chatbot.py

from load_models import *

import gradio as gr


import time
# def evaluate(*args, **kwargs):
#     rs = "### Instruction\n\ntest\n\n### Response:\n"
#     for i in "Hello!!!"+str(time.time())+"\n### Bad\n":
#         rs+=i
#         yield rs

import random

with gr.Blocks() as demo:
    gr.Markdown("## Online Chatbot")
    chatbot = gr.Chatbot()
    msg = gr.Textbox()
    
    def add_file(history, file):
        history = history + [((file.name,), None)]
        return history

    def user(user_message, history):
        return "", history + [[user_message, None]]
    
    def getInstruction(history):
        prompt = "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\nAs an AI model, you are reqired to generate response according to the chat history."
        # here add database log

        if len(history)>1:
            prompt+="\nThe history chat is:\n"
            for i in history[:-1]:
                prompt+="\nInput:\n"+i[0]+"\nResponse:\n"+i[1]+"\n" if type(i[0]) == str else "\nInput:\n"+i[1]+"\n"

        prompt+="\n\n### Input:\n"+history[-1][0]+"\n\n### Response:\n" 
        print(prompt)
        return prompt

    def callchat(history, log_box, temperature, top_p, top_k, num_beams, max_new_tokens):
        log_box += time.strftime("d% %b %Y %H:%M:%S: ", time.gmtime(time.time())) + "start streaming"
        yield {log_box: log_box}
        history[-1][1] = ""

        prompt = getInstruction(history)
        for current_response, decodeded_output in alpaca_evaluate(prompt,temperature, top_p, top_k, num_beams, max_new_tokens):

            history[-1][1] = current_response
            yield {history:history, decodeded_output:decodeded_output}
        log_box += time.strftime("d% %b %Y %H:%M:%S: ", time.gmtime(time.time())) + "end of streaming"
        yield {log_box: log_box}

    def bot(history):
        bot_message = random.choice(["User uploaded a picture.", "I am still learning to recognize pictures.", "I'm still learning."])
        history[-1][1] = ""
        for character in bot_message:
            history[-1][1] += character
            yield history
        
    with gr.Row():
        with gr.Column(scale=0.85):
            submit_btn = gr.Button(value="Submit",variant="primary")
        with gr.Column(scale=0.15, min_width=0):
            upload_btn = gr.UploadButton("📁", file_types=["image", "video", "audio"])
        with gr.Column(scale=0.15, min_width=0):
            clear = gr.Button("Clear")
    
    with gr.Accordion("Open for More!", open=False):
        with gr.Row():
            temperature = gr.Slider(
                minimum=0, maximum=1, value=0.1, label="Temperature"
            )
            top_p = gr.Slider(
                minimum=0, maximum=1, value=0.75, label="Top p"
            )
            top_k = gr.Slider(
                minimum=0, maximum=100, step=1, value=40, label="Top k"
            )
            num_beams = gr.Slider(
                minimum=1, maximum=4, step=1, value=1, label="Beams"
            )
            max_new_tokens = gr.Slider(
                minimum=16, maximum=2000, step=1, value=256, label="Max tokens"
            )
        with gr.Row():
            developer_box = gr.TextArea(label="Original Output",interactive = True,lines = 10)
            log_box = gr.TextArea(label="Log Output",interactive = True,lines = 10)

    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
        callchat, [chatbot, log_box, temperature, top_p, top_k, num_beams, max_new_tokens], [chatbot, developer_box, log_box]
    )
    submit_btn.click(user, [msg, chatbot], [msg, chatbot], queue=False).then(
        callchat, [chatbot, log_box, temperature, top_p, top_k, num_beams, max_new_tokens], [chatbot, developer_box, log_box]
    )
    upload_btn.upload(add_file, [chatbot, upload_btn], [chatbot]).then(
        bot, chatbot, chatbot
    )
    clear.click(lambda: None, None, chatbot, queue=False)
    
demo.queue(concurrency_count=3)
if __name__ == "__main__":
    demo.launch(server_name = server_name, server_port = server_port, share_gradio = share_gradio)