app.py

import json
import time
import random
import os

import openai
import gradio as gr
import pandas as pd
import numpy as np
from openai.embeddings_utils import distances_from_embeddings

from utils.gpt_processor import QuestionAnswerer
from utils.work_flow_controller import WorkFlowController
from utils.chatbot import Chatbot
from utils.utils import *
    
def create_chatbot():
    bot = Chatbot()
    return bot

with gr.Blocks() as demo:
    history = gr.State([])
    user_question = gr.State("")
    chatbot_utils = Chatbot()

    user_chatbot = gr.State(Chatbot())
    
    upload_state = gr.State("wating")
    finished = gr.State("finished")

    with gr.Row():
        gr.HTML('Junyi Academy Chatbot')
    with gr.Row(equal_height=True):
        with gr.Column(scale=5):
            with gr.Row():
                chatbot = gr.Chatbot()

            with gr.Row():
                with gr.Column(scale=12):
                    user_input = gr.Textbox(
                        show_label=False,
                        placeholder="Enter text",
                        container=False,
                    )

                with gr.Column(min_width=70, scale=1):
                    clear_btn = gr.Button("清除")
                with gr.Column(min_width=70, scale=1):
                    submit_btn = gr.Button("傳送")

                bot_args = dict(
                    fn=bot,
                    inputs=user_chatbot,
                    outputs=chatbot,
                )

                user_args = dict(
                    fn=user,
                    inputs=[user_chatbot, user_input],
                    outputs=[user_input, chatbot],
                    queue=False,
                )

                response = user_input.submit(**user_args).then(**bot_args)
                
                response.then(lambda: gr.update(interactive=True), None, [user_input], queue=False)

                submit_btn.click(user,
                                [user_input, chatbot], 
                                [user_input, chatbot],
                                chatbot,
                                queue=False).then(**bot_args).then(lambda: gr.update(interactive=True), None, [user_input], queue=False)
                
                

    with gr.Row():
        index_file = gr.File(file_count="multiple", file_types=["pdf"], label="Upload PDF file")
    
    with gr.Row():
        instruction = gr.Markdown("""
            ## 使用說明
            1. 上傳一個或多個 PDF 檔案，系統將自動進行摘要、翻譯等處理後建立知識庫
            2. 在上方輸入欄輸入問題，系統將自動回覆
            3. 可以根據下方的摘要內容來提問
            4. 每次對話會根據第一個問題的內容來檢索所有文件，並挑選最能回答問題的文件來回覆
            5. 要切換檢索的文件，請點選「清除對話記錄」按鈕後再重新提問
        """)

    with gr.Row():
        describe = gr.Markdown('', visible=True)

        
    clear_state_args = dict(
        fn=clear_state,
        inputs=user_chatbot,
        outputs=None,
    )

    clear_btn.click(**clear_state_args)

    send_system_nofification_args = dict(
        fn=send_system_nofification,
        inputs=user_chatbot,
        outputs=chatbot,
    )

    bulid_knowledge_base_args = dict(
        fn=build_knowledge_base,
        inputs=[user_chatbot, index_file],
        outputs=None,
    )

    change_md_args = dict(
        fn=change_md,
        inputs=[user_chatbot],
        outputs=[describe],
    )

    index_file.upload(**send_system_nofification_args) \
                  .then(lambda: gr.update(interactive=True), None, None, queue=False) \
                  .then(**bulid_knowledge_base_args) \
                  .then(**send_system_nofification_args) \
                  .then(lambda: gr.update(interactive=True), None, None, queue=False) \
                  .then(**change_md_args)
    
if __name__ == "__main__":
    demo.launch()