Update app.py

app.py

@@ -141,33 +141,28 @@ def init_db():
         conn.commit()
 
 def initialize_llm():
-    """Initialize the language model with fallbacks"""
-    models = [
-        "google/flan-t5-large",  # Smaller, reliable model
-        "facebook/opt-350m",     # Another reliable option
-        "gpt2",                  # Basic but stable
-    ]
-    
-    for model in models:
-        try:
-            print(f"Trying to initialize model: {model}")
-            api_token = os.environ.get('HF_TOKEN')
-            
-            llm = HuggingFaceHub(
-                repo_id=model,
-                huggingfacehub_api_token=api_token
-            )
-            
-            # Test the model
-            test_response = llm("Hello, this is a test.")
-            print(f"Successfully initialized {model}")
-            return llm
-            
-        except Exception as e:
-            print(f"Failed to initialize {model}: {str(e)}")
-            continue
-            
-    raise ValueError("Failed to initialize any model")
+    """Initialize the model in the simplest way possible"""
+    try:
+        # Get API token
+        api_token = os.environ.get('HF_TOKEN')
+        if not api_token:
+            raise ValueError("No API token found")
+
+        # Initialize with minimal settings
+        llm = HuggingFaceHub(
+            repo_id="google/flan-t5-base",  # Using a simpler model for testing
+            huggingfacehub_api_token=api_token
+        )
+
+        # Quick test
+        test = llm("Say hello")
+        print(f"Test response: {test}")
+
+        return llm
+
+    except Exception as e:
+        print(f"LLM initialization error: {str(e)}")
+        raise
 
         
 class ChatSession:
@@ -476,43 +471,38 @@ def get_chat_list():
 @app.route("/api/chat", methods=["POST"])
 def chat():
     try:
+        # Get the user's message
         data = request.json
         user_input = data.get("message", "")
-        session_id = data.get("sessionId", "default")
-
         print(f"Received message: {user_input}")
 
-        # Prepare the prompt
-        prompt = f"""Please provide a helpful response to: {user_input}
-
-        Guidelines:
-        1. If code is needed, use ```python for code blocks
-        2. Be clear and concise
-        3. If you don't know something, say so
-        """
+        # Very simple prompt for testing
+        prompt = f"You are a helpful assistant. Keep your response short and simple. User says: {user_input}"
 
-        # Get response from LLM
         try:
+            # Get response directly from model
             response = llm(prompt)
-            print(f"Generated response: {response[:100]}...")
+            print(f"Raw response received: {response}")
 
+            # Return whatever response we get
             return jsonify({
                 "success": True,
                 "response": response
             })
 
         except Exception as e:
-            print(f"Error generating response: {str(e)}")
+            print(f"Model error: {str(e)}")
+            # Return a simple error message
             return jsonify({
                 "success": False,
-                "response": "Error generating response. Please try again."
+                "response": "Sorry, I couldn't generate a response right now."
             })
 
     except Exception as e:
-        print(f"Error in chat endpoint: {str(e)}")
+        print(f"Request error: {str(e)}")
         return jsonify({
             "success": False,
-            "response": "An error occurred. Please try again."
+            "response": "Sorry, there was a problem with your request."
         })