app.py

import json
import time
import random

import gradio as gr
import pandas as pd

from utils.gpt_processor import QuestionAnswerer

qa_processor = QuestionAnswerer()
current_file = None
context = None

with open("final_result.json", 'r', encoding='UTF-8') as fp:
    db = json.load(fp)

def read_examples():
    df = pd.read_csv(r'examples.csv')
    return [f"{keyword}" for keyword in df['word'].tolist()]

def user(message, history):
    #return gr.update(value="", interactive=False), history + [[message, None]]
    return "", history + [[message, None]]

def bot(history):
    user_message = history[-1][0]
    global current_file
    global context
    #check if user input has "我想了解"
    if "我想了解" in user_message:
        # get keyword from "「」"
        keyword = user_message.split("「")[1].split("」")[0]
        # check if keyword is in db
        file_list = []
        for key in db.keys():
            if keyword in db[key]['keywords']:
                file_list.append(key)
        if len(file_list) == 0:
            response = [
                [user_message, "Sorry, I can't find any documents about this topic. Please try again."],
            ]
        else:
            bot_message = "以下是我所找到的文件："
            for file in file_list:
                bot_message += "\n" + file
            bot_message += "\n\n" + "請複製貼上想要了解的文件，我會給你該文件的摘要"
            response = [
                [user_message, bot_message],
            ]
        history = response
        # history[-1][1] = ""
        # for character in bot_message:
        #     history[-1][1] += character
        #     time.sleep(random.uniform(0.01, 0.05))
        #     yield history
        return history
    
    # check if user input has a pdf file name
    if ".pdf" in user_message or ".docx" in user_message:
        current_file = user_message
        context = db[current_file]['file_full_content']
        # check if file name is in db
        if user_message in db.keys():
            bot_message = f"文件 {user_message} 的摘要如下："
            bot_message += "\n\n" + db[user_message]['summarized_content']
            bot_message += "\n\n" + "可以透過詢問來了解更多這個文件的內容"
            response = [
                [user_message, bot_message],
            ]
        else:
            response = [
                [user_message, "Sorry, I can't find this file. Please try again."],
            ]
        history[-1] = response[0]
        # history[-1][1] = ""
        # for character in bot_message:
        #     history[-1][1] += character
        #     time.sleep(random.uniform(0.01, 0.05))
        #     yield history
        return history
    if context is None:
        response = [
            [user_message, "請輸入一個文件名稱或是點選下方的範例"],
        ]
        history[-1] = response[0]
        return history
    
    if context is not None:
        bot_message = qa_processor.answer_question(context, user_message)
        response = [
            [user_message, bot_message],
        ]
        history[-1] = response[0]
        return history

with gr.Blocks() as demo:
    history = gr.State([])
    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("Clear")

                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)


    examples = gr.Examples(examples=read_examples(),
                           inputs=[user_input])
    
if __name__ == "__main__":
    demo.launch()