 RunPod API: Response status:', response.status, response.statusText);
|
||||
|
||||
if (!response.ok) {
|
||||
const errorText = await response.text();
|
||||
console.error('❌ RunPod API: Error response:', errorText);
|
||||
throw new Error(`RunPod API error: ${response.status} - ${errorText}`);
|
||||
}
|
||||
|
||||
const data = await response.json();
|
||||
console.log('📥 RunPod API: Response data:', JSON.stringify(data, null, 2));
|
||||
|
||||
// Handle async job pattern (RunPod often returns job IDs)
|
||||
if (data.id && (data.status === 'IN_QUEUE' || data.status === 'IN_PROGRESS')) {
|
||||
console.log('⏳ RunPod API: Job queued/in progress, polling job ID:', data.id);
|
||||
const result = await pollRunPodJob(data.id, apiKey, runpodEndpointId);
|
||||
console.log('✅ RunPod API: Job completed, result length:', result.length);
|
||||
partial = result;
|
||||
onToken(partial, true);
|
||||
return;
|
||||
}
|
||||
|
||||
// Handle OpenAI-compatible response format (vLLM endpoints)
|
||||
if (data.output && data.output.choices && Array.isArray(data.output.choices)) {
|
||||
console.log('📥 RunPod API: Detected OpenAI-compatible response format');
|
||||
const choice = data.output.choices[0];
|
||||
if (choice && choice.message && choice.message.content) {
|
||||
const responseText = choice.message.content;
|
||||
console.log('✅ RunPod API: Extracted content from OpenAI-compatible format, length:', responseText.length);
|
||||
|
||||
// Stream the response character by character to simulate streaming
|
||||
for (let i = 0; i < responseText.length; i++) {
|
||||
partial += responseText[i];
|
||||
onToken(partial, false);
|
||||
// Small delay to simulate streaming
|
||||
await new Promise(resolve => setTimeout(resolve, 10));
|
||||
}
|
||||
onToken(partial, true);
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
// Handle direct response
|
||||
if (data.output) {
|
||||
console.log('📥 RunPod API: Processing output:', typeof data.output, Array.isArray(data.output) ? 'array' : 'object');
|
||||
// Try to extract text from various possible response formats
|
||||
let responseText = '';
|
||||
if (typeof data.output === 'string') {
|
||||
responseText = data.output;
|
||||
console.log('✅ RunPod API: Extracted string output, length:', responseText.length);
|
||||
} else if (data.output.text) {
|
||||
responseText = data.output.text;
|
||||
console.log('✅ RunPod API: Extracted text from output.text, length:', responseText.length);
|
||||
} else if (data.output.response) {
|
||||
responseText = data.output.response;
|
||||
console.log('✅ RunPod API: Extracted response from output.response, length:', responseText.length);
|
||||
} else if (data.output.content) {
|
||||
responseText = data.output.content;
|
||||
console.log('✅ RunPod API: Extracted content from output.content, length:', responseText.length);
|
||||
} else if (Array.isArray(data.output.segments)) {
|
||||
responseText = data.output.segments.map((seg: any) => seg.text || seg).join(' ');
|
||||
console.log('✅ RunPod API: Extracted text from segments, length:', responseText.length);
|
||||
} else {
|
||||
// Fallback: stringify the output
|
||||
console.warn('⚠️ RunPod API: Unknown output format, stringifying:', Object.keys(data.output));
|
||||
responseText = JSON.stringify(data.output);
|
||||
}
|
||||
|
||||
// Stream the response character by character to simulate streaming
|
||||
for (let i = 0; i < responseText.length; i++) {
|
||||
partial += responseText[i];
|
||||
onToken(partial, false);
|
||||
// Small delay to simulate streaming
|
||||
await new Promise(resolve => setTimeout(resolve, 10));
|
||||
}
|
||||
onToken(partial, true);
|
||||
return;
|
||||
}
|
||||
|
||||
// Handle error response
|
||||
if (data.error) {
|
||||
console.error('❌ RunPod API: Error in response:', data.error);
|
||||
throw new Error(`RunPod API error: ${data.error}`);
|
||||
}
|
||||
|
||||
// Check for status messages that might indicate endpoint is starting up
|
||||
if (data.status) {
|
||||
console.log('ℹ️ RunPod API: Response status:', data.status);
|
||||
if (data.status === 'STARTING' || data.status === 'PENDING') {
|
||||
console.log('⏳ RunPod API: Endpoint appears to be starting up, this may take a moment...');
|
||||
// Wait a bit and retry
|
||||
await new Promise(resolve => setTimeout(resolve, 2000));
|
||||
throw new Error('RunPod endpoint is starting up. Please wait a moment and try again.');
|
||||
}
|
||||
}
|
||||
|
||||
console.error('❌ RunPod API: No valid response format detected. Full response:', JSON.stringify(data, null, 2));
|
||||
throw new Error('No valid response from RunPod API');
|
||||
} catch (error) {
|
||||
console.error('❌ RunPod API error:', error);
|
||||
throw error;
|
||||
}
|
||||
} else if (provider === 'openai') {
|
||||
const openai = new OpenAI({
|
||||
apiKey,
|
||||
dangerouslyAllowBrowser: true,
|
||||
|
|
@ -556,6 +800,185 @@ async function callProviderAPI(
|
|||
onToken(partial, true);
|
||||
}
|
||||
|
||||
// Helper function to poll RunPod job status until completion
|
||||
async function pollRunPodJob(
|
||||
jobId: string,
|
||||
apiKey: string,
|
||||
endpointId: string,
|
||||
maxAttempts: number = 60,
|
||||
pollInterval: number = 1000
|
||||
): Promise<string> {
|
||||
const statusUrl = `https://api.runpod.ai/v2/${endpointId}/status/${jobId}`;
|
||||
console.log('🔄 RunPod API: Starting to poll job:', jobId);
|
||||
|
||||
for (let attempt = 0; attempt < maxAttempts; attempt++) {
|
||||
try {
|
||||
const response = await fetch(statusUrl, {
|
||||
method: 'GET',
|
||||
headers: {
|
||||
'Authorization': `Bearer ${apiKey}`
|
||||
}
|
||||
});
|
||||
|
||||
if (!response.ok) {
|
||||
const errorText = await response.text();
|
||||
console.error(`❌ RunPod API: Poll error (attempt ${attempt + 1}/${maxAttempts}):`, response.status, errorText);
|
||||
throw new Error(`Failed to check job status: ${response.status} - ${errorText}`);
|
||||
}
|
||||
|
||||
const data = await response.json();
|
||||
console.log(`🔄 RunPod API: Poll attempt ${attempt + 1}/${maxAttempts}, status:`, data.status);
|
||||
console.log(`📥 RunPod API: Full poll response:`, JSON.stringify(data, null, 2));
|
||||
|
||||
if (data.status === 'COMPLETED') {
|
||||
console.log('✅ RunPod API: Job completed, processing output...');
|
||||
console.log('📥 RunPod API: Output structure:', typeof data.output, data.output ? Object.keys(data.output) : 'null');
|
||||
console.log('📥 RunPod API: Full data object keys:', Object.keys(data));
|
||||
|
||||
// If no output after a couple of retries, try the stream endpoint as fallback
|
||||
if (!data.output) {
|
||||
if (attempt < 3) {
|
||||
// Only retry 2-3 times, then try stream endpoint
|
||||
console.log(`⏳ RunPod API: COMPLETED but no output yet, waiting briefly (attempt ${attempt + 1}/3)...`);
|
||||
await new Promise(resolve => setTimeout(resolve, 500));
|
||||
continue;
|
||||
}
|
||||
|
||||
// After a few retries, try the stream endpoint as fallback
|
||||
console.log('⚠️ RunPod API: Status endpoint not returning output, trying stream endpoint...');
|
||||
try {
|
||||
const streamUrl = `https://api.runpod.ai/v2/${endpointId}/stream/${jobId}`;
|
||||
const streamResponse = await fetch(streamUrl, {
|
||||
method: 'GET',
|
||||
headers: {
|
||||
'Authorization': `Bearer ${apiKey}`
|
||||
}
|
||||
});
|
||||
|
||||
if (streamResponse.ok) {
|
||||
const streamData = await streamResponse.json();
|
||||
console.log('📥 RunPod API: Stream endpoint response:', JSON.stringify(streamData, null, 2));
|
||||
|
||||
if (streamData.output) {
|
||||
// Use stream endpoint output
|
||||
data.output = streamData.output;
|
||||
console.log('✅ RunPod API: Found output via stream endpoint');
|
||||
} else if (streamData.choices && Array.isArray(streamData.choices)) {
|
||||
// Handle OpenAI-compatible format from stream endpoint
|
||||
data.output = { choices: streamData.choices };
|
||||
console.log('✅ RunPod API: Found choices via stream endpoint');
|
||||
}
|
||||
} else {
|
||||
console.log(`⚠️ RunPod API: Stream endpoint returned ${streamResponse.status}`);
|
||||
}
|
||||
} catch (streamError) {
|
||||
console.log('⚠️ RunPod API: Stream endpoint not available or failed:', streamError);
|
||||
}
|
||||
}
|
||||
|
||||
// Extract text from various possible response formats
|
||||
let result = '';
|
||||
if (typeof data.output === 'string') {
|
||||
result = data.output;
|
||||
console.log('✅ RunPod API: Extracted string output from job, length:', result.length);
|
||||
} else if (data.output?.text) {
|
||||
result = data.output.text;
|
||||
console.log('✅ RunPod API: Extracted text from output.text, length:', result.length);
|
||||
} else if (data.output?.response) {
|
||||
result = data.output.response;
|
||||
console.log('✅ RunPod API: Extracted response from output.response, length:', result.length);
|
||||
} else if (data.output?.content) {
|
||||
result = data.output.content;
|
||||
console.log('✅ RunPod API: Extracted content from output.content, length:', result.length);
|
||||
} else if (data.output?.choices && Array.isArray(data.output.choices)) {
|
||||
// Handle OpenAI-compatible response format (vLLM endpoints)
|
||||
const choice = data.output.choices[0];
|
||||
if (choice && choice.message && choice.message.content) {
|
||||
result = choice.message.content;
|
||||
console.log('✅ RunPod API: Extracted content from OpenAI-compatible format, length:', result.length);
|
||||
}
|
||||
} else if (data.output?.segments && Array.isArray(data.output.segments)) {
|
||||
result = data.output.segments.map((seg: any) => seg.text || seg).join(' ');
|
||||
console.log('✅ RunPod API: Extracted text from segments, length:', result.length);
|
||||
} else if (Array.isArray(data.output)) {
|
||||
// Handle array responses (some vLLM endpoints return arrays)
|
||||
result = data.output.map((item: any) => {
|
||||
if (typeof item === 'string') return item;
|
||||
if (item.text) return item.text;
|
||||
if (item.response) return item.response;
|
||||
return JSON.stringify(item);
|
||||
}).join('\n');
|
||||
console.log('✅ RunPod API: Extracted text from array output, length:', result.length);
|
||||
} else if (!data.output) {
|
||||
// No output field - check alternative structures or return empty
|
||||
console.warn('⚠️ RunPod API: No output field found, checking alternative structures...');
|
||||
console.log('📥 RunPod API: Full data structure:', JSON.stringify(data, null, 2));
|
||||
|
||||
// Try checking if output is directly in data (not data.output)
|
||||
if (typeof data === 'string') {
|
||||
result = data;
|
||||
console.log('✅ RunPod API: Data itself is a string, length:', result.length);
|
||||
} else if (data.text) {
|
||||
result = data.text;
|
||||
console.log('✅ RunPod API: Found text at top level, length:', result.length);
|
||||
} else if (data.response) {
|
||||
result = data.response;
|
||||
console.log('✅ RunPod API: Found response at top level, length:', result.length);
|
||||
} else if (data.content) {
|
||||
result = data.content;
|
||||
console.log('✅ RunPod API: Found content at top level, length:', result.length);
|
||||
} else {
|
||||
// Stream endpoint already tried above (around line 848), just log that we couldn't find output
|
||||
if (attempt >= 3) {
|
||||
console.warn('⚠️ RunPod API: Could not find output in status or stream endpoint after multiple attempts');
|
||||
}
|
||||
|
||||
// If still no result, return empty string instead of throwing error
|
||||
// This allows the UI to render something instead of failing
|
||||
if (!result) {
|
||||
console.warn('⚠️ RunPod API: No output found in response. Returning empty result.');
|
||||
console.log('📥 RunPod API: Available fields:', Object.keys(data));
|
||||
result = ''; // Return empty string so UI can render
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Return result even if empty - don't loop forever
|
||||
if (result !== undefined) {
|
||||
// Return empty string if no result found - allows UI to render
|
||||
console.log('✅ RunPod API: Returning result (may be empty):', result ? `length ${result.length}` : 'empty');
|
||||
return result || '';
|
||||
}
|
||||
|
||||
// If we get here, no output was found - return empty string instead of looping
|
||||
console.warn('⚠️ RunPod API: No output found after checking all formats. Returning empty result.');
|
||||
return '';
|
||||
}
|
||||
|
||||
if (data.status === 'FAILED') {
|
||||
console.error('❌ RunPod API: Job failed:', data.error || 'Unknown error');
|
||||
throw new Error(`Job failed: ${data.error || 'Unknown error'}`);
|
||||
}
|
||||
|
||||
// Check for starting/pending status
|
||||
if (data.status === 'STARTING' || data.status === 'PENDING') {
|
||||
console.log(`⏳ RunPod API: Endpoint still starting (attempt ${attempt + 1}/${maxAttempts})...`);
|
||||
}
|
||||
|
||||
// Job still in progress, wait and retry
|
||||
await new Promise(resolve => setTimeout(resolve, pollInterval));
|
||||
} catch (error) {
|
||||
if (attempt === maxAttempts - 1) {
|
||||
throw error;
|
||||
}
|
||||
// Wait before retrying
|
||||
await new Promise(resolve => setTimeout(resolve, pollInterval));
|
||||
}
|
||||
}
|
||||
|
||||
throw new Error('Job polling timeout - job did not complete in time');
|
||||
}
|
||||
|
||||
// Auto-migration function that runs automatically
|
||||
async function autoMigrateAPIKeys() {
|
||||
try {
|
||||
|
|
|
|||
Loading…
Reference in New Issue