Update app.py

app.py

@@ -2,51 +2,67 @@ import gradio as gr
 import requests
 import os
 
-# Groq API Setup (Replace with your API Key)
-GROQ_API_KEY = os.getenv("GROQ_API_KEY")  # Hugging Face Secrets se set karo
-GROQ_API_URL = "https://api.groq.com/openai/v1/chat/completions"
+# Initialize Groq API
+GROQ_API_KEY = os.environ.get("GROQ_API_KEY")
+if not GROQ_API_KEY:
+    raise ValueError("❌ Groq API Key not found! Please set it in Hugging Face Secrets")
 
-# Flood Prediction via Groq AI
-def predict_flood_risk(location, water_level, rainfall):
+def get_flood_warning(location, water_level, rainfall):
+    """Get flood prediction from Groq AI"""
     prompt = f"""
-    Flood Risk Assessment:
+    Analyze flood risk based on:
     - Location: {location}
-    - Current Water Level: {water_level} meters
-    - Last 24h Rainfall: {rainfall} mm
-    - Historical Data: Moderate flood risk zone
-
-    Question: Should we issue a flood warning? 
-    Reply format: "VERDICT: [HIGH/MEDIUM/LOW] | REASON: [1-line explanation]"
+    - 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>"
     """
-
-    headers = {
-        "Authorization": f"Bearer {GROQ_API_KEY}",
-        "Content-Type": "application/json"
-    }
-    payload = {
-        "messages": [{"role": "user", "content": prompt}],
-        "model": "mixtral-8x7b-32768"  # Groq's fast model
-    }
-
-    response = requests.post(GROQ_API_URL, headers=headers, json=payload)
-    if response.status_code == 200:
+    
+    try:
+        headers = {
+            "Authorization": f"Bearer {GROQ_API_KEY}",
+            "Content-Type": "application/json"
+        }
+        payload = {
+            "messages": [{"role": "user", "content": prompt}],
+            "model": "mixtral-8x7b-32768"
+        }
+        response = requests.post(
+            "https://api.groq.com/openai/v1/chat/completions",
+            headers=headers,
+            json=payload
+        )
         return response.json()["choices"][0]["message"]["content"]
-    else:
-        return f"Error: {response.text}"
+    except Exception as e:
+        return f"Error: {str(e)}"
 
-# Gradio UI
-with gr.Blocks(title="Flood Alert AI") as app:
-    gr.Markdown("## 🌊 Flood Warning System (Powered by Groq AI)")
-    with gr.Row():
-        location = gr.Textbox(label="📍 Location (e.g., Delhi, India)")
-        water_level = gr.Slider(label="💧 Water Level (meters)", minimum=0, maximum=10)
-        rainfall = gr.Slider(label="🌧️ 24h Rainfall (mm)", minimum=0, maximum=500)
-    submit_btn = gr.Button("🚨 Check Flood Risk")
+# Create Gradio Interface
+with gr.Blocks(theme=gr.themes.Soft(), title="Flood Prediction System") as app:
+    gr.Markdown("# 🌊 AI Flood Warning System")
+    gr.Markdown("Predict flood risks using Groq AI and real-time data")
     
-    output = gr.Textbox(label="⚠️ AI Alert", interactive=False)
+    with gr.Row():
+        with gr.Column():
+            location = gr.Textbox(label="📍 Location Name", placeholder="Enter city/village name")
+            water_level = gr.Slider(label="💧 Water Level (meters)", minimum=0, maximum=15, step=0.1)
+            rainfall = gr.Slider(label="🌧️ 24h Rainfall (mm)", minimum=0, maximum=300)
+            submit_btn = gr.Button("Analyze Flood Risk", variant="primary")
+        
+        with gr.Column():
+            output = gr.Textbox(label="⚠️ Flood Alert", interactive=False)
+            gr.Examples(
+                examples=[
+                    ["Delhi", 4.5, 120],
+                    ["Mumbai", 2.1, 250],
+                    ["Chennai", 1.8, 80]
+                ],
+                inputs=[location, water_level, rainfall]
+            )
     
     submit_btn.click(
-        fn=predict_flood_risk,
+        fn=get_flood_warning,
         inputs=[location, water_level, rainfall],
         outputs=output
     )