Create app.py

app.py

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+import streamlit as st
+from PyPDF2 import PdfReader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+import os
+import google.generativeai as genai
+from langchain.vectorstores import FAISS
+from langchain.prompts import PromptTemplate
+from langchain.chains.question_answering import load_qa_chain
+from dotenv import load_dotenv
+
+load_dotenv()
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+
+def get_pdf_text(pdf_docs):
+    text = ""
+    pdf_reader = PdfReader(pdf_docs)
+    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):
+    from langchain_google_genai import GoogleGenerativeAIEmbeddings
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    vector_store = FAISS.from_texts(text_chunks, embeddings)
+    vector_store.save_local("faiss_index")
+
+def get_conversational_chain():
+    from langchain_google_genai import ChatGoogleGenerativeAI
+    prompt_template = """
+        Answer the question as detailed as possible from the provided context and make sure to provide all the details.
+        If the answer is not present in the provided context, just say "Answer is not available in context". Do not provide
+        the wrong answer.
+        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):
+    from langchain_google_genai import GoogleGenerativeAIEmbeddings
+    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)
+    st.write("Reply:", response["output_text"])
+
+def main():
+    st.set_page_config(page_title="Chat with PDF")
+    st.header("Chat with PDF using Gemini AI")
+
+    user_question = st.text_input("Ask a question about the PDF file")
+    if user_question:
+        user_input(user_question)
+    with st.sidebar:
+        st.title("Menu")
+        pdf_docs = st.file_uploader("Upload your PDF file here")
+        if st.button("Submit & Process"):
+            if pdf_docs:
+                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("Processing complete")
+            else:
+                st.error("Please upload a PDF file")
+
+if __name__ == "__main__":
+    main()