Update app.py

app.py

@@ -2,12 +2,19 @@ import gradio as gr
 from groq import Groq
 import os
 
-
+# Get Groq API key from environment
 api_key = os.environ.get("GROQ_API_KEY")
+
+
+# Initialize Groq client
 client = Groq(api_key=api_key)
 
 def get_recommendation(quantity, food_type, expiry_hours):
-    # Basic logic (fast + reliable)
+    """
+    Returns a recommended recipient for surplus food along with AI explanation.
+    """
+
+    # Basic routing logic
     if quantity < 20:
         base = "Elderly Individuals"
     elif quantity < 100:
@@ -15,35 +22,45 @@ def get_recommendation(quantity, food_type, expiry_hours):
     else:
         base = "NGOs / Refugee Centers"
 
-    # AI explanation
+    # Prompt for AI explanation
     prompt = f"""
-    A restaurant has:
-    - {quantity} meals
-    - Food type: {food_type}
-    - Expiry in {expiry_hours} hours
+A restaurant has:
+- {quantity} meals
+- Food type: {food_type}
+- Expiry in {expiry_hours} hours
 
-    The system selected: {base}
+The system selected: {base}
 
-    Explain why this is the best recipient in 2-3 lines.
-    """
+Explain why this is the best recipient in 2-3 lines.
+Output format:
+Recommended recipient: <recipient>
+Reason: <AI explanation>
+"""
 
-    response = client.chat.completions.create(
-        model="llama-3.3-70b-versatile",
-        messages=[{"role": "user", "content": prompt}]
-    )
+    # Call Groq AI with retry logic
+    try:
+        response = client.chat.completions.create(
+            model="llama-3.3-70b-versatile",
+            messages=[{"role": "user", "content": prompt}]
+        )
+    except Exception as e:
+        return f"❌ AI request failed:\n{str(e)}"
 
-    explanation = response.choices[0].message.content
+    # Safely extract AI explanation
+    try:
+        explanation = response.choices[0].message.content
+    except (AttributeError, IndexError):
+        explanation = "❌ No AI explanation returned."
 
     return f"✅ Recommended: {base}\n\n🤖 AI Reasoning:\n{explanation}"
 
-
 # Gradio UI
 demo = gr.Interface(
     fn=get_recommendation,
     inputs=[
-        gr.Number(label="Number of Meals"),
+        gr.Number(label="Number of Meals", value=10),
         gr.Dropdown(["halal", "soft", "packaged"], label="Food Type"),
-        gr.Number(label="Hours Until Expiry")
+        gr.Number(label="Hours Until Expiry", value=5)
     ],
     outputs="text",
     title="🍽️ NourishNet AI",