Update app.py

app.py

@@ -38,45 +38,54 @@ tools = [
 # ----------------------
 # Define chatbot class
 # ----------------------
+from langchain.agents import initialize_agent, AgentType
+
 class cbfs:
-    def __init__(self, tools, openai_key: str, tavily_key: str):
-        if not openai_key or not tavily_key:
-            raise ValueError("⚠️ Please provide both OpenAI and Tavily API keys.")
+    def __init__(self, tools, openai_key: str, tavily_key: str = None):
+        if not openai_key:
+            raise ValueError("⚠️ Please provide an OpenAI API key.")
 
-        # Initialize OpenAI model with user key
+        # Initialize OpenAI model
         self.model = ChatOpenAI(temperature=0, openai_api_key=openai_key)
 
-        # Initialize Tavily client (for future tools or expansion)
-        self.tavily = TavilyClient(api_key=tavily_key)
+        # Initialize Tavily (optional)
+        self.tavily = TavilyClient(api_key=tavily_key) if tavily_key else None
 
-        # Memory + prompt
-        self.memory = ConversationBufferMemory(return_messages=True, memory_key="chat_history", ai_prefix="Assistant")
-        self.prompt = ChatPromptTemplate.from_messages([
-            ("system", "You are a helpful but sassy assistant. Remember what the user tells you in the conversation."),
-            MessagesPlaceholder(variable_name="chat_history"),
-            ("user", "{input}"),
-            MessagesPlaceholder(variable_name="agent_scratchpad")
-        ])
+        # Memory
+        self.memory = ConversationBufferMemory(
+            return_messages=True, memory_key="chat_history", ai_prefix="Assistant"
+        )
 
-        # Initialize agent
+        # Agent
         self.chain = initialize_agent(
             tools=tools,
             llm=self.model,
-            agent="zero-shot-react-description",
+            agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,  # ✅ correct way
             verbose=True,
-            memory=self.memory
+            memory=self.memory,
+            handle_parsing_errors=True  # ✅ prevents silent failure
         )
     
     def convchain(self, query: str) -> str:
-        """Run a single query through the agent."""
         if not query:
             return "Please enter a query."
         try:
             result = self.chain.invoke({"input": query})
-            response = result.get("output", "No response generated.")
+            # Debugging: show full raw result
+            print("Agent raw result:", result)
+
+            # Try both possible output keys
+            response = (
+                result.get("output")
+                or result.get("output_text")
+                or "⚠️ No response generated."
+            )
+
+            # Save memory
             self.memory.save_context({"input": query}, {"output": response})
             return response
         except Exception as e:
+            print("Execution Error:", str(e))
             return f"❌ Error: {str(e)}"