Update app.py

app.py

@@ -1,69 +1,73 @@
-import streamlit as st
-import os
-from langchain_groq import ChatGroq
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.chains.combine_documents import create_stuff_documents_chain
-from langchain_core.prompts import ChatPromptTemplate
-from langchain.chains import create_retrieval_chain
-from langchain_community.vectorstores import FAISS
-from langchain_community.document_loaders import PyPDFDirectoryLoader
-from langchain_google_genai import GoogleGenerativeAIEmbeddings
-from dotenv import load_dotenv
-import os
-import time
-load_dotenv()
-
-## load the GROQ And OpenAI API KEY 
-groq_api_key=os.getenv('GROQ_API_KEY')
-os.environ["GOOGLE_API_KEY"]=os.getenv("GOOGLE_API_KEY")
-
-llm=ChatGroq(groq_api_key=groq_api_key, model_name="Llama3-8b-8192") # Gemma2-9b-it Gemma-7b-it
-
-prompt=ChatPromptTemplate.from_template(
-"""
-Answer the questions based on the provided context only.
-Please provide the most accurate response based on the question
-<context>
-{context}
-<context>
-Questions:{input}
-"""
-)
-
-def vector_embedding():
-    if "vectors" not in st.session_state:
-        st.session_state.embeddings=GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
-        st.session_state.loader=PyPDFDirectoryLoader("./Documents") ## Data Ingestion
-        st.session_state.docs=st.session_state.loader.load() ## Document Loading
-        st.session_state.text_splitter=RecursiveCharacterTextSplitter(chunk_size=1000,chunk_overlap=200) ## Chunk Creation
-        st.session_state.final_documents=st.session_state.text_splitter.split_documents(st.session_state.docs[:20]) #splitting
-        st.session_state.vectors=FAISS.from_documents(st.session_state.final_documents,st.session_state.embeddings) #vector OpenAI embeddings
-
-
-st.header("Rag Model with multiple documents ChatBot")
-prompt1=st.text_input("Enter Your Question From Doduments")
-
-if prompt1:
-    document_chain=create_stuff_documents_chain(llm,prompt)
-    retriever=st.session_state.vectors.as_retriever()
-    retrieval_chain=create_retrieval_chain(retriever,document_chain)
-    start=time.process_time()
-    response=retrieval_chain.invoke({'input':prompt1})
-    print("Response time :",time.process_time()-start)
-    st.write(response['answer'])
-
-with st.sidebar:
-    st.title("Menu:")   
-    if st.button("Documents Embedding"):
-        with st.spinner("Processing..."):
-            vector_embedding()
-            st.write("Vector Store DB Is Ready")
-
-
-footer = """
----
-#### Made By [Surat Banerjee](https://www.linkedin.com/in/surat-banerjee/)
-For Any Queries, Reach out on [Portfolio](https://suratbanerjee.wixsite.com/myportfoliods)  
-"""
-
-st.markdown(footer, unsafe_allow_html=True)
+import streamlit as st
+import os
+from langchain_groq import ChatGroq
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.prompts import ChatPromptTemplate
+from langchain.chains import create_retrieval_chain
+from langchain_community.vectorstores import FAISS
+from langchain_community.document_loaders import PyPDFDirectoryLoader
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+from dotenv import load_dotenv
+import os
+import time
+load_dotenv()
+
+## load the GROQ And OpenAI API KEY 
+groq_api_key=os.getenv('GROQ_API_KEY')
+os.environ["GOOGLE_API_KEY"]=os.getenv("GOOGLE_API_KEY")
+
+llm=ChatGroq(groq_api_key=groq_api_key, model_name="Llama3-8b-8192") # Gemma2-9b-it Gemma-7b-it
+
+prompt=ChatPromptTemplate.from_template(
+"""
+Answer the questions based on the provided context only.
+Please provide the most accurate response based on the question
+<context>
+{context}
+<context>
+Questions:{input}
+"""
+)
+
+def vector_embedding():
+    if "vectors" not in st.session_state:
+        st.session_state.embeddings=GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
+        st.session_state.loader=PyPDFDirectoryLoader("./Documents") ## Data Ingestion
+        st.session_state.docs=st.session_state.loader.load() ## Document Loading
+        st.session_state.text_splitter=RecursiveCharacterTextSplitter(chunk_size=1000,chunk_overlap=200) ## Chunk Creation
+        st.session_state.final_documents=st.session_state.text_splitter.split_documents(st.session_state.docs[:20]) #splitting
+        st.session_state.vectors=FAISS.from_documents(st.session_state.final_documents,st.session_state.embeddings) #vector OpenAI embeddings
+
+
+st.header("Rag Model with multiple documents ChatBot")
+prompt1=st.text_input("Enter Your Question From Doduments")
+
+if prompt1:
+    document_chain=create_stuff_documents_chain(llm,prompt)
+    retriever=st.session_state.vectors.as_retriever()
+    retrieval_chain=create_retrieval_chain(retriever,document_chain)
+    start=time.process_time()
+    response=retrieval_chain.invoke({'input':prompt1})
+    print("Response time :",time.process_time()-start)
+    st.write(response['answer'])
+
+with st.sidebar:
+    st.title("Menu:")   
+    if st.button("Documents Embedding"):
+        with st.spinner("Processing..."):
+            vector_embedding()
+            st.write("Vector Store DB Is Ready")
+
+    if st.button("Clear Chat Window", use_container_width=True, type="primary"):
+        st.session_state.history = []
+        st.rerun()
+
+
+footer = """
+---
+#### Made By [Surat Banerjee](https://www.linkedin.com/in/surat-banerjee/)
+For Any Queries, Reach out on [Portfolio](https://suratbanerjee.wixsite.com/myportfoliods)  
+"""
+
+st.markdown(footer, unsafe_allow_html=True)