Update app.py

app.py

@@ -1,58 +1,127 @@
 import gradio as gr
 import os
 import requests
+import json
+from datetime import datetime
 
-# Groq API configuration
+# Configuration - Use Hugging Face Secrets
 GROQ_API_KEY = os.getenv("GROQ_API_KEY")
-GROQ_API_URL = "https://api.groq.com/openai/v1/chat/completions"
-MODEL = "llama3-70b-8192"  # Safe, currently supported model from Groq
+MODEL = "llama3-70b-8192"
+API_URL = "https://api.groq.com/openai/v1/chat/completions"
 
-def get_flood_risk(location, water_level, rainfall):
+def analyze_flood(location, water_level, rainfall, historical_data):
+    """Enhanced flood analysis with error handling"""
     prompt = f"""
-    Analyze flood risk based on:
+    As FloodAI Expert, analyze:
     - Location: {location}
-    - Current water level: {water_level} meters
-    - 24-hour rainfall: {rainfall} mm
-    - Historical flood data for area
-
-    Provide output in this exact format:
-    "RISK: <HIGH/MEDIUM/LOW> | ALERT: <Warning message> | ACTION: <Recommended action>"
+    - 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)}"}
 
-    headers = {
-        "Authorization": f"Bearer {GROQ_API_KEY}",
-        "Content-Type": "application/json"
-    }
-
-    data = {
-        "model": MODEL,
-        "messages": [
-            {"role": "system", "content": "You are a flood risk analysis expert."},
-            {"role": "user", "content": prompt}
-        ]
-    }
-
-    response = requests.post(GROQ_API_URL, headers=headers, json=data)
-
-    if response.status_code == 200:
-        result = response.json()
-        message = result["choices"][0]["message"]["content"].strip()
-        return message
-    else:
-        return f"❌ Error: {response.status_code} - {response.text}"
-
-# Gradio UI
-iface = gr.Interface(
-    fn=get_flood_risk,
-    inputs=[
-        gr.Textbox(label="Location"),
-        gr.Number(label="Current Water Level (m)"),
-        gr.Number(label="24-hour Rainfall (mm)")
-    ],
-    outputs="text",
-    title="🌊 Flood Risk Predictor",
-    description="Enter location, water level and rainfall to get flood risk prediction using Groq AI"
-)
+# 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
+    )
 
-if __name__ == "__main__":
-    iface.launch()
+# Required for Hugging Face Spaces
+app.launch(debug=True)