app.py

import gradio as gr
import os
import requests
import json
from datetime import datetime

# Configuration - Use Hugging Face Secrets
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
MODEL = "llama3-70b-8192"
API_URL = "https://api.groq.com/openai/v1/chat/completions"

def analyze_flood(location, water_level, rainfall, historical_data):
    """Enhanced flood analysis with error handling"""
    prompt = f"""
    As FloodAI Expert, analyze:
    - Location: {location}
    - Water Level: {water_level}m
    - Rainfall: {rainfall}mm
    - Historical Data: {'Available' if historical_data else 'Unavailable'}
    
    Respond in JSON with:
    - risk_level (HIGH/MEDIUM/LOW)
    - confidence (0-100)
    - alert_message
    - actions (3 bullet points)
    - emergency (boolean)
    """
    
    try:
        response = requests.post(
            API_URL,
            headers={
                "Authorization": f"Bearer {GROQ_API_KEY}",
                "Content-Type": "application/json"
            },
            json={
                "model": MODEL,
                "messages": [
                    {
                        "role": "system",
                        "content": "You are FloodAI. Respond in valid JSON only."
                    },
                    {
                        "role": "user",
                        "content": prompt
                    }
                ],
                "response_format": {"type": "json_object"}
            },
            timeout=10
        )
        response.raise_for_status()
        result = json.loads(response.json()["choices"][0]["message"]["content"])
        
        # Format for Gradio output
        return {
            "Risk Level": result.get("risk_level", "UNKNOWN"),
            "Confidence": f"{result.get('confidence', 0)}%",
            "Alert": result.get("alert_message", "No alert generated"),
            "Recommended Actions": "\n".join([f"• {action}" for action in result.get("actions", [])]),
            "Emergency": "🚨 EVACUATE" if result.get("emergency") else "⚠️ Monitor"
        }
        
    except Exception as e:
        return {"Error": f"API Failure: {str(e)}"}

# Hugging Face Optimized Interface
with gr.Blocks(theme=gr.themes.Soft(), title="FloodAI Pro") as app:
    # Header Section
    gr.Markdown("""
    <div style='text-align: center'>
        <h1>🌊 FloodAI Pro</h1>
        <p>Real-time Flood Risk Assessment powered by Groq AI</p>
    </div>
    """)
    
    # Input Section
    with gr.Row():
        with gr.Column():
            gr.Markdown("### 📍 Location Data")
            location = gr.Textbox(label="City/Region", placeholder="e.g. Karachi, Pakistan")
            water_level = gr.Slider(0, 15, step=0.1, label="Water Level (meters)")
            rainfall = gr.Slider(0, 500, step=5, label="24h Rainfall (mm)")
            historical = gr.Checkbox(label="Include historical flood data")
            submit_btn = gr.Button("Analyze Risk", variant="primary")
            
        # Output Section
        with gr.Column():
            gr.Markdown("### 📊 Risk Assessment")
            risk_output = gr.JSON(label="Analysis Results")
            
            with gr.Accordion("🛡️ Safety Recommendations", open=False):
                gr.Markdown("""
                - Move to higher ground if risk is HIGH
                - Prepare emergency supplies
                - Monitor local authorities' instructions
                """)
    
    # Examples Section
    gr.Markdown("### 🧪 Try Example Scenarios")
    gr.Examples(
        examples=[
            ["Dhaka, Bangladesh", 4.5, 200, True],
            ["Lahore, Pakistan", 2.1, 80, False],
            ["Mumbai, India", 3.8, 300, True]
        ],
        inputs=[location, water_level, rainfall, historical],
        label="Click any example to load"
    )
    
    # Footer
    gr.Markdown(f"""
    <div style='text-align: center; color: #666'>
        <p>Last Updated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}</p>
        <p>Powered by Groq LPU • Model: {MODEL}</p>
    </div>
    """)
    
    # Event Handling
    submit_btn.click(
        fn=analyze_flood,
        inputs=[location, water_level, rainfall, historical],
        outputs=risk_output
    )

# Required for Hugging Face Spaces
app.launch(debug=True)