Update app.py

app.py

@@ -235,29 +235,28 @@ def stream_text_completion(prompt: str):
     except Exception as e:
         yield f"Error: {str(e)}"  # Handle errors gracefully
 
-def stream_image_completion(image_b64: str):
-    invoke_url = "https://ai.api.nvidia.com/v1/gr/meta/llama-3.2-90b-vision-instruct/chat/completions"
-    headers = {
-        "Authorization": f"Bearer {NVIDIA_API_KEY}",
-        "Accept": "text/event-stream"
-    }
-    payload = {
-        "model": "meta/llama-3.2-90b-vision-instruct",
-        "messages": [
-            {
-                "role": "user",
-                "content": f'What is in this image? <img src="data:image/png;base64,{image_b64}" />'
-            }
-        ],
-        "max_tokens": 512,
-        "temperature": 1.00,
-        "top_p": 1.00,
-        "stream": True
-    }
-    response = requests.post(invoke_url, headers=headers, json=payload, stream=True)
-    for line in response.iter_lines():
-        if line:
-            yield line.decode("utf-8") + "\n"
+def stream_text_completion(prompt: str):
+    from openai import OpenAI
+    client = OpenAI(
+        base_url="https://integrate.api.nvidia.com/v1",
+        api_key=NVIDIA_API_KEY
+    )
+    print(f"Using NVIDIA API Key: {NVIDIA_API_KEY}")  # Debugging
+
+    try:
+        completion = client.chat.completions.create(
+            model="meta/llama-3.1-405b-instruct",
+            messages=[{"role": "user", "content": prompt}],
+            temperature=0.2,
+            top_p=0.7,
+            max_tokens=1024,
+            stream=True
+        )
+        for chunk in completion:
+            if chunk.choices[0].delta.content is not None:
+                yield chunk.choices[0].delta.content
+    except Exception as e:
+        yield f"Error: {str(e)}"  # Handle errors gracefully
 
 # --- Helper Function for Order Tracking ---
 async def log_order_tracking(order_id: str, status: str, message: str = None):
@@ -706,6 +705,7 @@ app = FastAPI()
 async def on_startup():
     await init_db()
 
+@app.post("/chatbot")
 @app.post("/chatbot")
 async def chatbot_response(request: Request, background_tasks: BackgroundTasks):
     data = await request.json()
@@ -731,38 +731,10 @@ async def chatbot_response(request: Request, background_tasks: BackgroundTasks):
     background_tasks.add_task(log_sentiment, user_id, user_message, sentiment_score)
     sentiment_modifier = "Great to hear from you! " if sentiment_score > 0.3 else ""
 
-    # --- Order Flow Handling ---
-   # In the chatbot_response function
-    if is_order_intent(user_message) or (user_id in user_state and user_state[user_id].flow == "order"):
-        order_response = await process_order_flow(user_id, user_message)
-        if order_response:
-            background_tasks.add_task(log_chat_to_db, user_id, "outbound", order_response)
-            conversation_context[user_id].append({
-                "timestamp": datetime.utcnow().isoformat(),
-                "role": "bot",
-                "message": order_response
-            })
-            return JSONResponse(content={"response": sentiment_modifier + order_response})
-
-    # --- Fallback to NVIDIA LLM ---
-    recent_context = conversation_context.get(user_id, [])[-5:]  # Get the last 5 messages
-    response_stream = stream_text_completion(user_message, recent_context)
-    fallback_response = "".join([chunk for chunk in response_stream])
-
-    # Log the bot's response
-    background_tasks.add_task(log_chat_to_db, user_id, "outbound", fallback_response)
-    conversation_context[user_id].append({
-        "timestamp": datetime.utcnow().isoformat(),
-        "role": "bot",
-        "message": fallback_response
-    })
-
-    return JSONResponse(content={"response": sentiment_modifier + fallback_response})
-
-    # --- Menu Display ---
-    if "menu" in user_message.lower():
+    # --- Handle Menu Selection ---
+    if user_message.strip() == "1" or "menu" in user_message.lower():
         if user_id in user_state:
-            del user_state[user_id]
+            del user_state[user_id]  # Clear any existing state
         menu_with_images = []
         for index, item in enumerate(menu_items, start=1):
             image_url = google_image_scrape(item["name"])
@@ -790,57 +762,34 @@ async def chatbot_response(request: Request, background_tasks: BackgroundTasks):
         })
         return JSONResponse(content=response_payload)
 
-    # --- Dish Selection via Menu ---
-    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)):
-        selected_dish = None
-        if user_message.strip().isdigit():
-            dish_number = int(user_message.strip())
-            if 1 <= dish_number <= len(menu_items):
-                selected_dish = menu_items[dish_number - 1]["name"]
-        else:
-            for item in menu_items:
-                if item["name"].lower() in user_message.lower():
-                    selected_dish = item["name"]
-                    break
-        if selected_dish:
-            state = ConversationState()
-            state.flow = "order"
-            # Set step to 2 since the dish is already selected
-            state.step = 2  
-            state.data["dish"] = selected_dish
-            state.update_last_active()
-            user_state[user_id] = state
-            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)
-            conversation_context[user_id].append({
-                "timestamp": datetime.utcnow().isoformat(),
-                "role": "bot",
-                "message": 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)
+    # --- Order Flow Handling ---
+    if is_order_intent(user_message) or (user_id in user_state and user_state[user_id].flow == "order"):
+        order_response = await process_order_flow(user_id, user_message)
+        if order_response:
+            background_tasks.add_task(log_chat_to_db, user_id, "outbound", order_response)
             conversation_context[user_id].append({
                 "timestamp": datetime.utcnow().isoformat(),
                 "role": "bot",
-                "message": response_text
+                "message": order_response
             })
-            return JSONResponse(content={"response": sentiment_modifier + response_text})
-
-   
+            return JSONResponse(content={"response": sentiment_modifier + order_response})
 
-    # --- Fallback: LLM Response Streaming with Conversation Context ---
-    recent_context = conversation_context.get(user_id, [])[-5:]
+    # --- Fallback to NVIDIA LLM ---
+    recent_context = conversation_context.get(user_id, [])[-5:]  # Get the last 5 messages
     context_str = "\n".join([f"{entry['role'].capitalize()}: {entry['message']}" for entry in recent_context])
     prompt = f"Conversation context:\n{context_str}\nUser query: {user_message}\nGenerate a helpful, personalized response for a restaurant chatbot."
-    def stream_response():
-        for chunk in stream_text_completion(prompt):
-            yield chunk
-    fallback_log = f"LLM fallback response for prompt: {prompt}"
-    background_tasks.add_task(log_chat_to_db, user_id, "outbound", fallback_log)
-    return StreamingResponse(stream_response(), media_type="text/plain")
+    response_stream = stream_text_completion(prompt)  # Pass only the prompt
+    fallback_response = "".join([chunk for chunk in response_stream])
+
+    # Log the bot's response
+    background_tasks.add_task(log_chat_to_db, user_id, "outbound", fallback_response)
+    conversation_context[user_id].append({
+        "timestamp": datetime.utcnow().isoformat(),
+        "role": "bot",
+        "message": fallback_response
+    })
+
+    return JSONResponse(content={"response": sentiment_modifier + fallback_response})
 
 # --- Other Endpoints (Chat History, Order Details, User Profile, Analytics, Voice, Payment Callback) ---
 @app.get("/chat_history/{user_id}")