Update app.py

app.py

@@ -2,58 +2,42 @@ import gradio as gr
 import os
 import requests
 import json
-from datetime import datetime
 
 # Configuration
 GROQ_API_KEY = os.getenv("GROQ_API_KEY")
 MODEL = "llama3-70b-8192"
 API_URL = "https://api.groq.com/openai/v1/chat/completions"
 
-def get_risk_color(risk_level):
-    """Return color based on risk level"""
-    colors = {
-        "HIGH": "red",
-        "MEDIUM": "orange",
-        "LOW": "green",
-        "ERROR": "gray"
-    }
-    return colors.get(risk_level.upper(), "gray")
-
-def analyze_flood(location, water_level, rainfall, historical_data):
-    """Enhanced flood analysis with structured output"""
+def analyze_flood_risk(country, city, area, water_level, rainfall):
+    """Detailed flood analysis based on location"""
     prompt = f"""
-    As a hydrology expert, analyze flood risk for:
-    Location: {location}
-    Water Level: {water_level}m
-    Rainfall: {rainfall}mm
-    Historical Data: {'Available' if historical_data else 'Not available'}
+    As a flood risk expert, analyze:
+    - Country: {country}
+    - City: {city}
+    - Area: {area}
+    - Water Level: {water_level} meters
+    - 24h Rainfall: {rainfall} mm
     
     Respond in JSON format with:
-    - risk_level (HIGH/MEDIUM/LOW)
-    - summary (1 sentence)
-    - detailed_analysis (3-5 bullet points)
-    - recommended_actions (3 bullet points)
-    - confidence (percentage)
+    - risk_level: <HIGH/MEDIUM/LOW>
+    - summary: <1 sentence>
+    - affected_areas: <list>
+    - precautions: <3 bullet points>
+    - emergency_contacts: <local authorities>
     """
     
     try:
         response = requests.post(
             API_URL,
-            headers={
-                "Authorization": f"Bearer {GROQ_API_KEY}",
-                "Content-Type": "application/json"
-            },
+            headers={"Authorization": f"Bearer {GROQ_API_KEY}"},
             json={
                 "model": MODEL,
                 "messages": [
                     {
                         "role": "system",
-                        "content": "You are a flood risk analysis AI. Respond in valid JSON format."
+                        "content": "You are a local flood analysis expert. Consider regional geography."
                     },
-                    {
-                        "role": "user",
-                        "content": prompt
-                    }
+                    {"role": "user", "content": prompt}
                 ],
                 "response_format": {"type": "json_object"}
             },
@@ -62,86 +46,67 @@ def analyze_flood(location, water_level, rainfall, historical_data):
         response.raise_for_status()
         result = json.loads(response.json()["choices"][0]["message"]["content"])
         
-        # Format results
-        risk_level = result.get("risk_level", "UNKNOWN")
-        color = get_risk_color(risk_level)
+        # Determine color based on risk
+        color = "#ff0000" if result["risk_level"] == "HIGH" else "#ff9900" if result["risk_level"] == "MEDIUM" else "#00aa00"
         
-        return (
-            f"<span style='color: {color}; font-weight: bold'>{risk_level}</span>",
-            result.get("summary", "No summary available"),
-            "\n".join([f"• {point}" for point in result.get("detailed_analysis", [])]),
-            "\n".join([f"• {action}" for action in result.get("recommended_actions", [])]),
-            f"{result.get('confidence', 'N/A')}%"
-        )
+        # Format HTML output
+        return f"""
+        <div style='border-left: 5px solid {color}; padding-left: 10px'>
+            <h3>{area}, {city}, {country}</h3>
+            <p><b>Risk Level:</b> <span style='color:{color}'>{result['risk_level']}</span></p>
+            <p><b>Summary:</b> {result['summary']}</p>
+            
+            <h4>Affected Areas:</h4>
+            <ul>
+                {"".join([f"<li>{area}</li>" for area in result['affected_areas']])}
+            </ul>
+            
+            <h4>Precautions:</h4>
+            <ol>
+                {"".join([f"<li>{action}</li>" for action in result['precautions']])}
+            </ol>
+            
+            <h4>Emergency Contacts:</h4>
+            <ul>
+                {"".join([f"<li>{contact}</li>" for contact in result['emergency_contacts']])}
+            </ul>
+        </div>
+        """
         
     except Exception as e:
-        return (
-            "<span style='color: gray; font-weight: bold'>ERROR</span>",
-            f"Analysis failed: {str(e)}",
-            "Please try again later",
-            "Check your API key and connection",
-            "0%"
-        )
+        return f"<div style='color:red'>Error: {str(e)}</div>"
 
 # Gradio Interface
 with gr.Blocks(theme=gr.themes.Soft(), title="Flood Risk Analyzer") as app:
-    # Header
-    gr.Markdown("""
-    <div style='text-align: center'>
-        <h1>🌊 Flood Risk Assessment</h1>
-        <p>Instant flood risk analysis powered by Groq AI</p>
-    </div>
-    """)
+    gr.Markdown("# 🌊 Flood Risk Assessment")
+    gr.Markdown("Enter location details to analyze flood risk")
     
-    # Input Section
     with gr.Row():
         with gr.Column():
-            gr.Markdown("### 📍 Enter Location Details")
-            location = gr.Textbox(label="City/Region")
-            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")
+            country = gr.Textbox(label="Country", placeholder="e.g. Pakistan")
+            city = gr.Textbox(label="City", placeholder="e.g. Karachi")
+            area = gr.Textbox(label="Area/Neighborhood", placeholder="e.g. Clifton")
+            water_level = gr.Slider(0, 10, step=0.1, label="Water Level (meters)")
+            rainfall = gr.Slider(0, 500, label="24h Rainfall (mm)")
             submit_btn = gr.Button("Analyze Risk", variant="primary")
             
-    # Output Section
-    with gr.Row():
         with gr.Column():
-            gr.Markdown("### 🚨 Risk Overview")
-            risk_level = gr.HTML(label="Risk Level")
-            summary = gr.Textbox(label="Summary", interactive=False)
-            
-            with gr.Accordion("📊 Detailed Analysis", open=False):
-                details = gr.Markdown()
-                
-            with gr.Accordion("🛡️ Recommended Actions", open=False):
-                actions = gr.Markdown()
-                
-            confidence = gr.Textbox(label="Confidence Level", interactive=False)
+            output_html = gr.HTML(label="Analysis Results")
     
     # Examples
-    gr.Markdown("### 🧪 Example Scenarios")
     gr.Examples(
         examples=[
-            ["Karachi, Pakistan", 4.2, 180, True],
-            ["Delhi, India", 2.5, 90, False],
-            ["Dhaka, Bangladesh", 5.1, 250, True]
+            ["Pakistan", "Karachi", "Clifton", 3.5, 200],
+            ["Pakistan", "Lahore", "Iqbal Town", 2.0, 80],
+            ["India", "Mumbai", "Nariman Point", 4.0, 300]
         ],
-        inputs=[location, water_level, rainfall, historical],
-        label="Click any example to load"
+        inputs=[country, city, area, water_level, rainfall]
     )
     
-    # Footer
-    gr.Markdown(f"""
-    <div style='text-align: center; color: #666; margin-top: 20px'>
-        <p>Last update: {datetime.now().strftime('%Y-%m-%d %H:%M')} | Model: {MODEL}</p>
-    </div>
-    """)
-    
-    # Event Handling - Now returning a tuple instead of dict
     submit_btn.click(
-        fn=analyze_flood,
-        inputs=[location, water_level, rainfall, historical],
-        outputs=[risk_level, summary, details, actions, confidence]
+        fn=analyze_flood_risk,
+        inputs=[country, city, area, water_level, rainfall],
+        outputs=output_html
     )
 
 app.launch()