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.gitignore

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+.venv
+venv/
+
+.env
+

app.py

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+import streamlit as st
+from PyPDF2 import PdfReader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+import os
+
+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_core.prompts import PromptTemplate
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# Initialize session state
+if 'processed' not in st.session_state:
+    st.session_state.processed = False
+
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+
+@st.cache_data
+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
+
+@st.cache_data
+def get_text_chunks(text):
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=5000, chunk_overlap=500)
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+@st.cache_data
+def get_vector_store(chunks):
+    embeddings=GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    vector_store = FAISS.from_texts(chunks, embedding=embeddings)
+    vector_store.save_local("faiss_index")
+
+@st.cache_resource
+def get_conversation_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=0.4)
+    prompt=PromptTemplate(template=prompt_template, input_variables=["context", "question"])
+    chain=load_qa_chain(model,chain_type="stuff",prompt=prompt)
+    return chain
+
+def process_pdfs(pdf_docs):
+    raw_text = get_pdf_text(pdf_docs)
+    text_chunks = get_text_chunks(raw_text)
+    get_vector_store(text_chunks)
+    st.session_state.processed = True
+    return "PDFs processed successfully!"
+
+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_conversation_chain()
+    response=chain(
+        {"input_documents":docs, "question":user_question},
+        return_only_outputs=True
+    )
+    return response["output_text"]
+
+def main():
+    st.title("Chat with multiple PDFs")
+    
+    tab1, tab2 = st.tabs(["Upload PDFs", "Chat"])
+    
+    with tab1:
+        pdf_docs = st.file_uploader("Upload your PDF files", type=['pdf'], accept_multiple_files=True)
+        if st.button("Process"):
+            with st.spinner("Processing PDFs..."):
+                status = process_pdfs(pdf_docs)
+                st.success(status)
+    
+    with tab2:
+        if not st.session_state.processed:
+            st.warning("Please upload and process PDFs first")
+        else:
+            user_question = st.text_input("Ask a question from the PDF files")
+            if st.button("Submit"):
+                with st.spinner("Generating response..."):
+                    response = user_input(user_question)
+                    st.write(response)
+
+if __name__=="__main__":
+    main()
+

requirements.txt

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+streamlit
+google-generativeai
+python-dotenv
+langchain
+PyPDF2
+faiss-cpu
+ipykernel
+langchain-google-genai
+langchain-text-splitters
+langchain-community