added main function

app.py

@@ -1,39 +1,55 @@
 # Langchain imports
 from langchain_community.vectorstores.faiss import FAISS
-from langchain_groq import ChatGroq 
 from langchain_community.document_loaders import WebBaseLoader
-from langchain_community.embeddings import HuggingFaceEmbeddings
 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
 
-# Embedding and model import
+# Embedding and model imports
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_groq import ChatGroq
+
 # Other
 import streamlit as st
 import os
 import time
 from PyPDF2 import PdfReader
 import tempfile
-import pdfplumber
-
-
-st.title("Ask questions from your PDF(s) or website")
-option = None
-
-# Prompt user to choose between PDFs or website
-option = st.radio("Choose input type:", ("PDF(s)", "Website"), index=None)
 
 def get_pdf_processed(pdf_docs):
-    text = ""
+    text=""
     for pdf in pdf_docs:
-        with pdfplumber.open(pdf) as pdf_file:
-            for page in pdf_file.pages:
-                text += page.extract_text()
+        pdf_reader= PdfReader(pdf)
+        for page in pdf_reader.pages:
+            text += page.extract_text()
     return text
 
-def llm_model():
-    # llm = ChatGroq(model="mixtral-8x7b-32768",groq_api_key=st.secrets['GROQ_API_KEY'])
+st.session_state.embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size =1000, chunk_overlap= 200)
+
+def initialize_vector_store(option):
+    if option:
+        if option == "Website":
+            website_link = st.text_input("Enter the website link:")
+            if st.button("Submit & Process"):
+                with st.spinner("Loading website content..."):
+                    st.session_state.loader = WebBaseLoader(website_link)
+                    st.session_state.docs = st.session_state.loader.load()
+                    st.session_state.final_documents = st.session_state.text_splitter.split_documents(st.session_state.docs)
+                    st.session_state.vector = FAISS.from_documents(st.session_state.final_documents,st.session_state.embeddings)
+                    st.success("Website content loaded successfully!")
+
+        elif option == "PDF(s)":
+            pdf_files = st.file_uploader("Upload your PDF files", type=["pdf"], accept_multiple_files=True)
+            if st.button("Submit & Process"):
+                with st.spinner("Loading pdf..."):
+                    st.session_state.docs = get_pdf_processed(pdf_files)
+                    st.session_state.final_documents = st.session_state.text_splitter.split_text(st.session_state.docs)
+                    st.session_state.vector = FAISS.from_texts(st.session_state.final_documents,st.session_state.embeddings)
+                    st.success("PDF content loaded successfully!")
+
+def get_conversational_chain():
     llm = ChatGroq(model="mixtral-8x7b-32768")
     prompt = ChatPromptTemplate.from_template(
     """
@@ -48,40 +64,31 @@ def llm_model():
     document_chain = create_stuff_documents_chain(llm,prompt)
     retriever = st.session_state.vector.as_retriever() if st.session_state.vector else None
     retrieval_chain = create_retrieval_chain(retriever,document_chain)
+    return retrieval_chain
 
-    prompt = st.text_input("Input your question here")
+def user_input(prompt):
+    chain = get_conversational_chain()
+    start =time.process_time()
+    response = chain.invoke({"input":prompt})
+    st.write(response['answer'])
+    st.write("Response time: ", time.process_time() - start)
 
-    if prompt:
-        start = time.process_time()
-        response = retrieval_chain.invoke({"input":prompt})
-        st.write(response['answer'])
-        st.write("Response time: ", time.process_time() - start)
+    with st.expander("Did not like the response? Check out more here"):
+        for i, doc in enumerate(response['context']):
+            st.write(doc.page_content)
+            st.write("-----------------------------")
 
-model_name = "all-MiniLM-L6-v2"
-st.session_state.embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+def main():
+    st.title("Ask your questions from pdf(s) or website")
 
-st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size =1000, chunk_overlap= 200)
-
-if option:
-    if option == "Website":
-        website_link = st.text_input("Enter the website link:")
-        if st.button("Submit & Process"):
-            with st.spinner("Loading website content..."):
-                st.session_state.loader = WebBaseLoader(website_link)
-                st.session_state.docs = st.session_state.loader.load()
-                st.session_state.final_documents = st.session_state.text_splitter.split_documents(st.session_state.docs)
-                st.session_state.vector = FAISS.from_documents(st.session_state.final_documents,st.session_state.embeddings)            
-            st.success("Done!")
-            llm_model()
-            
-    elif option == "PDF(s)":
-        pdf_files = st.file_uploader("Upload your PDF files", type=["pdf"], accept_multiple_files=True)
-        if st.button("Submit & Process"):
-            with st.spinner("Loading pdf..."):
-                st.session_state.docs = get_pdf_processed(pdf_files)
-                st.session_state.final_documents = st.session_state.text_splitter.split_text(st.session_state.docs)
-                st.session_state.vector = FAISS.from_texts(st.session_state.final_documents,st.session_state.embeddings)            
-            st.success("Done!")
-            llm_model()
+    option = None
+    # Prompt user to choose between PDFs or website
+    option = st.radio("Choose input type:", ("PDF(s)", "Website"), index=None)
+    initialize_vector_store(option)
+    prompt = st.text_input("Input your question here")
+    if prompt:
+        user_input(prompt)
 
 
+if __name__ == "__main__":
+    main()