app.py

import streamlit as st
from PyPDF2 import PdfReader
from llama_index.llms import HuggingFaceInferenceAPI
from llama_index import VectorStoreIndex
from llama_index.embeddings import HuggingFaceEmbedding
from llama_index import ServiceContext
from llama_index.schema import Document


def read_pdf(uploaded_file):
    pdf_reader = PdfReader(uploaded_file)
    text = ""
    for page_num in range(len(pdf_reader.pages)):
        text += pdf_reader.pages[page_num].extract_text()
    return text

def querying(query_engine):
    progress_container = st.empty()
    query = st.text_input("Enter the Query for PDF:")
    submit = st.button("Generate The response for the query")

    if submit:
         progress_container.text("Fetching the response...")
         response = query_engine.query(query)
         st.write(f"**Response:** {response}")
        
        
        # docs = document_search.similarity_search(query_text)
        # output = chain.run(input_documents=docs, question=query_text)
        # st.write(output)
            
def main():
    st.title("PdfQuerier using LLAMA by Rahul Bhoyar")
    hf_token = st.text_input("Enter your Hugging Face token:")
    llm = HuggingFaceInferenceAPI(model_name="HuggingFaceH4/zephyr-7b-alpha", token=hf_token)
    uploaded_file = st.file_uploader("Choose a PDF file", type=["pdf"])

    if uploaded_file is not None:
        file_contents = read_pdf(uploaded_file)
        documents = Document(text=file_contents)
        documents = [documents]
        st.success("Documents loaded successfully!")
         
        embed_model_uae = HuggingFaceEmbedding(model_name="WhereIsAI/UAE-Large-V1")
        service_context = ServiceContext.from_defaults(llm=llm, chunk_size=800, chunk_overlap=20, embed_model=embed_model_uae)
         
         # Indexing the documents
        progress_container = st.empty()
        progress_container.text("Creating VectorStoreIndex...")
        # Download embeddings from OpenAI
        
        index = VectorStoreIndex.from_documents(documents, service_context=service_context, show_progress=True)
        index.storage_context.persist()
        query_engine = index.as_query_engine()
        st.success("VectorStoreIndex created successfully!")
        
        querying(query_engine)
        
                
if __name__ == "__main__":
    main()