Update app.py

app.py

@@ -1,22 +1,20 @@
 import streamlit as st 
 import sqlite3
 import uuid
-import langchain
 from langchain_google_genai import GoogleGenerativeAI
 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
 
-# Load API key from file
-# ✅ Access Hugging Face Secret API Key
+# Load API key from secrets
 GOOGLE_API_KEY = st.secrets.get("GOOGLE_API_KEY")
 
-# Set up the Gemini 2.0 Flash model
+# Set up the Gemini 1.5 Pro model
 llm = GoogleGenerativeAI(api_key=GOOGLE_API_KEY, model="gemini-1.5-pro")
 
 # Initialize SQLite database for chat history
-conn = sqlite3.connect("chat_history.db")
+conn = sqlite3.connect("chat_history.db", check_same_thread=False)
 cursor = conn.cursor()
 cursor.execute("""
 CREATE TABLE IF NOT EXISTS chat (
@@ -28,18 +26,21 @@ CREATE TABLE IF NOT EXISTS chat (
 """)
 conn.commit()
 
+# Function to save messages
 def save_message(session_id, role, content):
     cursor.execute("INSERT INTO chat (session_id, role, content) VALUES (?, ?, ?)", (session_id, role, content))
     conn.commit()
 
+# Function to load chat history
 def load_chat_history(session_id):
     cursor.execute("SELECT role, content FROM chat WHERE session_id = ?", (session_id,))
     return cursor.fetchall()
 
+# Chat history instance
 def chat_history(session_id):
     return SQLChatMessageHistory(
         session_id=session_id,
-        connection='sqlite:///chat_history.db'
+        connection=conn  # FIXED: Pass direct SQLite connection
     )
 
 # Generate unique session ID for each user
@@ -49,14 +50,16 @@ if "session_id" not in st.session_state:
 session_id = st.session_state.session_id
 chat_history_instance = chat_history(session_id)
 
-# Define Chat Prompt Template
+# Define Chat Prompt Template (FIXED)
 chat_prompt = ChatPromptTemplate(
-    messages=[('system', ''''You are an AI assistant.You are a Data Science tutor. You provide answers only about Data Science.Answer every question as deeply as possible.
-                            If asked anything outside this topic, do not respond and instead prompt the user to ask a Data Science-related question. 
-                            Only provide responses related to Data Science.
-                          '''),
-              MessagesPlaceholder(variable_name="history", optional=True),
-              ('human', '{prompt}')]
+    messages=[
+        ('system', """You are an AI assistant specialized in Data Science tutoring. 
+                      You will only answer questions related to Data Science. 
+                      If asked anything outside this topic, politely decline and request a Data Science-related question.
+                   """),
+        MessagesPlaceholder(variable_name="history", optional=True),
+        ('human', '{prompt}')
+    ]
 )
 
 # Define output parser
@@ -68,28 +71,36 @@ chain = chat_prompt | llm | out_parser
 # Define Runnable with message history
 chat = RunnableWithMessageHistory(
     chain,
-    chat_history,
-    input_messages_key='prompt',
-    history_messages_key='history'
+    lambda session: SQLChatMessageHistory(session, conn),  # FIXED: Pass connection correctly
+    input_messages_key="prompt",
+    history_messages_key="history"
 )
 
 # Streamlit UI
 st.title("Conversational AI Data Science Tutor")
 st.write("Ask me anything about Data Science!")
 
-# Load chat history from database
-st.session_state.messages = load_chat_history(session_id)
+# Load chat history
+st.session_state.setdefault("messages", load_chat_history(session_id))
 
+# Display chat history
 for role, content in st.session_state.messages:
     with st.chat_message(role):
         st.markdown(content)
 
+# User input
 user_input = st.text_input("You:", "", key="user_input")
+
 if user_input:
     save_message(session_id, "user", user_input)
     st.session_state.messages.append(("user", user_input))
+    
+    # Invoke the AI model
     config = {'configurable': {'session_id': session_id}}
     response = chat.invoke({'prompt': user_input}, config)
+    
     save_message(session_id, "assistant", response)
     st.session_state.messages.append(("assistant", response))
-    st.chat_message("assistant").markdown(response)
+    
+    with st.chat_message("assistant"):
+        st.markdown(response)