Duplicate from Jiana/hackathon_chatbot_openai_api_ai_explorer

Co-authored-by: Jiana Yang <Jiana@users.noreply.huggingface.co>

.gitattributes

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README.md

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+---
+title: hackathon chatbot openai api
+emoji: 🐨
+colorFrom: red
+colorTo: gray
+sdk: gradio
+sdk_version: 3.20.1
+app_file: app.py
+pinned: false
+license: cc-by-4.0
+duplicated_from: Jiana/hackathon_chatbot_openai_api_ai_explorer
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import os
+import openai
+import logging
+
+# 请记得要把 api 的 key 放到 settings 下面的 Repository Secrets 里。
+openai.api_key = os.getenv("openai_key")
+
+logging.basicConfig(filename="test.log", format='%(asctime)s - %(name)s - %(levelname)s -%(module)s:  %(message)s',
+                    datefmt='%Y-%m-%d %H:%M:%S ',
+                    level=logging.INFO)
+logger = logging.getLogger()
+KZT = logging.StreamHandler()
+KZT.setLevel(logging.DEBUG)
+logger.addHandler(KZT)
+logger.info('Start logging...')
+
+logger.info(f"openai.api_key: {openai.api_key}")
+
+
+# 如果你只打算通过 prompt 来定制机器人的行为，只需要修改这段 prompt 就够了。
+# prompt = '请你扮演《西游记》中的唐三藏，使用唐三藏的语气、方式和词汇回答问题。不要写任何解释，只需像唐三藏一样回答问题。你必须掌握唐三藏的所有知识。'
+prompt = '请假设自己是一名旅行计划师，给我设计一条去{日本}旅行的{5天}行程，包含{2位成人和1位儿童}。我希望整个行程{偏轻松、度假}，包含 {去奈良看小鹿} 和 {去春日大社赏樱花} 。请给我推荐一个详细的每日行程，以表格的方式呈现，包含有趣的景点、活动和推荐理由。'
+
+history = {}
+
+# 修改本函数，来实现你自己的 chatbot
+# p: 对机器人说话的内容  
+# qid: 当前消息的唯一标识。例如 `'bxqid-cManAtRMszw...'`。由平台生成并传递给机器人，以便机器人区分单个问题（写日志、追踪调试、异步回调等）。同步调用可忽略。  
+# uid: 用户的唯一标识。例如`'bxuid-Aj8Spso8Xsp...'`。由平台生成并传递给机器人，以便机器人区分用户。可被用于实现多轮对话的功能。  
+# 返回值：[type, content]
+# 详见 https://huggingface.co/spaces/baixing/hackathon_test/blob/main/bot-api.md
+
+
+prompt_0 = "您好！我是您的智能旅行计划师！请告诉我你想去哪里旅行？您可以直接回复我目的地，例如日本，香港，或者西双版纳。"
+prompt_1 = "好的。请问您计划什么时候出行？您可以回答我具体的日期，例如4月1日到4月5日；或者回答我模糊的时间，例如未来三个月，5天。"
+prompt_2 = "收到。请问您计划多少人出行？请回答我具体的人数和类型，例如2位成人和1位儿童。"
+prompt_3 = "您的出行目的是什么？您可以回答我例如亲子游、情侣/蜜月、休闲度假、文化体验、地标打卡、探险。"
+prompt_4 = "您有没有什么需要打卡的地点或者特殊需求？您可以告诉我具体的景点，例如金阁寺，或者特定的活动，例如赏樱花。"
+
+
+predefined_prompts = [
+    prompt_0,
+    prompt_1,
+    prompt_2,
+    prompt_3,
+    prompt_4
+]
+
+_user_response = ["pred def" for _ in range(6)]
+
+# count_i = 1
+count_i_dict = dict()
+
+def init_user_info(uid):
+    global count_i_dict
+    count_i_dict[uid] = dict()
+    count_i_dict[uid]["count_i"] = 1
+    count_i_dict[uid]["user_response"] = _user_response
+
+
+def get_count_i(uid):
+    global count_i_dict
+    if uid not in count_i_dict:
+        return 1
+    return count_i_dict[uid]["count_i"]
+
+def set_count_i(uid, delta=1):
+    global count_i_dict
+    if uid not in count_i_dict:
+        # TODO: call user prof init
+        return
+
+    count_i_dict[uid]["count_i"] += delta
+
+
+def get_user_response(uid, i=0):
+    global count_i_dict
+
+    if uid not in count_i_dict:
+        return ""
+
+    return count_i_dict[uid]["user_response"][i]
+
+def set_user_response(uid, i, current_p):
+    global count_i_dict
+    if uid not in count_i_dict:
+        # TODO: call user prof init
+        return
+
+    count_i_dict[uid]["user_response"][i] = current_p
+
+    
+
+
+def get_info(p, qid, uid):
+    global predefined_prompts    
+    # global user_response
+    # global count_i
+    global count_i_dict
+
+    if uid not in count_i_dict:
+        init_user_info(uid)
+        logger.info(f"Init info for user {uid}")
+        
+    # current_count_i = count_i_dict[uid]["count_i"]
+    current_count_i = get_count_i(uid)
+    # count_i_dict[uid]["user_response"][current_count_i-1] = p
+    set_user_response(uid, current_count_i-1, p)
+
+    # user_response[current_count_i-1] = p
+    # user_response[count_i-1] = p
+
+    
+    next_prompt = predefined_prompts[current_count_i]
+    set_count_i(uid, 1)
+    
+    # count_i += 1
+    # count_i_dict[uid]["count_i"] += 1
+    
+    logger.info(f"In get info loop: get from user: {p}")
+    logger.info(f"In get info loop: Our next prompt: {next_prompt}, uid={uid}, count_i={get_count_i(uid)}")
+    return ["text", next_prompt]
+    
+
+
+def chat(p, qid, uid):
+    # global user_response
+    # global count_i
+
+    global count_i_dict
+
+    count_i = get_count_i(uid)
+    # count_i = count_i_dict[uid]["count_i"]
+    
+    if count_i <= 4:
+        ret = get_info(p, qid, uid)
+        return ret
+    
+
+
+    if count_i == 5:  # 5
+        # user_response[4] = p
+        set_user_response(uid, count_i-1, p)
+        # prompt_0_response = user_response[0]
+        # prompt_1_response = user_response[1]
+        # prompt_2_response = user_response[2]
+        # prompt_3_response = user_response[3]
+        # prompt_4_response = user_response[4]
+
+        prompt_0_response = get_user_response(uid, 0)
+        prompt_1_response = get_user_response(uid, 1)
+        prompt_2_response = get_user_response(uid, 2)
+        prompt_3_response = get_user_response(uid, 3)
+        prompt_4_response = get_user_response(uid, 4)
+        
+        final_prompt = f"请假设自己是一名旅行计划师，给我设计一条去{prompt_0_response}旅行的{prompt_1_response}行程，包含{prompt_2_response}。我的出行目的是{prompt_3_response}，希望包含 {prompt_4_response} 。请给我推荐一个详细的每日行程，以表格的方式呈现，包含有趣的景点、活动和推荐理由。请尽量考虑可执行性。"
+        p = final_prompt
+        logger.info(f"Our final prompt: {final_prompt}")
+        logger.info(f"count_i: {count_i}")
+        
+        
+    # 找出该 uid 对应的历史对话
+    global history
+    if uid in history:
+        msgs = history[uid]
+    else:
+        msgs = []
+    
+    response = callapi(p, msgs)
+    history[uid] = msgs + [[p, response]]
+    logger.info(f"history: {msgs}")
+    logger.info(f"p: {p}")
+    logger.info(f"response: {response}")
+    # count_i_dict[uid]["count_i"] += 1
+    set_count_i(uid,1)
+
+    
+    return ["text", response]
+
+
+def callapi(p, msgs):
+    if (len(msgs) > 8):  #简单 hard-code 8 回合对话。如果需要更精准的，应该计算 token 数
+        msgs = msgs[-8:]
+        
+    data = [{"role":"system", "content":prompt}]
+    for m in msgs:
+        data = data + [
+            {"role":"user", "content":m[0]},
+            {"role":"assistant", "content":m[1]}
+        ]
+    data = data + [{"role":"user", "content":p}]
+    response = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo",
+        messages= data
+    )
+    print(response)
+    response = response["choices"][0]["message"]["content"]
+    while response.startswith("\n"):
+        response = response[1:]
+    return response
+
+iface = gr.Interface(fn=chat, 
+                     inputs=["text", "text", "text"], 
+                     outputs=["text", "text"],
+                     description="""您好！我是您的智能旅行计划师。请告诉我你想去哪里旅行？您可以直接回复我目的地，例如日本，香港，或者西双版纳。""")
+
+comments = """
+已添加多轮对话的极简示范，能将该 uid 的最近八条消息一起发给openai。本实现是内存中的，一旦重启即被清空。如需可持久的多轮对话，需要改用数据库等方式。
+注意：duplicate 本项目后，需要将你自己的 openai apikey 设置到 settings 的 Repository Secrets 里，否则运行会报错。[了解详情](https://huggingface.co/spaces/baixing/hackathon_chatbot_openai_api/blob/main/%E6%B7%BB%E5%8A%A0%20secret%20%E7%9A%84%E6%96%B9%E6%B3%95.jpg)    
+[对话测试](https://huggingface.co/spaces/BaixingAI/hackathon_test)   [参考文档](https://huggingface.co/spaces/baixing/hackathon_test/blob/main/bot-api.md)    [Q & A](https://huggingface.co/spaces/baixing/hackathon_test/blob/main/qna.md)
+                     
+"""
+
+iface.launch()

requirements.txt

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+openai==0.27.0