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.chains.question_answering import load_qa_chain
+from langchain.prompts import PromptTemplate
+from dotenv import load_dotenv
+from langchain_community.embeddings import HuggingFaceBgeEmbeddings
+from langchain_huggingface import HuggingFaceEndpoint
+
+
+os.environ["HUGGINGFACEHUB_API_TOKEN"] = HUGGINGFACEHUB_API_TOKEN
+
+
+
+
+
+
+
+
+
+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):
+    model_name = "BAAI/bge-large-en"
+    model_kwargs = {'device': 'cpu'}
+    encode_kwargs = {'normalize_embeddings': True}
+    hf = HuggingFaceBgeEmbeddings(
+        model_name=model_name,
+        model_kwargs=model_kwargs,
+        encode_kwargs=encode_kwargs
+)
+    vector_store = FAISS.from_texts(text_chunks, embedding=hf)
+    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 = HuggingFaceEndpoint(
+    repo_id="google/gemma-2-9b-it",
+    temperature=0.3,
+    huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN,
+)
+    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):
+    model_name = "BAAI/bge-large-en"
+    model_kwargs = {'device': 'cpu'}
+    encode_kwargs = {'normalize_embeddings': True}
+    hf = HuggingFaceBgeEmbeddings(
+        model_name=model_name,
+        model_kwargs=model_kwargs,
+        encode_kwargs=encode_kwargs
+    )
+    
+    new_db = FAISS.load_local("faiss_index", hf)
+    docs = new_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("Reply: ", response["output_text"])
+
+
+
+
+def main():
+    st.set_page_config("Chat PDF")
+    st.header("Chat with PDF using Gemma")
+
+    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)
+        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()