Upload 3 files

app.py

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+import streamlit as st
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.output_parsers import StrOutputParser
+from langchain_community.chat_message_histories import SQLChatMessageHistory
+from langchain_core.runnables.history import RunnableWithMessageHistory
+import os
+
+API_KEY = os.getenv("api_key")
+
+template = ChatPromptTemplate(
+    messages=[
+        ("system", "You're a helpful data science AI chatbot. Answer only questions related to data science and what he told you within a 300-word limit."),
+        MessagesPlaceholder(variable_name="chat_history"),
+        ("human", "{input}") 
+    ]
+)
+
+model = ChatGoogleGenerativeAI(api_key=API_KEY, model="gemini-1.5-pro")
+output = StrOutputParser()
+chain = template | model | output
+
+def messages_history(session_id):
+    return SQLChatMessageHistory(session_id=session_id, connection="sqlite:///Chat_history/sqlite.db")
+
+conversation_chain = RunnableWithMessageHistory(
+    chain, messages_history, input_message_key="input", history_messages_key="chat_history"
+)
+
+with st.sidebar:
+    st.title("🤖 AI Data Science Chatbot")
+    st.header("User Login")
+    user_id = st.text_input("Enter your User ID:", key="user_id_input")
+
+if not user_id:
+    st.warning("Please enter a User ID to start chatting.")
+    st.stop()
+
+if "last_user_id" not in st.session_state or st.session_state.last_user_id != user_id:
+    st.session_state.chat_history = []  
+    st.session_state.last_user_id = user_id  
+
+chat_history = messages_history(user_id).messages
+st.session_state.chat_history = [(msg.type, msg.content) for msg in chat_history]
+
+st.write("Welcome! Start chatting below:")
+
+for role, message in st.session_state.chat_history:
+    st.chat_message(role).write(message)
+
+user_input = st.chat_input("Type your message...")
+
+if user_input:
+    st.session_state.chat_history.append(("user", user_input))
+    st.chat_message("user").write(user_input)
+
+    config = {"configurable": {"session_id": user_id}}
+    input_prompt = {"input": user_input} 
+    response = conversation_chain.invoke(input_prompt, config=config)
+
+    st.session_state.chat_history.append(("assistant", response))
+    st.chat_message("assistant").write(response)

requirements.txt

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+streamlit
+langchain_core
+langchain_community
+langchain_google_genai