Create app.py

app.py

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+import streamlit as st
+import sqlite3
+import uuid
+import time
+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
+GOOGLE_API_KEY = st.secrets.get("GOOGLE_API_KEY")
+
+# Set up the Gemini 1.5 Pro model
+llm = GoogleGenerativeAI(api_key=GOOGLE_API_KEY, model="gemini-1.5-pro")
+
+# Initialize SQLite database
+conn = sqlite3.connect("chat_history.db", check_same_thread=False)
+cursor = conn.cursor()
+cursor.execute("""
+CREATE TABLE IF NOT EXISTS chat (
+    id INTEGER PRIMARY KEY AUTOINCREMENT,
+    session_id TEXT,
+    role TEXT,
+    content TEXT
+)
+""")
+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"
+    )
+
+# Generate unique session ID
+if "session_id" not in st.session_state:
+    st.session_state.session_id = str(uuid.uuid4())
+
+# Custom CSS for UI enhancements
+st.markdown("""
+    <style>
+        body {
+            background-color: #E3F2FD;
+        }
+        .title-text {
+            text-align: center;
+            font-size: 28px;
+            font-weight: bold;
+            color: #1976D2;
+            padding: 15px;
+        }
+        .stTextInput {
+            position: fixed;
+            bottom: 10px;
+            width: 80%;
+            left: 10%;
+            z-index: 999;
+        }
+        .chat-container {
+            background-color: white;
+            padding: 20px;
+            border-radius: 10px;
+            box-shadow: 2px 2px 10px rgba(0,0,0,0.1);
+        }
+    </style>
+""", unsafe_allow_html=True)
+
+# Display title
+st.markdown("""<h1 class='title-text'>💬 AI Data Science Tutor</h1>""", unsafe_allow_html=True)
+
+# New Chat Button
+if st.button("🆕 New Chat"):
+    st.session_state.session_id = str(uuid.uuid4())  # Generate new session
+    st.session_state.messages = []  # Clear chat history
+    st.rerun()  # Refresh the app
+
+# Get session ID
+session_id = st.session_state.session_id
+chat_history_instance = chat_history(session_id)
+
+# Define Chat Prompt Template
+chat_prompt = ChatPromptTemplate(
+    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
+out_parser = StrOutputParser()
+
+# Create a chain
+chain = chat_prompt | llm | out_parser
+
+# Define Runnable with message history
+chat = RunnableWithMessageHistory(
+    chain,
+    lambda session: SQLChatMessageHistory(session, "sqlite:///chat_history.db"),
+    input_messages_key="prompt",
+    history_messages_key="history"
+)
+
+# Chat History Container
+st.markdown("### Chat History")
+chat_container = st.container()
+
+# Load chat history and display it
+if "messages" not in st.session_state:
+    st.session_state.messages = load_chat_history(session_id)
+
+with chat_container:
+    for role, content in st.session_state.messages:
+        with st.chat_message(role):
+            st.markdown(content)
+
+# User input at the bottom
+user_input = st.text_input("Type your message here:", key="user_message")
+
+# If user submits a message
+if user_input:
+    save_message(session_id, "user", user_input)
+    st.session_state.messages.append(("user", user_input))
+
+    # Invoke 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))
+
+    # Display AI response
+    with chat_container:
+        with st.chat_message("assistant"):
+            st.markdown(response)
+
+    # Clear the input field
+    st.session_state.pop("user_message")
+    st.session_state["user_message"] = ""
+    st.rerun()  # Refresh the app