Update app.py

app.py

@@ -1,94 +1,98 @@
-import streamlit as st
-from langchain_groq import ChatGroq
-from langchain.chains import LLMChain
-from langchain.prompts import PromptTemplate
-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
-
-# 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 (Groq API - llama-3.1-70b)
-llm = ChatGroq(model="llama-3.1-70b-versatile", groq_api_key=groq_api_key)
-
-# Initialize Wikipedia tool for information retrieval
-wikipedia_wrapper = WikipediaAPIWrapper()
-wikipedia_tool = Tool(
-    name="Wikipedia",
-    func=wikipedia_wrapper.run,
-    description="A tool for searching the Internet to find information on various topics, including general knowledge."
-)
-
-# Prompt template for general knowledge questions
-prompt = """
-You are a knowledgeable assistant. Your task is to answer the user's questions accurately, using your general knowledge.
-If the answer is not readily available in your knowledge base, search Wikipedia for relevant information.
-Your information should be accurate and 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
-)
-
-# Combine all the tools into a chain for answering general knowledge questions
-chain = LLMChain(llm=llm, prompt=prompt_template)
-
-# Reasoning tool for logic-based or factual questions
-reasoning_tool = Tool(
-    name="Reasoning tool",
-    func=chain.run,
-    description="A tool for answering general knowledge questions using logical reasoning and factual information.Try to use the latest information"
-)
-
-# Initialize the agent with the tools and LLM
-assistant_agent = initialize_agent(
-    tools=[wikipedia_tool, reasoning_tool],
-    llm=llm,
-    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
-    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
-question = st.text_area("Enter your question:", "Please enter your general knowledge question here")
-
-# Handle the button click to process the question
-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")
+import os
+from dotenv import load_dotenv
+import streamlit as st
+from langchain_groq import ChatGroq
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+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
+
+# Load environment variables from the .env file
+load_dotenv()
+
+# Get API key from environment variables
+groq_api_key = os.getenv("GROQ_API_KEY")
+
+# If the API key is not set, show an info message and stop execution
+if not groq_api_key:
+    st.info("Please set your Groq API key in the .env file to continue")
+    st.stop()
+
+# Set up Streamlit page configuration
+st.set_page_config(page_title="General Knowledge Assistant", page_icon="🧭")
+st.title("General Knowledge Assistant")
+
+# Initialize the LLM (Groq API - llama-3.1-70b)
+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()
+wikipedia_tool = Tool(
+    name="Wikipedia",
+    func=wikipedia_wrapper.run,
+    description="A tool for searching the Internet to find information on various topics, including general knowledge."
+)
+
+# Prompt template for general knowledge questions
+prompt = """
+You are a knowledgeable assistant. Your task is to answer the user's questions accurately, using your general knowledge.
+If the answer is not readily available in your knowledge base, search Wikipedia for relevant information.
+Your information should be accurate and 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
+)
+
+# Combine all the tools into a chain for answering general knowledge questions
+chain = LLMChain(llm=llm, prompt=prompt_template)
+
+# Reasoning tool for logic-based or factual questions
+reasoning_tool = Tool(
+    name="Reasoning tool",
+    func=chain.run,
+    description="A tool for answering general knowledge questions using logical reasoning and factual information. Try to use the latest information"
+)
+
+# Initialize the agent with the tools and LLM
+assistant_agent = initialize_agent(
+    tools=[wikipedia_tool, reasoning_tool],
+    llm=llm,
+    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+    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
+question = st.text_area("Enter your question:", "Please enter your general knowledge question here")
+
+# Handle the button click to process the question
+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")