Update app.py

app.py

@@ -1,4 +1,5 @@
 import streamlit as st
+
 from langchain_groq import ChatGroq
 from langchain.chains import LLMChain
 from langchain.prompts import PromptTemplate
@@ -6,130 +7,76 @@ from langchain_community.utilities import WikipediaAPIWrapper
 from langchain.agents.agent_types import AgentType
 from langchain.agents import Tool, initialize_agent
 from langchain.callbacks import StreamlitCallbackHandler
-import datetime # Import datetime to add current date context
 
-# Set up Streamlit page configuration
 st.set_page_config(page_title="General Knowledge Assistant", page_icon="🧭")
 st.title("General Knowledge Assistant")
 
-# API Key input for Groq
 groq_api_key = st.sidebar.text_input(label="Groq API Key", type="password")
 
 if not groq_api_key:
     st.info("Please add your Groq API key to continue")
     st.stop()
 
-# Initialize the LLM (Using the model from your original code)
-# You might consider trying 'llama3-70b-8192' if Maverick struggles with tool selection
 llm = ChatGroq(model="meta-llama/llama-4-maverick-17b-128e-instruct", groq_api_key=groq_api_key)
 
-# Initialize Wikipedia tool for information retrieval
-wikipedia_wrapper = WikipediaAPIWrapper(top_k_results=2, doc_content_chars_max=2000) # Limit results slightly
+wikipedia_wrapper = WikipediaAPIWrapper()
 wikipedia_tool = Tool(
-    name="Wikipedia Search", # Renamed slightly for clarity
+    name="Wikipedia",
     func=wikipedia_wrapper.run,
-    description=(
-        "Use this tool to find specific information, facts, or details about people, places, events, or topics. "
-        "It is especially useful for getting CURRENT and UP-TO-DATE information or checking facts that might change over time. "
-        "Input should be a clear search query."
-    )
+    description="Use this tool to fetch updated information from the internet when your base knowledge is outdated or incomplete."
 )
 
-# Prompt template for general knowledge questions (used by the Reasoning Tool)
-# Added a note about the current date to potentially help the LLM contextualize recency
-current_date = datetime.datetime.now().strftime("%Y-%m-%d")
-prompt_text = f"""
-You are a knowledgeable assistant. The current date is {current_date}.
-Your task is to answer the user's questions accurately using your general knowledge.
-If you are asked to write an essay, please provide a title for the essay.
-Your information should be accurate and up-to-date based on your internal knowledge cutoff.
-If you suspect your internal knowledge might be outdated for the question, mention that the information might not be the absolute latest.
-
-Question: {{question}}
+prompt = """
+You are a knowledgeable assistant. Your task is to answer the user's questions accurately with your general knowledge.
+If you detect that your stored information is outdated or missing recent details, immediately search Wikipedia for updated info.
+Always ensure your answer is up to date. Whenever I tell you to write essay give a title also to the essay.
+
+Question: {question}
 Answer:
 """
 
-# Initialize the prompt template
 prompt_template = PromptTemplate(
     input_variables=["question"],
-    template=prompt_text
+    template=prompt
 )
 
-# Create the LLMChain for the Reasoning tool
 chain = LLMChain(llm=llm, prompt=prompt_template)
 
-# Reasoning tool for logic-based or factual questions
 reasoning_tool = Tool(
-    name="General Knowledge and Reasoning", # Renamed slightly for clarity
+    name="Reasoning tool",
     func=chain.run,
-    description=(
-        "Use this tool to answer general knowledge questions, perform reasoning tasks, or explain concepts based on the AI's internal knowledge base. "
-        "This tool relies on the AI's trained data, which might have a knowledge cut-off date. Do NOT use this tool if the question likely requires very recent information (use Wikipedia Search instead)."
-    )
+    description="A tool for answering general knowledge questions using logical reasoning and factual information. Use Wikipedia if your answer might be outdated."
 )
 
-# Initialize the agent with the tools and LLM
-# Ensure verbose=False and handle_parsing_errors=True as per your original code
 assistant_agent = initialize_agent(
     tools=[wikipedia_tool, reasoning_tool],
     llm=llm,
     agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
-    verbose=False, # Set to True temporarily if you need to debug the agent's thought process
-    handle_parsing_errors=True,
-    # Add max_iterations to prevent potential infinite loops if the agent gets stuck
-    max_iterations=5,
-    early_stopping_method="generate" # Stop generating if it thinks it's done
+    verbose=False,
+    handle_parsing_errors=True
 )
 
-# Initialize session state for message history if it doesn't exist
 if "messages" not in st.session_state:
     st.session_state["messages"] = [
         {"role": "assistant", "content": "Hi, I'm your general knowledge assistant. Feel free to ask me any question!"}
     ]
 
-# Display the conversation history
 for msg in st.session_state.messages:
     st.chat_message(msg["role"]).write(msg['content'])
 
-# Get the user's question (using st.chat_input for better UI)
-if user_query := st.chat_input("Please enter your general knowledge question here"):
-    # Add user message to state and display it
-    st.session_state.messages.append({"role": "user", "content": user_query})
-    st.chat_message("user").write(user_query)
-
-    # Format the input for the agent - pass only the latest user query
-    # Include conversation history *if* the agent type supports it well,
-    # but ZERO_SHOT_REACT_DESCRIPTION primarily focuses on the latest input.
-    # We will pass only the user_query for simplicity and correctness with .run()
-    agent_input = user_query
-
-    # Add context about the conversation history potentially (optional, advanced)
-    # agent_input = f"Previous conversation:\n{st.session_state.messages}\n\n Current question: {user_query}"
-
-    # Generate and display response
-    with st.chat_message("assistant"):
-        st_cb = StreamlitCallbackHandler(st.container(), expand_new_thoughts=False)
-        # *** FIX: Pass only the user_query string to the agent's run method ***
-        response = assistant_agent.run(agent_input, callbacks=[st_cb])
-        st.session_state.messages.append({"role": "assistant", "content": response})
-        st.write(response) # Display the final response
-
-# Note: Removed the text_area + button combo in favor of st.chat_input for a cleaner chat interface.
-# If you prefer the text_area and button:
-# user_question = st.text_area("Enter your question:", "Please enter your general knowledge question here", key="user_q_input")
-# if st.button("Find my answer", key="submit_q"):
-#     if user_question and user_question != "Please enter your general knowledge question here":
-#         # Add user message to state and display it
-#         st.session_state.messages.append({"role": "user", "content": user_question})
-#         st.chat_message("user").write(user_question)
-
-#         agent_input = user_question # Use the text_area content
-
-#         # Generate and display response
-#         with st.chat_message("assistant"):
-#             st_cb = StreamlitCallbackHandler(st.container(), expand_new_thoughts=False)
-#             response = assistant_agent.run(agent_input, callbacks=[st_cb])
-#             st.session_state.messages.append({"role": "assistant", "content": response})
-#             st.write(response)
-#     else:
-#         st.warning("Please enter a question.")
+question = st.text_area("Enter your question:", "Please enter your general knowledge question here")
+
+if st.button("find my answer"):
+    if question:
+        with st.spinner("Generate response.."):
+            st.session_state.messages.append({"role": "user", "content": question})
+            st.chat_message("user").write(question)
+            
+            st_cb = StreamlitCallbackHandler(st.container(), expand_new_thoughts=False)
+            response = assistant_agent.run(st.session_state.messages, callbacks=[st_cb])
+            
+            st.session_state.messages.append({"role": "assistant", "content": response})
+            st.write("### Response:")
+            st.success(response)
+    else:
+        st.warning("Please enter the question")