Update app.py

app.py

@@ -364,14 +364,14 @@ async def chatbot_response(request: Request, background_tasks: BackgroundTasks):
     elif sentiment_score > 0.3:
         sentiment_modifier = "Great to hear from you! "
 
-    # Check if this is an order flow request
+    # ========== REORDERED LOGIC ==========
+    # 1. Check if the user is already in an order flow
     order_response = process_order_flow(user_id, user_message)
     if order_response:
         background_tasks.add_task(log_chat_to_db, user_id, "outbound", order_response)
         return JSONResponse(content={"response": sentiment_modifier + order_response})
 
-
-    # Check for specialized commands:
+    # 2. Handle menu display
     if "menu" in user_message.lower():
         # Return menu with images and options for selection
         menu_with_images = []
@@ -396,6 +396,52 @@ async def chatbot_response(request: Request, background_tasks: BackgroundTasks):
         background_tasks.add_task(log_chat_to_db, user_id, "outbound", str(response_payload))
         return JSONResponse(content=response_payload)
 
+    # 3. Handle dish selection (ONLY if not in order flow)
+    if any(item["name"].lower() in user_message.lower() for item in menu_items) or \
+       any(str(index) == user_message.strip() for index, item in enumerate(menu_items, start=1)):
+        # Extract the selected dish
+        selected_dish = None
+        if user_message.strip().isdigit():
+            # User selected by number
+            dish_number = int(user_message.strip())
+            if 1 <= dish_number <= len(menu_items):
+                selected_dish = menu_items[dish_number - 1]["name"]
+        else:
+            # User selected by name
+            for item in menu_items:
+                if item["name"].lower() in user_message.lower():
+                    selected_dish = item["name"]
+                    break
+
+        if selected_dish:
+            # Trigger the order flow
+            user_state[user_id] = {"flow": "order", "step": 1, "data": {"dish": selected_dish}, "last_active": datetime.utcnow()}
+            response_text = f"You selected {selected_dish}. How many servings would you like?"
+            background_tasks.add_task(log_chat_to_db, user_id, "outbound", response_text)
+            return JSONResponse(content={"response": sentiment_modifier + response_text})
+        else:
+            response_text = "Sorry, I couldn't find that dish in the menu. Please try again."
+            background_tasks.add_task(log_chat_to_db, user_id, "outbound", response_text)
+            return JSONResponse(content={"response": sentiment_modifier + response_text})
+
+    # 4. Handle nutritional facts
+    if "nutritional facts for" in user_message.lower():
+        dish_name = user_message.lower().replace("nutritional facts for", "").strip().title()
+        dish = next((item for item in menu_items if item["name"].lower() == dish_name.lower()), None)
+        if dish:
+            response_text = f"Nutritional facts for {dish['name']}:\n{dish['nutrition']}"
+        else:
+            response_text = f"Sorry, I couldn't find nutritional facts for {dish_name}."
+        background_tasks.add_task(log_chat_to_db, user_id, "outbound", response_text)
+        return JSONResponse(content={"response": sentiment_modifier + response_text})
+
+    # 5. Fallback: use NVIDIA text LLM streaming for a response
+    prompt = f"User query: {user_message}\nGenerate a helpful, personalized response for a restaurant chatbot."
+    def stream_response():
+        for chunk in stream_text_completion(prompt):
+            yield chunk
+    background_tasks.add_task(log_chat_to_db, user_id, "outbound", f"LLM fallback response for prompt: {prompt}")
+    return StreamingResponse(stream_response(), media_type="text/plain")
         
 
     # Handle dish selection for ordering