Update app.py

app.py

@@ -1,34 +1,86 @@
+import openai
+import os
 import gradio as gr
-from transformers import Conversation, pipeline
 
-# 创建聊天机器人pipeline，使用你的 api key
-chatbot_pipeline = pipeline("conversational", model="Llama-2-70b-chat-hf", use_auth_token="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")
+openai.api_key = os.environ.get("OPENAI_API_KEY")
 
-def chatbot_response(input_text, history=[]):
-    # 添加新的用户输入到对话历史
-    history.append(input_text)
-    
-    # 使用 Hugging Face's Conversation API 创建对话
-    conversation = Conversation(input_text)
-    
-    # 获取模型的回答
-    responses = chatbot_pipeline([conversation])
-    model_response = responses[-1].generated_responses[-1]
-    
-    # 添加模型的回答到对话历史
-    history.append(model_response)
+class Conversation:
+    def __init__(self, prompt, round):
+        self.prompt = prompt
+        self.round = round
+        self.messages = []
+        self.messages.append({"role": "system", "content": self.prompt})
+
+    def ask(self, question):
+        try:
+            self.messages.append({"role": "user", "content": question})
+            response = openai.ChatCompletion.create(
+                model="gpt-3.5-turbo",
+                messages=self.messages,
+                temperature=0.5,
+                max_tokens=2048,
+                top_p=1,
+            )
+        except Exception as e:
+            print(e)
+            return e
+
+        message = response["choices"][0]["message"]["content"]
+        self.messages.append({"role": "assistant", "content": message})
+
+        if len(self.messages) > self.round * 2 + 1:
+            text = self._build_message(self.messages)
+            #print (text)
+            #print ("=====summarize=====")
+            summarize = self.summarize(text)
+            #print (summarize)
+            #print ("=====summarize=====")
+            self.messages = []
+            self.messages.append({"role": "system", "content": summarize})
+        return message
+
+    def summarize(self, text, max_tokens=200):
+        response = openai.Completion.create(
+            model = "text-davinci-003",
+            prompt = text + "\n\n请总结一下上面User和Assistant聊了些什么，限制100字以内：\n",
+            max_tokens = max_tokens,
+        )
+        return response["choices"][0]["text"]
 
-    # 创建用于显示在 Gradio UI 的聊天历史
-    chat_log = [(u,b) for u,b in zip(history[::2], history[1::2])]
+    def _build_message(self, messages):
+        text = ""
+        for message in messages:
+            if message["role"] == "user":
+                text += "User : " + message["content"] + "\n\n"
+            if message["role"] == "assistant":
+                text += "Assistant : " + message["content"] + "\n\n" 
+        return text
 
-    return chat_log, history
 
-# 创建 Gradio 接口
-iface = gr.Interface(
-    fn=chatbot_response, 
-    inputs=gr.inputs.Textbox(lines=2, placeholder='Type a message...'), 
-    outputs=[gr.outputs.Textbox(label="Chat History"), gr.outputs.Textbox(label="Chat Log")]
-)
+prompt = """你是一个大数据和AI领域的专家，用中文回答大数据和AI的相关问题。你的回答需要满足以下要求:
+1. 你的回答必须是中文
+2. 回答限制在200个字以内
+3. 拒绝回答违反社会道德和法律的问题"""
+
+conv = Conversation(prompt, 3)
+
+
+def answer(question, history=[]):
+    history.append(question)
+    message = conv.ask(question)
+    history.append(message)
+    responses = [(u,b) for u,b in zip(history[::2], history[1::2])]
+    print(responses)
+    return responses, history
+
+with gr.Blocks(css="#chatbot{height:300px} .overflow-y-auto{height:500px}") as rxbot:
+    chatbot = gr.Chatbot(elem_id="chatbot")
+    state = gr.State([])
+
+    with gr.Row():
+        txt = gr.Textbox(show_label=False, placeholder="请输入你的问题").style(container=False)
+
+    txt.submit(answer, [txt, state], [chatbot, state])
+    
 
-# 启动 Gradio 应用
-iface.launch()
+rxbot.launch()