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app.py

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+import os
+# exec(os.getenv("CODE"))   # to execute the whole code in huggingface.
+
+import streamlit as st
+from PyPDF2 import PdfReader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+import google.generativeai as genai
+from langchain_community.vectorstores import FAISS
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain.chains.question_answering import load_qa_chain
+from langchain.prompts import PromptTemplate
+from dotenv import load_dotenv
+import base64
+from io import BytesIO
+
+load_dotenv()
+
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+
+## going to each and very pdf and each page of that padf and extracting text from it.
+def get_pdf_text(pdf_docs):
+    text = ""
+    for pdf in pdf_docs:
+        pdf_reader = PdfReader(BytesIO(pdf.read()))
+        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
+
+## converting chunks into vectors
+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")
+
+## developing bot
+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 the provided context just say, "answer is not available in the context",
+    don't provide the wrong answer.
+    Context: \n{context}?\n
+    Question: \n{question}\n
+
+    Answer:
+    """
+    model = ChatGoogleGenerativeAI(model = "gemini-pro", temperature= 0.45)
+    prompt= PromptTemplate(template=prompt_template, input_variables=['context', 'question'])
+    chain = load_qa_chain(model, chain_type="stuff", prompt= prompt)
+    return chain
+
+## the user input interface
+def user_input(user_question):
+    embeddings = GoogleGenerativeAIEmbeddings(model='models/embedding-001')
+
+    db = FAISS.load_local('faiss_index', embeddings, allow_dangerous_deserialization= True)
+    docs = db.similarity_search(user_question)
+
+    chain = get_conversational_chain()
+
+    response= chain({"input_documents":docs, "question":user_question}, return_only_outputs=True)
+
+    print(response)
+    st.write("Bot: ", response["output_text"])
+
+# streamlit app
+def main():
+    st.set_page_config(page_title="Chat With Multiple PDF")
+
+    # Function to set a background image
+    def set_background(image_file):
+        with open(image_file, "rb") as image:
+            b64_image = base64.b64encode(image.read()).decode("utf-8")
+        css = f"""
+        <style>
+        .stApp {{
+            background: url(data:image/png;base64,{b64_image});
+            background-size: cover;
+            background-position: centre;
+            backgroun-repeat: no-repeat;
+        }}
+        </style>
+        """
+        st.markdown(css, unsafe_allow_html=True)
+
+    # Set the background image
+    set_background("background_image.png")
+
+    st.header("Podcast With Your PDF's")
+
+    user_question = st.text_input("Ask a Question from the PDF Files")
+
+    if user_question:
+        user_input(user_question)
+    
+    with st.sidebar:
+        st.title("Menu:")
+        pdf_docs = st.file_uploader("Upload Your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True, type='pdf')
+        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 __name__ == "__main__":
+    main()

requirements.txt

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+streamlit
+google-generativeai
+python-dotenv
+langchain
+langchain-community
+PyPDF2
+faiss-cpu
+langchain_google_genai