);
}
export default PoliceBotChat;
```
## ๐ค Adding a Third Local LLM
You can enhance the system with additional local LLMs for specialized tasks:
### Option 1: Emotion Analysis LLM
```python
# Add to police_runtime.py
def get_emotion_llm_response(self, text: str) -> str:
"""Get emotion analysis from a dedicated LLM"""
try:
# You can use a smaller, specialized model for emotion analysis
data = {
"model": "emotion-analyzer", # Different Ollama model
"prompt": f"Analyze the emotional state of this text: '{text}'. Respond with only: stressed, positive, negative, or neutral.",
"stream": False,
"options": {
"temperature": 0.1, # Lower temperature for consistent analysis
"max_tokens": 10
}
}
response = requests.post(self.ollama_url, json=data, timeout=10)
response.raise_for_status()
result = response.json()
emotion = result.get("response", "").strip().lower()
# Validate emotion
valid_emotions = ["stressed", "positive", "negative", "neutral"]
return emotion if emotion in valid_emotions else "neutral"
except Exception as e:
logger.error(f"Error in emotion analysis: {e}")
return "neutral"
```
### Option 2: Wellness Recommendation LLM
```python
def get_wellness_llm_response(self, emotional_state: str, context: str = "") -> list:
"""Get personalized wellness recommendations from a specialized LLM"""
try:
prompt = f"""
As a wellness expert for police officers, provide 3-4 specific, actionable wellness tips for someone who is feeling {emotional_state}.
Context: {context}
Focus on:
- Quick, practical interventions (2-5 minutes)
- Stress management techniques
- Physical wellness (hydration, movement)
- Mental wellness (mindfulness, perspective)
Respond with only the tips, one per line, no numbering.
"""
data = {
"model": "wellness-expert", # Specialized wellness model
"prompt": prompt,
"stream": False,
"options": {
"temperature": 0.7,
"max_tokens": 200
}
}
response = requests.post(self.ollama_url, json=data, timeout=15)
response.raise_for_status()
result = response.json()
tips_text = result.get("response", "").strip()
# Parse tips into list
tips = [tip.strip() for tip in tips_text.split('\n') if tip.strip()]
return tips[:4] # Limit to 4 tips
except Exception as e:
logger.error(f"Error in wellness recommendations: {e}")
return self.generate_wellness_tips(emotional_state) # Fallback
```
### Option 3: Multi-Model Setup
Create a model manager to handle multiple LLMs:
```python
class ModelManager:
def __init__(self):
self.models = {
"primary": "police-bot", # Main conversation
"emotion": "emotion-analyzer", # Emotion analysis
"wellness": "wellness-expert", # Wellness recommendations
"kannada": "kannada-assistant" # Kannada language support
}
def get_response(self, model_type: str, prompt: str, **kwargs) -> str:
"""Get response from specified model"""
model_name = self.models.get(model_type, "police-bot")
data = {
"model": model_name,
"prompt": prompt,
"stream": False,
**kwargs
}
response = requests.post("http://localhost:11434/api/generate", json=data)
return response.json().get("response", "").strip()
```
## ๐ง Configuration
### Environment Variables
Create a `.env` file:
```env
# Ollama Configuration
OLLAMA_URL=http://localhost:11434
OLLAMA_MODEL=police-bot
# F5-TTS Configuration
F5TTS_CKPT=C:\Users\Samarth Kadam\Voice-train\F5-TTS\ckpts\my_speak\model_last.pt
F5TTS_SCRIPT=C:\Users\Samarth Kadam\Voice-train\F5-TTS\src\f5_tts\infer\infer_cli.py
# Web API Configuration
WEB_PORT=5000
WEB_HOST=0.0.0.0
# Security
API_KEY=your-secret-api-key
```
### CORS Configuration
For production deployment, configure CORS properly:
```python
# In web_api.py
from flask_cors import CORS
app = Flask(__name__)
CORS(app, origins=[
"http://localhost:3000", # React dev server
"http://your-website.com", # Production website
"https://your-website.com"
])
```
## ๐ Production Deployment
### Using Gunicorn
```bash
# Install gunicorn
pip install gunicorn
# Start production server
gunicorn -w 4 -b 0.0.0.0:5000 web_api:app
```
### Using Docker
```dockerfile
FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
EXPOSE 5000
CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:5000", "web_api:app"]
```
### Systemd Service (Linux)
```ini
# /etc/systemd/system/police-bot.service
[Unit]
Description=Police Bot AI Runtime
After=network.target
[Service]
Type=simple
User=police-bot
WorkingDirectory=/opt/police-bot-runtime
Environment=PATH=/opt/police-bot-runtime/venv/bin
ExecStart=/opt/police-bot-runtime/venv/bin/python web_api.py
Restart=always
[Install]
WantedBy=multi-user.target
```
## ๐ Security Considerations
1. **API Authentication**: Add API key validation
2. **Rate Limiting**: Implement request rate limiting
3. **Input Validation**: Sanitize all user inputs
4. **HTTPS**: Use SSL/TLS in production
5. **Firewall**: Restrict access to necessary ports only
## ๐ Monitoring and Logging
```python
# Add to web_api.py
import logging
from logging.handlers import RotatingFileHandler
# Configure logging
if not app.debug:
file_handler = RotatingFileHandler('logs/police_bot.log', maxBytes=10240, backupCount=10)
file_handler.setFormatter(logging.Formatter(
'%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]'
))
file_handler.setLevel(logging.INFO)
app.logger.addHandler(file_handler)
app.logger.setLevel(logging.INFO)
app.logger.info('Police Bot startup')
```
## ๐งช Testing
### API Testing
```bash
# Test chat endpoint
curl -X POST http://localhost:5000/api/chat \
-H "Content-Type: application/json" \
-d '{"message": "I am feeling stressed today"}'
# Test health endpoint
curl http://localhost:5000/health
# Test status endpoint
curl http://localhost:5000/api/status
```
### Load Testing
```python
import requests
import time
import threading
def test_load():
for i in range(10):
response = requests.post('http://localhost:5000/api/chat',
json={'message': f'Test message {i}'})
print(f'Request {i}: {response.status_code}')
# Run multiple threads
threads = [threading.Thread(target=test_load) for _ in range(5)]
for thread in threads:
thread.start()
for thread in threads:
thread.join()
```
## ๐ฏ Next Steps
1. **Voice Avatar**: Add a visual avatar that speaks
2. **Multi-language**: Implement Kannada language support
3. **Session Management**: Add user session tracking
4. **Analytics**: Track usage patterns and wellness trends
5. **Mobile App**: Create a mobile companion app
6. **Integration**: Connect with existing police systems
## ๐ Support
For technical support or questions:
- Check the logs in the `logs/` directory
- Verify all services are running: `python setup.py`
- Test individual components: `python police_runtime.py`
- Check API health: `curl http://localhost:5000/health`
---
**Remember**: This system is designed to support the mental wellness of police personnel. Always prioritize their privacy and well-being in any modifications or deployments.