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requirements.txt

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+streamlit
+google-generativeai
+python-dotenv
+langchain
+PyPDF2
+chromadb
+faiss-cpu
+pdf2image
+langchain
+PyPDF2
+chromadb
+faiss-cpu
+langchain_google_genai
+pdfplumber
+pickle

test.py

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+from dotenv import load_dotenv
+load_dotenv() # Load all env variables 
+
+import streamlit as st
+from PyPDF2 import PdfReader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+import os
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+import google.generativeai as genai
+from langchain.vectorstores import FAISS
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain.chains.question_answering import load_qa_chain
+from langchain.prompts import PromptTemplate
+
+## Function to load gemini pro model and get responses 
+model = genai.GenerativeModel("gemini-pro")
+def get_gemini_response(question):
+    response=model.generate_content(question)
+    return response.text
+
+def get_pdf_text(pdf_docs):
+    text=""
+    for pdf in pdf_docs:
+        pdf_reader= PdfReader(pdf)
+        for page in pdf_reader.pages:
+            text+= page.extract_text()
+    return  text
+
+def get_text_chunks(text):
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+def get_vector_store(text_chunks):
+    embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
+    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
+    vector_store.save_local("faiss_index")
+
+def get_conversational_chain():
+    prompt_template = """
+    Answer the question as detailed as possible from the provided context, make sure to provide all the details, if the answer is not in
+    provided context just say, "answer is not available in the context", don't provide the wrong answer\n\n
+    Context:\n {context}?\n
+    Question: \n{question}\n
+
+    Answer:
+    """
+    model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3)
+    prompt = PromptTemplate(template = prompt_template, input_variables = ["context", "question"])
+    chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
+    return chain
+
+def user_input(user_question):
+    embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
+    new_db = FAISS.load_local("faiss_index", embeddings,allow_dangerous_deserialization=True)
+    docs = new_db.similarity_search(user_question)
+    chain = get_conversational_chain()
+    response = chain({"input_documents":docs, "question": user_question}, return_only_outputs=True)
+    return response["output_text"]
+
+def main():
+    st.set_page_config(page_title='Q&A Demo')
+    st.header("Combined Application")
+
+    app_mode = st.sidebar.selectbox("Choose the App Mode", ["Gemini Q&A", "PDF Q&A"])
+
+    if app_mode == "Gemini Q&A":
+        st.subheader("Gemini LLM Application")
+        user_question = st.text_input("Input")
+        if st.button("Ask the question"):
+            response = get_gemini_response(user_question)
+            st.subheader("The Response is :")
+            st.write(response)
+
+    elif app_mode == "PDF Q&A":
+        st.subheader("Chat with PDF using Gemini💁")
+        user_question = st.text_input("Ask a Question from the PDF Files")
+        pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True)
+        if st.button("Submit & Process"):
+            with st.spinner("Processing..."):
+                raw_text = get_pdf_text(pdf_docs)
+                text_chunks = get_text_chunks(raw_text)
+                get_vector_store(text_chunks)
+                st.success("Done")
+
+        if user_question:
+            response = user_input(user_question)
+            st.write("Reply: ", response)
+
+if __name__ == "__main__":
+    main()