app.py

import streamlit as st
import os
from typing import Annotated
from typing_extensions import TypedDict
from langchain_community.utilities import ArxivAPIWrapper, WikipediaAPIWrapper
from langchain_community.tools import ArxivQueryRun, WikipediaQueryRun
from langgraph.graph.message import add_messages
from langgraph.graph import StateGraph, START, END
from langchain_groq import ChatGroq
from langgraph.prebuilt import ToolNode, tools_condition

# Initialize tools
arxiv_wrapper = ArxivAPIWrapper(top_k_results=1, doc_content_chars_max=300)
arxiv_tool = ArxivQueryRun(api_wrapper=arxiv_wrapper)
wiki_wrapper = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=300)
wiki_tool = WikipediaQueryRun(api_wrapper=wiki_wrapper)
tools = [wiki_tool, arxiv_tool]

# Define State
class State(TypedDict):
    messages: Annotated[list, add_messages]

# Initialize LLM
@st.cache_resource
def initialize_llm():
    groq_api_key = os.getenv("GROQ_API_KEY")
    if not groq_api_key:
        st.error("Please set the GROQ_API_KEY environment variable.")
        st.stop()
    return ChatGroq(groq_api_key=groq_api_key, model_name="Gemma2-9b-It")

llm = initialize_llm()
llm_with_tools = llm.bind_tools(tools=tools)

# Define chatbot function
def chatbot(state: State):
    return {"messages": [llm_with_tools.invoke(state["messages"])]}

# Build graph
@st.cache_resource
def build_graph():
    graph_builder = StateGraph(State)
    graph_builder.add_node("chatbot", chatbot)
    tool_node = ToolNode(tools=tools)
    graph_builder.add_node("tools", tool_node)
    graph_builder.add_conditional_edges("chatbot", tools_condition)
    graph_builder.add_edge("tools", "chatbot")
    graph_builder.add_edge(START, "chatbot")
    return graph_builder.compile()

graph = build_graph()

# Streamlit UI
st.title("WIKXIV AI: Wikipedia and ArXiv Chatbot")

# Initialize chat history
if "messages" not in st.session_state:
    st.session_state.messages = []

# Display chat messages from history on app rerun
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])

# React to user input
if prompt := st.chat_input("What is your question?"):
    # Display user message in chat message container
    st.chat_message("user").markdown(prompt)
    # Add user message to chat history
    st.session_state.messages.append({"role": "user", "content": prompt})
    
    # Get bot response
    events = graph.stream(
        {"messages": [("user", prompt)]},
        stream_mode="values"
    )
    
    # Display assistant response in chat message container
    with st.chat_message("assistant"):
        message_placeholder = st.empty()
        full_response = ""
        for event in events:
            message = event["messages"][-1]
            full_response += message.content
            message_placeholder.markdown(full_response + "▌")
        message_placeholder.markdown(full_response)
    
    # Add assistant response to chat history
    st.session_state.messages.append({"role": "assistant", "content": full_response})
    
# Display info about the app
st.sidebar.title("About")
st.sidebar.info("This is a Streamlit app made by theaimart\nthat uses LangGraph to create a chatbot with access to Wikipedia and ArXiv tools.")
# st.sidebar.title("made with love by theaimart")
#by theaimart