app.py

from langchain_community.chat_message_histories import StreamlitChatMessageHistory
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.runnables.history import RunnableWithMessageHistory
from langchain_google_genai import GoogleGenerativeAIimport
import os
from dotenv import load_dotenv


import streamlit as st


load_dotenv()
api_key=os.getenv("GOOGLE_API_KEY")
# Configure l'API de Gemini
#llm = GoogleGenerativeAI(model="models/text-bison-001", google_api_key=api_key)
#genai.configure(api_key=api_key)


st.set_page_config(page_title="StreamlitChatMessageHistory", page_icon="📖")
st.title("📖 StreamlitChatMessageHistory")

"""
A basic example of using StreamlitChatMessageHistory to help LLMChain remember messages in a conversation.
The messages are stored in Session State across re-runs automatically. You can view the contents of Session State
in the expander below. View the
[source code for this app](https://github.com/langchain-ai/streamlit-agent/blob/main/streamlit_agent/basic_memory.py).
"""

# Set up memory
msgs = StreamlitChatMessageHistory(key="langchain_messages")
if len(msgs.messages) == 0:
    msgs.add_ai_message("How can I help you?")

view_messages = st.expander("View the message contents in session state")



# Set up the LangChain, passing in Message History

prompt = ChatPromptTemplate.from_messages(
    [
        ("system", "You are an AI chatbot having a conversation with a human."),
        MessagesPlaceholder(variable_name="history"),
        ("human", "{question}"),
    ]
)

chain = prompt | GoogleGenerativeAI(model="models/gemini-2.0-flash-exp", google_api_key=api_key)



chain_with_history = RunnableWithMessageHistory(
    chain,
    lambda session_id: msgs,
    input_messages_key="question",
    history_messages_key="history",
)

# Render current messages from StreamlitChatMessageHistory
for msg in msgs.messages:
    st.chat_message(msg.type).write(msg.content)

# If user inputs a new prompt, generate and draw a new response
if prompt := st.chat_input():
    st.chat_message("human").write(prompt)
    # Note: new messages are saved to history automatically by Langchain during run
    config = {"configurable": {"session_id": "any"}}
    response = chain_with_history.invoke({"question": prompt}, config)
    st.chat_message("ai").write(response.content)

# Draw the messages at the end, so newly generated ones show up immediately
with view_messages:
    """
    Message History initialized with:
    ```python
    msgs = StreamlitChatMessageHistory(key="langchain_messages")
    ```

    Contents of `st.session_state.langchain_messages`:
    """
    view_messages.json(st.session_state.langchain_messages)