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

qa_processor = QuestionAnswerer()
CSV_FILE_PATHS = ''
JSON_FILE_PATHS = ''
KNOWLEDGE_BASE = None
CONTEXT = None
CONTEXT_PAGE_NUM = None
CONTEXT_FILE_NAME = None

def build_knowledge_base(files):
    global CSV_FILE_PATHS
    global JSON_FILE_PATHS
    global KNOWLEDGE_BASE

    work_flow_controller = WorkFlowController(files)
    CSV_FILE_PATHS = work_flow_controller.csv_result_path
    JSON_FILE_PATHS = work_flow_controller.result_path
    with open(CSV_FILE_PATHS, 'r', encoding='UTF-8') as fp:
        knowledge_base = pd.read_csv(fp)
    knowledge_base['page_embedding'] = knowledge_base['page_embedding'].apply(eval).apply(np.array)
    KNOWLEDGE_BASE = knowledge_base

def construct_summary():
    with open(JSON_FILE_PATHS, 'r', encoding='UTF-8') as fp:
        knowledge_base = json.load(fp)

    context = """"""
    for key in knowledge_base.keys():
        file_name = knowledge_base[key]['file_name']
        total_page = knowledge_base[key]['total_pages']
        summary = knowledge_base[key]['summarized_content']
        file_context = f"""
            ##＃ 文件摘要
            {file_name}  (共 {total_page} 頁)<br><br>
            {summary}<br><br>
        """
        context += file_context
    return context

def change_md():
    content = construct_summary()
    return gr.Markdown.update(content, visible=True)

def user(message, history):
    return "", history + [[message, None]]

def system_notification(action):
    if action == 'upload':
        return [['已上傳文件', '文件處理中（摘要、翻譯等），結束後將自動回覆']]
    else:
        return [['已上傳文件', '文件處理完成，請開始提問']]
    
def get_index_file(user_message):
    global KNOWLEDGE_BASE
    global CONTEXT
    global CONTEXT_PAGE_NUM
    global CONTEXT_FILE_NAME

    user_message_embedding = openai.Embedding.create(input=user_message, engine='text-embedding-ada-002')['data'][0]['embedding']
    KNOWLEDGE_BASE['distance'] = distances_from_embeddings(user_message_embedding, KNOWLEDGE_BASE['page_embedding'].values, distance_metric='cosine')
    KNOWLEDGE_BASE = KNOWLEDGE_BASE.sort_values(by='distance', ascending=True).head(1)
    if KNOWLEDGE_BASE['distance'].values[0] > 0.2:
        CONTEXT = None
    else:

        CONTEXT = KNOWLEDGE_BASE['page_content'].values[0]
        CONTEXT_PAGE_NUM = KNOWLEDGE_BASE['page_num'].values[0]
        CONTEXT_FILE_NAME = KNOWLEDGE_BASE['file_name'].values[0]

def bot(history):
    user_message = history[-1][0]
    global CONTEXT
    print(f'user_message: {user_message}')

    if KNOWLEDGE_BASE is None:
        response = [
            [user_message, "請先上傳文件"],
        ]
        history = response
        return history
    elif CONTEXT is None:
        get_index_file(user_message)
        print(f'CONTEXT: {CONTEXT}')
        if CONTEXT is None:
            response = [
                [user_message, "無法找到相關文件，請重新提問"],
            ]
            history = response
            return history
    else:
        pass

    if CONTEXT is not None:
        bot_message = qa_processor.answer_question(CONTEXT, CONTEXT_PAGE_NUM, CONTEXT_FILE_NAME, history)
        print(f'bot_message: {bot_message}')
        response = [
            [user_message, bot_message],
        ]
        history[-1] = response[0]
        return history
    
def clear_state():
    global CONTEXT
    global CONTEXT_PAGE_NUM
    global CONTEXT_FILE_NAME

    CONTEXT = None
    CONTEXT_PAGE_NUM = None
    CONTEXT_FILE_NAME = None

with gr.Blocks() as demo:
    history = gr.State([])
    upload_state = gr.State("upload")
    finished = gr.State("finished")
    user_question = gr.State("")
    with gr.Row():
        gr.HTML('Junyi Academy Chatbot')
        #status_display = gr.Markdown("Success", elem_id="status_display")
    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):
                #     submit_btn = gr.Button("Send")
                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")

                response = user_input.submit(user, 
                                  [user_input, chatbot], 
                                  [user_input, chatbot], 
                                  queue=False,
                                  ).then(bot, chatbot, chatbot)
                response.then(lambda: gr.update(interactive=True), None, [user_input], queue=False)

                clear_btn.click(lambda: None, None, chatbot, queue=False)

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

        index_file.upload(system_notification, [upload_state], chatbot) \
                  .then(lambda: gr.update(interactive=True), None, None, queue=False) \
                  .then(build_knowledge_base, [index_file]) \
                  .then(system_notification, [finished], chatbot) \
                  .then(lambda: gr.update(interactive=True), None, None, queue=False) \
                  .then(change_md, None, describe)
                  
    
if __name__ == "__main__":
    demo.launch()