app.py

import os
from langchain_core.prompts import ChatPromptTemplate
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.prompts import MessagesPlaceholder
from langchain.memory import ConversationBufferWindowMemory
from operator import itemgetter
from langchain_core.runnables import RunnableLambda,RunnablePassthrough
import streamlit as st

genai_key = os.getenv("gen_key")

model = ChatGoogleGenerativeAI(temperature=0,model='gemini-1.5-pro',max_output_tokens=150,convert_system_message_to_human=True,google_api_key=genai_key)

prompt=ChatPromptTemplate.from_messages([
     ("system","you are a good assistant"),
     MessagesPlaceholder(variable_name="history"),
     ("human","{input}")])


memory=ConversationBufferWindowMemory(k=3,return_messages=True)

chain=(RunnablePassthrough.assign(history=RunnableLambda(memory.load_memory_variables)|
       itemgetter("history"))|prompt|model)

# Streamlit interface
st.title("chat bot")
st.write("Enter your input text:")


def end_conv():
    st.write("Conversation ended.")
    memory.clear()
user_input = st.text_area("Input Text")
for i in range(1,5):
    
# Generate and display the response
    if st.button("Generate Response"):
    # Load current conversation history
        history = memory.load_memory_variables({})['history']

    # Invoke the chain to get the response
        res = chain.invoke({"input": user_input})
        response_content = res.content
        st.write("Generated Response:")
        st.write(response_content)

        # Save the context in memory
        memory.save_context({"input": user_input}, {"output": response_content})

        # Display the conversation history
    #st.write("Conversation History:")
    #for msg in memory.buffer:
    #    st.write(f"{msg['role']}: {msg['content']}")

    # Optionally, you can also add a button to reset the conversation history
    if st.button("End Conversation"):
        end_conv()