Update app.py

app.py

@@ -1,97 +1,97 @@
-import os
-import json
-import streamlit as st
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain_postgres.vectorstores import PGVector
-from langchain_groq import ChatGroq
-from langchain.memory import ConversationBufferMemory
-from langchain.chains import ConversationalRetrievalChain
-
-# Load the embeddings function
-from vectorize_data_pgvector import embeddings  # Assuming embeddings are imported from your previous script
-
-# Load configuration
-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
-
-# Define the connection string and collection name for PostgreSQL
-connection_string = "postgresql+psycopg2://postgres:krishna23@localhost:5432/vector_db"
-collection_name = "whatsapp_chatbot"
-
-# Set up the PGVector-based vectorstore
-def setup_vectorstore():
-    embeddings = HuggingFaceEmbeddings()  # Use HuggingFaceEmbeddings
-    vectorstore = PGVector(
-        embeddings=embeddings,
-        connection=connection_string,
-        collection_name=collection_name,
-        
-    )
-    return vectorstore
-
-# Set up the conversational 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="WhatsApp FAQ AI",
-    page_icon="🤖AI",
-    layout="centered"
-)
-
-st.title("🤖AI WhatsApp FAQ")
-
-# Initialize session state for chat history and vectorstore
-if "chat_history" not in st.session_state:
-    st.session_state.chat_history = []
-
-if "vectorstore" not in st.session_state:
-    st.session_state.vectorstore = setup_vectorstore()
-
-if "conversational_chain" not in st.session_state:
-    st.session_state.conversational_chain = chat_chain(st.session_state.vectorstore)
-
-# Display chat history
-for message in st.session_state.chat_history:
-    with st.chat_message(message["role"]):
-        st.markdown(message["content"])
-
-# User input
-user_input = st.chat_input("Ask AI....")
-
-if user_input:
-    # Append user message to chat history
-    st.session_state.chat_history.append({"role": "user", "content": user_input})
-
-    with st.chat_message("user"):
-        st.markdown(user_input)
-
-    with st.chat_message("assistant"):
-        response = st.session_state.conversational_chain({"question": user_input})
-        assistant_response = response["answer"]
-        st.markdown(assistant_response)
-
-        # Append assistant response to chat history
-        st.session_state.chat_history.append({"role": "assistant", "content": assistant_response})
+import os
+import json
+import streamlit as st
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_postgres.vectorstores import PGVector
+from langchain_groq import ChatGroq
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+
+# Load the embeddings function
+from vectorize_data_pgvector import embeddings  # Assuming embeddings are imported from your previous script
+
+# Load configuration
+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
+
+# Define the connection string and collection name for PostgreSQL
+connection_string = "postgresql+psycopg2://postgres:krishna@localhost:5432/whatsapp_vector_db"
+collection_name = "whatsapp_chatbot"
+
+# Set up the PGVector-based vectorstore
+def setup_vectorstore():
+    embeddings = HuggingFaceEmbeddings()  # Use HuggingFaceEmbeddings
+    vectorstore = PGVector(
+        embeddings=embeddings,
+        connection=connection_string,
+        collection_name=collection_name,
+        
+    )
+    return vectorstore
+
+# Set up the conversational 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="WhatsApp FAQ AI",
+    page_icon="🤖AI",
+    layout="centered"
+)
+
+st.title("🤖AI WhatsApp FAQ")
+
+# Initialize session state for chat history and vectorstore
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
+
+if "vectorstore" not in st.session_state:
+    st.session_state.vectorstore = setup_vectorstore()
+
+if "conversational_chain" not in st.session_state:
+    st.session_state.conversational_chain = chat_chain(st.session_state.vectorstore)
+
+# Display chat history
+for message in st.session_state.chat_history:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# User input
+user_input = st.chat_input("Ask AI....")
+
+if user_input:
+    # Append user message to chat history
+    st.session_state.chat_history.append({"role": "user", "content": user_input})
+
+    with st.chat_message("user"):
+        st.markdown(user_input)
+
+    with st.chat_message("assistant"):
+        response = st.session_state.conversational_chain({"question": user_input})
+        assistant_response = response["answer"]
+        st.markdown(assistant_response)
+
+        # Append assistant response to chat history
+        st.session_state.chat_history.append({"role": "assistant", "content": assistant_response})