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app.py

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+import openai
+import tiktoken
+
+import numpy as np
+import concurrent
+import collections
+import threading
+import datetime
+import time
+import pytz
+import json
+import os
+
+openai.api_key = os.getenv('API_KEY')
+
+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
+        len_values = 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():
+                try:
+                    num_tokens += len(encoding.encode(value))
+                except:
+                    num_tokens += int(num_tokens/len_values*len(value)) # linear estimation
+                len_values += len(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.""")
+
+
+def read_tasks():
+    with open(f"qas.json", "r", encoding="utf-8") as f:
+        qas = json.loads(f.read())
+    qs = [{"q":qa["q"], "a":qa["a"]} for qa in qas if qa["a"] is None] # 还未请求处理的queries
+    qas = [{"q":qa["q"], "a":qa["a"]} for qa in qas if qa["a"] is not None] # 已经完成请求处理的queries
+    print(f"read {len(qs)} queries without responses from qas.json")
+    print(f"read {len(qas)} queries with responses from qas.json")
+    return qs, qas
+
+qs, qas = read_qs()
+start_time = time.time()
+num_read_qas = len(qas)
+
+def ask(query, timeout=600):
+    answer = None
+    dead_time = time.time() + timeout
+    attempt_times = 0
+    while answer is None and time.time()<dead_time and attempt_times<10:
+        try:
+            messages=[
+                {"role": "user", "content": query}
+            ]
+            if num_tokens_from_messages(messages)>4096:
+                return None
+            answer = openai.ChatCompletion.create(
+                model="gpt-3.5-turbo",
+                messages=messages
+            )["choices"][0]["message"]["content"]
+        except Exception as e:
+            if time.time()<dead_time:
+                print(e)
+                if "Please reduce the length of the messages." in str(e):
+                    return None
+                else:
+                    attempt_times += 1
+                    wait_time = int(attempt_times*10)
+                    time.sleep(wait_time)
+                    print(f"retry in {attempt_times*10} seconds...")
+    return answer
+
+
+def askingChatGPT(qs, qas, min_interval_seconds=3, max_interval_seconds=15, max_retry_times=3):
+    
+    history_elapsed_time = [max_interval_seconds]*10
+    return
+    for i, q in enumerate(qs):
+        ask_start_time = time.time()
+        
+        # 最直接的方法，调用ask函数，但可能因为超时等原因阻塞住
+        #a = ask(q)
+        
+        # 下面是我之前设计的一系列，超时->重试，的方法
+        def ask_(q, timeout):
+            executor = concurrent.futures.ThreadPoolExecutor()
+            future = executor.submit(ask, q, timeout)  # 提交函数调用任务
+            try:
+                a = future.result(timeout=timeout)  # 等待函数调用任务完成，超时时间为30秒
+                return a
+            except concurrent.futures.TimeoutError:
+                print(f"ask call timed out after {timeout:.2f} seconds, retrying...")
+            executor.shutdown(wait=False)
+            return ask_(q, timeout*2)  # 当超时时，重新调用函数
+        
+        retry_times = 0
+        a = None
+        while a is None and retry_times<max_retry_times:
+            a = ask_(q, timeout=max(max_interval_seconds,np.mean(sorted(history_elapsed_time)[:8])))
+            retry_times += 1
+        
+        qas.append({"q":q, "a":a})
+        
+        ask_end_time = time.time()
+        elapsed_time = ask_end_time - ask_start_time
+        history_elapsed_time = history_elapsed_time[1:] + [elapsed_time]
+        delayTime = min_interval_seconds - elapsed_time
+        if delayTime>0:
+            time.sleep(delayTime)
+        
+        print(f"{timestamp2string(time.time())}:  iterations:  {i+1} / {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.startswith(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=4))
+        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)-num_read_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:
+        num_total_qs, num_processed_qs = len(qs), len(qas) - num_read_qas
+        time_takes = time.time() - start_time
+        time_remains = time_takes * (num_total_qs-num_processed_qs) / num_processed_qs
+        end_time = start_time + time_takes + time_remains
+        
+        messages = []
+        for qa in qas:
+            messages.append({"role":"user", "content":qa["q"]})
+            messages.append({"role":"assistant", "content":qa["a"] or ""})
+        num_tokens_processed = num_tokens_from_messages(messages)
+        num_tokens_total = int(num_tokens_processed * (num_total_qs+num_read_qas) / (num_processed_qs+num_read_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"{num_processed_qs} / {num_total_qs} (+{num_read_qas})")]
+        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()

qas.json

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+[
+    {
+        "q": "�й����׶����ĸ�����",
+        "a": "����"
+    },
+    {
+        "q": "�������׶����ĸ�����",
+        "a": null
+    }
+]

requirements.txt

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+openai==0.27.0
+tiktoken==0.3.0