app.py

import openai
import tiktoken

import collections
import threading
import datetime
import time
import pytz
import json
import os

openai.api_keys = os.getenv('API_KEYs').split("\n")
openai.api_key = openai.api_keys[0]

timezone = pytz.timezone('Asia/Shanghai')
timestamp2string = lambda timestamp: datetime.datetime.fromtimestamp(timestamp).astimezone(timezone).strftime('%Y-%m-%d %H:%M:%S')

def num_tokens_from_messages(messages, model="gpt-3.5-turbo"):
    """Returns the number of tokens used by a list of messages."""
    try:
        encoding = tiktoken.encoding_for_model(model)
    except KeyError:
        encoding = tiktoken.get_encoding("cl100k_base")
    if model == "gpt-3.5-turbo":  # note: future models may deviate from this
        num_tokens = 0
        for message in messages:
            num_tokens += 4  # every message follows <im_start>{role/name}\n{content}<im_end>\n
            for key, value in message.items():
                num_tokens += len(encoding.encode(value))
                if key == "name":  # if there's a name, the role is omitted
                    num_tokens += -1  # role is always required and always 1 token
        num_tokens += 2  # every reply is primed with <im_start>assistant
        return num_tokens
    else:
        raise NotImplementedError(f"""num_tokens_from_messages() is not presently implemented for model {model}.
See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens.""")


qas = []
qs = []
start_time = time.time()

def read_qs(qs, qas):
    directory = "./dialogues_set"
    filenames = [
        'dialogues_film.json', 
        'dialogues_jindong.json', 
        'dialogues_music.json', 
        'dialogues_natural.json', 
        'dialogues_taobao.json', 
        'dialogues_travel_kd.json'
    ]
    for filename in filenames:
        with open(f"{directory}/{filename}", "r", encoding="utf-8") as f:
            for idx,line in enumerate(f):
                idx2query = json.loads(line)
                query = idx2query[str(idx)]
                qs.append(query)
    print(f"read {len(qs)} queries from files")
    
    if os.path.exists("qas.json"):
        with open("qas.json", "r", encoding="utf-8") as f:
            qas = json.loads(f.read())
        print(f"read {len(qas)} query-responses from qas.json")
        
        existed_qs = collections.Counter([qa["q"] for qa in qas])
        remained_qs = []
        for q in qs:
            if existed_qs[q]>0:
                existed_qs[q] -= 1
            else:
                remained_qs.append(q)
        print(f"filter out {len(qs)-len(remained_qs)} with reference to qas.json")
        qs = remained_qs
    
    return qs


def ask(query, max_attempt_times=3):
    answer = None
    attempt_times = 0
    while answer is None and attempt_times<max_attempt_times:
        attempt_times += 1
        try:
            answer = openai.ChatCompletion.create(
                model="gpt-3.5-turbo",
                messages=[
                    {"role": "user", "content": query}
                ]
            )["choices"][0]["message"]["content"]
        except Exception as e:
            print(e)
            if "You exceeded your current quota, please check your plan and billing details." in str(e):
                idx = openai.api_keys.index(openai.api_key)
                idx = (idx + 1) % len(openai.api_keys)
                openai.api_key = openai.api_keys[idx]
                print(f"switch api_key")
            print(f"retry in {attempt_times*10} seconds...")
            time.sleep(attempt_times*10)
    return answer


def askingChatGPT(qs, qas, min_interval_seconds=10):
    read_qs(qs, qas)
    
    for i, q in enumerate(qs):
        ask_start_time = time.time()
        
        a = ask(q)
        qas.append({"q":q, "a":a})
        
        ask_end_time = time.time()
        elapsed_time = ask_end_time - ask_start_time
        delayTime = min_interval_seconds - elapsed_time
        if delayTime>0:
            time.sleep(delayTime)
        
        print(f"{timestamp2string(time.time())}:  iterations:  {i} / {len(qs)} | elapsed time of this query (s):  {elapsed_time:.2f}")
    
    return


thread = threading.Thread(target=lambda :askingChatGPT(qs, qas))
thread.daemon = True
thread.start()


import gradio as gr


def showcase(access_key):
    if not access_key==os.getenv('access_key'):
        chatbot_ret = [(f"Your entered Access Key:<br>{access_key}<br>is incorrect.", f"So i cannot provide you any information in this private space.")]
    else:
        recent_qas = qas[-10:]
        chatbot_ret = [(f"Your entered Access Key is correct.", f"The latest {len(recent_qas)} query-responses are displayed below.")]
        for qa in recent_qas:
            chatbot_ret += [(qa["q"].replace("\n","<br>"), str(qa["a"]).replace("\n","<br>"))]
    return chatbot_ret


def download(access_key):
    if not access_key==os.getenv('access_key'):
        chatbot_ret = [(f"Your entered Access Key:<br>{access_key}<br>is incorrect.", f"So i cannot provide you any information in this private space.")]
        file_ret = gr.File.update(value=None, visible=False)
    else:
        chatbot_ret = [(f"Your entered Access Key is correct.", f"The file containing all processed query-responses ({len(qas)} in total) can be downloaded below.")]
        filename = f"qas-{len(qas)}.json"
        with open(filename, "w", encoding="utf-8") as f:
            f.write(json.dumps(qas, ensure_ascii=False, indent=2))
        file_ret = gr.File.update(value=filename, visible=True)
    return chatbot_ret, file_ret


def display(access_key):
    if not access_key==os.getenv('access_key'):
        chatbot_ret = [(f"Your entered Access Key:<br>{access_key}<br>is incorrect.", f"So i cannot provide you any information in this private space.")]
    elif len(qas)<1:
        chatbot_ret = [(f"Your entered Access Key is correct.", f"But the progress has just started for a while and has no useful progress information to provide.")]
    else:
        time_takes = time.time() - start_time
        time_remains = time_takes * (len(qs)-len(qas)) / len(qas)
        end_time = start_time + time_remains
        
        messages = []
        for qa in qas:
            messages.append({"role":"user", "content":qa["q"]})
            messages.append({"role":"assistant", "content":qa["a"]})
        num_tokens_processed = num_tokens_from_messages(messages)
        num_tokens_total = int(num_tokens_processed * len(qs) / len(qas))
        dollars_tokens_processed = 0.002 * int(num_tokens_processed/1000)
        dollars_tokens_total = 0.002 * int(num_tokens_total/1000)
        
        chatbot_ret = [(f"Your entered Access Key is correct.", f"The information of progress is displayed below.")]
        chatbot_ret += [(f"The number of processed / total queries:", f"{len(qas)} / {len(qs)}")]
        chatbot_ret += [(f"The hours already takes / est. remains:", f"{time_takes/3600:.2f} / {time_remains/3600:.2f}")]
        chatbot_ret += [(f"The time starts / est. ends:", f"{timestamp2string(start_time)} / {timestamp2string(end_time)}")]
        chatbot_ret += [(f"The number of processed / est. total tokens:", f"{num_tokens_processed} / {num_tokens_total}")]
        chatbot_ret += [(f"The dollars of processed / est. total tokens:", f"{dollars_tokens_processed:.2f} / {dollars_tokens_total:.2f}")]
        
    return chatbot_ret


with gr.Blocks() as demo:

    gr.Markdown(
        """
        Hello friends,
        
        Thanks for your attention on this space. But this space is for my own use, i.e., building a dataset with answers from ChatGPT, and the access key for runtime feedback is only shared to my colleagues.
        
        If you want to ask ChatGPT on Huggingface just as the title says, you can try this [one](https://huggingface.co/spaces/zhangjf/chatbot) I built for public.
        """
    )
    
    with gr.Column(variant="panel"):
        chatbot = gr.Chatbot()
        txt = gr.Textbox(show_label=False, placeholder="Enter your Access Key to access this private space").style(container=False)
        with gr.Row():
            button_showcase = gr.Button("Show Recent Query-Responses")
            button_download = gr.Button("Download All Query-Responses")
            button_display = gr.Button("Display Progress Infomation")
    
    downloadfile = gr.File(None, interactive=False, show_label=False, visible=False)
    
    button_showcase.click(fn=showcase, inputs=[txt], outputs=[chatbot])
    button_download.click(fn=download, inputs=[txt], outputs=[chatbot, downloadfile])
    button_display.click(fn=display, inputs=[txt], outputs=[chatbot])

demo.launch()