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# Flask API Setup Guide
## Overview
The Flask API (`flask_api.py`) provides a simple REST API interface to the Research AI Assistant orchestrator. It runs on port 5001, separate from Gradio (port 7860).
## Architecture
```
βββββββββββββββββββ
β Express.js UI β Port 3000
β (or other β - User interface
β frontend) β - Session management
ββββββββββ¬βββββββββ
β HTTP POST /api/chat
β
βββββββββββββββββββ
β Flask API β Port 5001
β flask_api.py β - REST endpoints
β β - Imports from app.py
ββββββββββ¬βββββββββ - Direct Python access
β
βββββββββββββββββββ
β Orchestrator β Python
β (from app.py) β - AI agents
β β - Context management
βββββββββββββββββββ
βββββββββββββββββββ
β Gradio UI β Port 7860
β (optional) β - Alternative UI
β app.py β - Can run alongside
βββββββββββββββββββ
```
## Quick Start
### Step 1: Install Dependencies
```bash
pip install flask flask-cors
```
Or install all requirements:
```bash
pip install -r requirements.txt
```
### Step 2: Start Flask API
```bash
# Default port 5001
python flask_api.py
# Or specify custom port
FLASK_PORT=5001 python flask_api.py
```
You should see:
```
STARTING FLASK API
INITIALIZING AI ORCHESTRATOR
β AI ORCHESTRATOR READY
Starting Flask API on port 5001
```
### Step 3: Test the API
```bash
# Health check
curl http://localhost:5001/health
# Chat endpoint
curl -X POST http://localhost:5001/api/chat \
-H "Content-Type: application/json" \
-d '{
"message": "Hello, how are you?",
"session_id": "test-session-123",
"user_id": "test-user"
}'
```
## API Endpoints
### `GET /health`
Health check endpoint.
**Response:**
```json
{
"status": "ok",
"orchestrator_available": true,
"service": "flask_api"
}
```
### `POST /api/chat`
Main chat endpoint for processing user messages.
**Request:**
```json
{
"message": "What is machine learning?",
"history": [], // optional
"session_id": "session-123", // optional, auto-generated if not provided
"user_id": "user-123" // optional, defaults to "Admin_J"
}
```
**Response:**
```json
{
"response": "Machine learning is...",
"history": [
{"role": "user", "content": "What is machine learning?"},
{"role": "assistant", "content": "Machine learning is..."}
],
"reasoning": {
"chain_of_thought": {...},
"confidence_calibration": {...}
},
"performance": {
"processing_time": 1.23,
"tokens_used": 456
},
"context": {
"session_context": "...",
"relevant_interactions": [...]
},
"session_id": "session-123",
"skills": "<div>...</div>" // HTML for skills display
}
```
### `GET /api/orchestrator/status`
Check if orchestrator is available.
**Response:**
```json
{
"orchestrator_available": true,
"orchestrator_type": "MVPOrchestrator"
}
```
## Configuration
### Environment Variables
```bash
# Flask configuration
FLASK_PORT=5001 # Port for Flask API (default: 5001)
FLASK_HOST=0.0.0.0 # Host to bind to (default: 0.0.0.0)
# AI configuration (used by orchestrator)
HF_TOKEN=your_token_here # Hugging Face token for API access
```
### Ports
- **Flask API**: 5001 (configurable via `FLASK_PORT`)
- **Gradio UI**: 7860 (separate, can run alongside)
- **Express.js** (external): 3000 (your frontend)
## Running with Gradio
You can run Flask API alongside Gradio:
**Terminal 1: Flask API**
```bash
python flask_api.py
```
**Terminal 2: Gradio UI (optional)**
```bash
python app.py
```
Both can run simultaneously on different ports.
## Integration Examples
### Express.js Integration
See `EXPRESS_FLASK_INTEGRATION.md` for Express.js setup.
### Python Client
```python
import requests
def chat_with_flask(message, session_id=None):
url = "http://localhost:5001/api/chat"
data = {
"message": message,
"session_id": session_id or "python-client-session"
}
response = requests.post(url, json=data)
return response.json()
# Usage
result = chat_with_flask("Hello, how are you?")
print(result["response"])
```
### JavaScript/Node.js Client
```javascript
const axios = require('axios');
async function chatWithFlask(message, sessionId = null) {
const response = await axios.post('http://localhost:5001/api/chat', {
message: message,
session_id: sessionId || `node-client-session-${Date.now()}`
});
return response.data;
}
// Usage
chatWithFlask("Hello, how are you?")
.then(result => console.log(result.response))
.catch(error => console.error(error));
```
## Troubleshooting
### Issue: "Orchestrator not available"
**Check:**
1. Flask API logs show initialization errors
2. `HF_TOKEN` environment variable is set
3. All dependencies are installed
**Solution:**
```bash
# Check logs
# Look for "INITIALIZING AI ORCHESTRATOR" section
# Verify HF token
echo $HF_TOKEN
# Reinstall dependencies
pip install -r requirements.txt
```
### Issue: "Port already in use"
**Solution:**
```bash
# Use different port
FLASK_PORT=5002 python flask_api.py
```
### Issue: "Module not found"
**Check:**
1. You're in the correct directory (same as `app.py`)
2. All imports are available
**Solution:**
```bash
# Make sure you're in the project root
cd /path/to/Research_AI_Assistant
# Verify Python path
python -c "import app; print('β app.py importable')"
```
### Issue: "CORS errors"
**Solution:** Flask-CORS is enabled by default. If issues persist:
```python
# In flask_api.py, customize CORS:
from flask_cors import CORS
CORS(app, resources={r"/api/*": {"origins": "*"}})
```
## Advantages Over Gradio API
| Feature | Flask API | Gradio HTTP |
|---------|-----------|-------------|
| **Simplicity** | β
Simple JSON | β οΈ Gradio-specific format |
| **Control** | β
Full control | β οΈ Limited by Gradio |
| **Path issues** | β
None | β οΈ Can have path problems |
| **Dependencies** | β
Just Flask | β οΈ Requires Gradio client |
| **API design** | β
You design it | β οΈ Gradio's design |
| **Startup** | β
Fast | β οΈ Slower (Gradio overhead) |
## Production Deployment
### Using PM2 (Process Manager)
```bash
# Install PM2
npm install -g pm2
# Start Flask API
pm2 start flask_api.py --interpreter python3 --name flask-api
# Start Express (if using)
pm2 start server.js --name express
# View status
pm2 status
```
### Using systemd (Linux)
Create `/etc/systemd/system/flask-api.service`:
```ini
[Unit]
Description=Research AI Assistant Flask API
After=network.target
[Service]
Type=simple
User=your-user
WorkingDirectory=/path/to/Research_AI_Assistant
Environment="FLASK_PORT=5001"
Environment="HF_TOKEN=your-token"
ExecStart=/usr/bin/python3 flask_api.py
Restart=always
[Install]
WantedBy=multi-user.target
```
Then:
```bash
sudo systemctl enable flask-api
sudo systemctl start flask-api
```
## Security Considerations
1. **CORS**: Currently allows all origins (`*`). In production, restrict to your frontend domain:
```python
CORS(app, origins=["https://your-frontend.com"])
```
2. **Rate Limiting**: Consider adding rate limiting:
```bash
pip install flask-limiter
```
3. **Authentication**: Add API keys or tokens for production:
```python
@app.before_request
def check_api_key():
api_key = request.headers.get('X-API-Key')
if api_key != os.getenv('API_KEY'):
return jsonify({'error': 'Unauthorized'}), 401
```
## Summary
β
**Simple**: Just Flask, no complex wrappers
β
**Flexible**: Your API design, your control
β
**Compatible**: Works alongside Gradio
β
**Standard**: Standard REST API pattern
β
**Fast**: Direct Python imports, no overhead
Start Flask, integrate your frontend, done! π
|