Update app.py

app.py

@@ -1,137 +1,137 @@
-import os
-import json
-import sqlite3
-from datetime import datetime
-import streamlit as st
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain_chroma import Chroma
-from langchain_groq import ChatGroq
-from langchain.memory import ConversationBufferMemory
-from langchain.chains import ConversationalRetrievalChain
-
-from vectorize_documents import embeddings
-
-working_dir = os.path.dirname(os.path.abspath(__file__))
-config_data = json.load(open(f"{working_dir}/config.json"))
-GROQ_API_KEY = config_data["GROQ_API_KEY"]
-os.environ["GROQ_API_KEY"]= GROQ_API_KEY
-
-# Set up the database with check_same_thread=False
-def setup_db():
-    conn = sqlite3.connect("chat_history.db", check_same_thread=False)  # Ensure thread-safe connection
-    cursor = conn.cursor()
-    cursor.execute("""
-        CREATE TABLE IF NOT EXISTS chat_histories (
-            id INTEGER PRIMARY KEY AUTOINCREMENT,
-            username TEXT,
-            timestamp TEXT,
-            day TEXT,
-            user_message TEXT,
-            assistant_response TEXT
-        )
-    """)
-    conn.commit()
-    return conn  # Return the connection
-
-# Function to save chat history to SQLite
-def save_chat_history(conn, username, timestamp, day, user_message, assistant_response):
-    cursor = conn.cursor()
-    cursor.execute("""
-        INSERT INTO chat_histories (username, timestamp, day, user_message, assistant_response)
-        VALUES (?, ?, ?, ?, ?)
-    """, (username, timestamp, day, user_message, assistant_response))
-    conn.commit()
-
-# Function to set up vectorstore for embeddings
-def setup_vectorstore():
-    embeddings = HuggingFaceEmbeddings()
-    vectorstore = Chroma(persist_directory="House_vectordb", embedding_function=embeddings)
-    return vectorstore
-
-# Function to set up the chatbot chain
-def chat_chain(vectorstore):
-    llm = ChatGroq(model="llama-3.1-70b-versatile", temperature=0)
-    retriever = vectorstore.as_retriever()
-    memory = ConversationBufferMemory(
-        llm=llm,
-        output_key="answer",
-        memory_key="chat_history",
-        return_messages=True
-    )
-    chain = ConversationalRetrievalChain.from_llm(
-        llm=llm,
-        retriever=retriever,
-        chain_type="stuff",
-        memory=memory,
-        verbose=True,
-        return_source_documents=True
-    )
-    return chain
-
-# Streamlit UI setup
-st.set_page_config(page_title="House.Ai", page_icon="🤖AI", layout="centered")
-
-st.title("🤖 House.Ai")
-st.subheader("You can ask your general questions and queries to our AI")
-
-# Step 1: Initialize the connection and check if the user is already logged in
-if "conn" not in st.session_state:
-    st.session_state.conn = setup_db()
-
-if "username" not in st.session_state:
-    username = st.text_input("Enter your name to proceed:")
-    if username:
-        with st.spinner("Loading chatbot interface... Please wait."):
-            st.session_state.username = username
-            st.session_state.chat_history = []  # Initialize empty chat history in memory
-            st.session_state.vectorstore = setup_vectorstore()
-            st.session_state.conversational_chain = chat_chain(st.session_state.vectorstore)
-            st.success(f"Welcome, {username}! The chatbot interface is ready.")
-else:
-    username = st.session_state.username
-
-# Step 2: Initialize components if not already set
-if "conversational_chain" not in st.session_state:
-    st.session_state.vectorstore = setup_vectorstore()
-    st.session_state.conversational_chain = chat_chain(st.session_state.vectorstore)
-
-# Step 3: Display the chat history in the UI
-if "username" in st.session_state:
-    st.subheader(f"Hello {username}, start your query below!")
-
-    # Display chat history (messages exchanged between user and assistant)
-    if st.session_state.chat_history:
-        for message in st.session_state.chat_history:
-            if message['role'] == 'user':
-                with st.chat_message("user"):
-                    st.markdown(message["content"])
-            elif message['role'] == 'assistant':
-                with st.chat_message("assistant"):
-                    st.markdown(message["content"])
-
-    # Input field for the user to type their message
-    user_input = st.chat_input("Ask AI....")
-    
-    if user_input:
-        with st.spinner("Processing your query... Please wait."):
-            # Save user input to chat history in memory
-            st.session_state.chat_history.append({"role": "user", "content": user_input})
-
-            # Display user's message in chatbot (for UI display)
-            with st.chat_message("user"):
-                st.markdown(user_input)
-
-            # Get assistant's response from the chain
-            with st.chat_message("assistant"):
-                response = st.session_state.conversational_chain({"question": user_input})
-                assistant_response = response["answer"]
-                st.markdown(assistant_response)
-
-                # Save assistant's response to chat history in memory
-                st.session_state.chat_history.append({"role": "assistant", "content": assistant_response})
-
-                # Save the chat history to the database (SQLite)
-                timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-                day = datetime.now().strftime("%A")  # Get the day of the week (e.g., Monday)
-                save_chat_history(st.session_state.conn, username, timestamp, day, user_input, assistant_response)
-
+import os
+import json
+import sqlite3
+from datetime import datetime
+import streamlit as st
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_chroma import Chroma
+from langchain_groq import ChatGroq
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+
+from vectorize_documents import embeddings
+
+working_dir = os.path.dirname(os.path.abspath(__file__))
+config_data = json.load(open(f"{working_dir}/config.json"))
+GROQ_API_KEY = config_data["GROQ_API_KEY"]
+os.environ["GROQ_API_KEY"]= GROQ_API_KEY
+
+# Set up the database with check_same_thread=False
+def setup_db():
+    conn = sqlite3.connect("chat_history.db", check_same_thread=False)  # Ensure thread-safe connection
+    cursor = conn.cursor()
+    cursor.execute("""
+        CREATE TABLE IF NOT EXISTS chat_histories (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            username TEXT,
+            timestamp TEXT,
+            day TEXT,
+            user_message TEXT,
+            assistant_response TEXT
+        )
+    """)
+    conn.commit()
+    return conn  # Return the connection
+
+# Function to save chat history to SQLite
+def save_chat_history(conn, username, timestamp, day, user_message, assistant_response):
+    cursor = conn.cursor()
+    cursor.execute("""
+        INSERT INTO chat_histories (username, timestamp, day, user_message, assistant_response)
+        VALUES (?, ?, ?, ?, ?)
+    """, (username, timestamp, day, user_message, assistant_response))
+    conn.commit()
+
+# Function to set up vectorstore for embeddings
+def setup_vectorstore():
+    embeddings = HuggingFaceEmbeddings()
+    vectorstore = Chroma(persist_directory="House_vectordb", embedding_function=embeddings)
+    return vectorstore
+
+# Function to set up the chatbot chain
+def chat_chain(vectorstore):
+    llm = ChatGroq(model="llama-3.3-70b-versatile", temperature=0)
+    retriever = vectorstore.as_retriever()
+    memory = ConversationBufferMemory(
+        llm=llm,
+        output_key="answer",
+        memory_key="chat_history",
+        return_messages=True
+    )
+    chain = ConversationalRetrievalChain.from_llm(
+        llm=llm,
+        retriever=retriever,
+        chain_type="stuff",
+        memory=memory,
+        verbose=True,
+        return_source_documents=True
+    )
+    return chain
+
+# Streamlit UI setup
+st.set_page_config(page_title="House.Ai", page_icon="🤖AI", layout="centered")
+
+st.title("🤖 House.Ai")
+st.subheader("You can ask your general questions and queries to our AI")
+
+# Step 1: Initialize the connection and check if the user is already logged in
+if "conn" not in st.session_state:
+    st.session_state.conn = setup_db()
+
+if "username" not in st.session_state:
+    username = st.text_input("Enter your name to proceed:")
+    if username:
+        with st.spinner("Loading chatbot interface... Please wait."):
+            st.session_state.username = username
+            st.session_state.chat_history = []  # Initialize empty chat history in memory
+            st.session_state.vectorstore = setup_vectorstore()
+            st.session_state.conversational_chain = chat_chain(st.session_state.vectorstore)
+            st.success(f"Welcome, {username}! The chatbot interface is ready.")
+else:
+    username = st.session_state.username
+
+# Step 2: Initialize components if not already set
+if "conversational_chain" not in st.session_state:
+    st.session_state.vectorstore = setup_vectorstore()
+    st.session_state.conversational_chain = chat_chain(st.session_state.vectorstore)
+
+# Step 3: Display the chat history in the UI
+if "username" in st.session_state:
+    st.subheader(f"Hello {username}, start your query below!")
+
+    # Display chat history (messages exchanged between user and assistant)
+    if st.session_state.chat_history:
+        for message in st.session_state.chat_history:
+            if message['role'] == 'user':
+                with st.chat_message("user"):
+                    st.markdown(message["content"])
+            elif message['role'] == 'assistant':
+                with st.chat_message("assistant"):
+                    st.markdown(message["content"])
+
+    # Input field for the user to type their message
+    user_input = st.chat_input("Ask AI....")
+    
+    if user_input:
+        with st.spinner("Processing your query... Please wait."):
+            # Save user input to chat history in memory
+            st.session_state.chat_history.append({"role": "user", "content": user_input})
+
+            # Display user's message in chatbot (for UI display)
+            with st.chat_message("user"):
+                st.markdown(user_input)
+
+            # Get assistant's response from the chain
+            with st.chat_message("assistant"):
+                response = st.session_state.conversational_chain({"question": user_input})
+                assistant_response = response["answer"]
+                st.markdown(assistant_response)
+
+                # Save assistant's response to chat history in memory
+                st.session_state.chat_history.append({"role": "assistant", "content": assistant_response})
+
+                # Save the chat history to the database (SQLite)
+                timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+                day = datetime.now().strftime("%A")  # Get the day of the week (e.g., Monday)
+                save_chat_history(st.session_state.conn, username, timestamp, day, user_input, assistant_response)
+