app.py

from typing import Any, List, Tuple
import gradio as gr
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
from langchain.chains import ConversationalRetrievalChain
from langchain_openai import ChatOpenAI
from langchain_community.document_loaders import PyMuPDFLoader
import fitz
from PIL import Image
import os
import re
import openai

openai.api_key = "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"

def add_text(history: List[Tuple[str, str]], text: str) -> List[Tuple[str, str]]:
    if not text:
        raise gr.Error("Enter text")
    history.append((text, ""))
    return history

class MyApp:
    def __init__(self) -> None:
        self.OPENAI_API_KEY: str = openai.api_key
        self.chain = None
        self.chat_history: list = []
        self.documents = None
        self.file_name = None

    def __call__(self, file: str) -> ConversationalRetrievalChain:
        if self.chain is None:
            self.chain = self.build_chain(file)
        return self.chain

    def process_file(self, file) -> Image.Image:
        loader = PyMuPDFLoader(file.name)
        self.documents = loader.load()
        pattern = r"/([^/]+)$"
        match = re.search(pattern, file.name)
        try:
            self.file_name = match.group(1)
        except:
            self.file_name = os.path.basename(file)
        doc = fitz.open(file.name)
        page = doc[0]
        pix = page.get_pixmap(dpi=150)
        image = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
        return image

    def build_chain(self, file) -> str:
        embeddings = OpenAIEmbeddings(openai_api_key=self.OPENAI_API_KEY)
        pdfsearch = Chroma.from_documents(
            self.documents,
            embeddings,
            collection_name=self.file_name,
        )
        self.chain = ConversationalRetrievalChain.from_llm(
            ChatOpenAI(temperature=0.0, openai_api_key=self.OPENAI_API_KEY),
            retriever=pdfsearch.as_retriever(search_kwargs={"k": 1}),
            return_source_documents=True,
        )
        return "Vector database built successfully!"

def get_response(history, query, file):
    if not file:
        raise gr.Error(message="Upload a PDF")
    chain = app(file)
    try:
        result = chain.invoke(
            {"question": query, "chat_history": app.chat_history}
        )
        app.chat_history.append((query, result["answer"]))
        source_docs = result["source_documents"]
        source_texts = []
        for doc in source_docs:
            source_texts.append(f"Page {doc.metadata['page'] + 1}: {doc.page_content}")
        source_texts_str = "\n\n".join(source_texts)
        for char in result["answer"]:
            history[-1][-1] += char
        return history, source_texts_str
    except Exception:
        app.chat_history.append((query, "I have no information about it. Feed me knowledge, please!"))
        return history, "I have no information about it. Feed me knowledge, please!"

def render_file(file) -> Image.Image:
    doc = fitz.open(file.name)
    page = doc[0]
    pix = page.get_pixmap(dpi=150)
    image = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
    return image

def purge_chat_and_render_first(file) -> Tuple[Image.Image, list]:
    app.chat_history = []
    doc = fitz.open(file.name)
    page = doc[0]
    pix = page.get_pixmap(dpi=150)
    image = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
    return image, []

def refresh_chat():
    app.chat_history = []
    return []

app = MyApp()

with gr.Blocks() as demo:
    with gr.Tab("Step 1: Upload PDF"):
        btn = gr.UploadButton("📁 Upload a PDF", file_types=[".pdf"])
        show_img = gr.Image(label="Uploaded PDF")

    with gr.Tab("Step 2: Process File"):
        process_btn = gr.Button("Process PDF")
        show_img_processed = gr.Image(label="Processed PDF")
        process_status = gr.Textbox(label="Processing Status", interactive=False)

    with gr.Tab("Step 3: Build Vector Database"):
        build_vector_btn = gr.Button("Build Vector Database")
        status_text = gr.Textbox(label="Status", value="", interactive=False)

    with gr.Tab("Step 4: Ask Questions"):
        chatbot = gr.Chatbot(elem_id="chatbot")
        txt = gr.Textbox(
            show_label=False,
            placeholder="Enter text and press submit",
            scale=2
        )
        submit_btn = gr.Button("Submit", scale=1)
        refresh_btn = gr.Button("Refresh Chat", scale=1)
        source_texts_output = gr.Textbox(label="Source Texts", interactive=False)

    btn.upload(
        fn=purge_chat_and_render_first,
        inputs=[btn],
        outputs=[show_img, chatbot],
    )

    process_btn.click(
        fn=lambda file: (app.process_file(file), "Processing complete!"),
        inputs=[btn],
        outputs=[show_img_processed, process_status],
    )

    build_vector_btn.click(
        fn=app.build_chain,
        inputs=[btn],
        outputs=[status_text],
    )

    submit_btn.click(
        fn=add_text,
        inputs=[chatbot, txt],
        outputs=[chatbot],
        queue=False,
    ).success(
        fn=get_response, inputs=[chatbot, txt, btn], outputs=[chatbot, source_texts_output]
    )

    refresh_btn.click(
        fn=refresh_chat,
        inputs=[],
        outputs=[chatbot],
    )

demo.queue()
demo.launch()