app.py

import os
import gradio as gr
import openai
from langchain import hub
from langchain_community.document_loaders import PyPDFLoader
from langchain_community.vectorstores import Chroma
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
from pypdf import PdfReader, PdfWriter
from pathlib import Path
from typing import List


def build_rag_chain(pdf_paths: List[str], chunk_size: int, chunk_overlap: int, model_name: str):
    loaders = [PyPDFLoader(path) for path in pdf_paths]

    docs = []
    for loader in loaders:
        docs.extend(
            loader.load()[0:] # skip first page
        )


    text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, 
                                                chunk_overlap=chunk_overlap)

    splits = text_splitter.split_documents(docs)

    vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings())
    retriever = vectorstore.as_retriever()
    prompt = hub.pull("rlm/rag-prompt")

    # model_name = 'gpt-3.5-turbo-0125'
    # model_name = 'gpt-4-1106-preview'
    # model_name = 'gpt-4-0125-preview'
    llm = ChatOpenAI(model_name=model_name, temperature=0)

    def format_docs(docs):
        return '\n\n'.join(doc.page_content for doc in docs)

    rag_chain = (
        {"context": retriever | format_docs, "question": RunnablePassthrough()}
        | prompt
        | llm
        | StrOutputParser()
    )

    return rag_chain


def predict(query: str, pdf_id: str =None, user_id: str = None, chunk_size: int =1000, chunk_overlap: int =200, model_name: str ='gpt-4-0125-preview'):
    print(type(pdf_id))
    # print(user_id)
    if pdf_id:
        # pdf_path = Path(pdf_file)
        # pdf_reader = PdfReader(pdf_path)
        # pdf_writer = PdfWriter()


        # pdf_name = pdf_file.split('/')[-1]
        # pdf_path = data_root + pdf_name

        # if pdf_path not in load_pdf_paths(data_root):
        #     print('Saving file...')
        #     for page in pdf_reader.pages:
        #         pdf_writer.add_page(page)
            
        #     with open(pdf_path, 'wb') as f:
        #         pdf_writer.write(f)
        # os.system("ls data/pdf")
            
    # pdf_paths = load_pdf_paths(data_root)
        rag_chain = build_rag_chain([pdf_id], chunk_size=chunk_size, chunk_overlap=chunk_overlap, model_name=model_name)
        return rag_chain.invoke(query)
    return "Please upload PDF file"

# examples = [
#     "هل هناك غرامة للتخلف عن سداد ضريبة القيمة المضافة؟",
#     "ما هي ضريبة القيمة المضافة؟",
#     "ما الواجب على الخاضغين لضريبة القيمة المضافة؟",
#     "من هو الشخص الخاضغ لضريبة القيمة المضافة؟",
#     "متى يجب على الشخص التسجيل لضريبة القيمة المضافة؟",
#     "أريد بيع منزل, هل يخضع ذلك لضريبة القيمة المضافة؟"
# ]

textbox = gr.Textbox(label="اكتب سؤالك هنا", placeholder="", lines=4)
upload_btn = gr.UploadButton(label='Upload a PDF file.')

iface = gr.Interface(fn=predict, inputs=[textbox, upload_btn], outputs="text")
iface.launch(share=True)