Update app.py

app.py

@@ -1,65 +1,92 @@
-import requests
-import pickle 
-# 用于存储API返回的上下文
-class gpt:
-    def send_request(self, messages):
-        # 设置代理服务器的地址和端口
-        proxies = {
-            "http": "http://127.0.0.1:7890",
-            "https": "http://127.0.0.1:7890"
-        }
-        # ChatGPT API的URL
-        url = "https://api.openai.com/v1/chat/completions"
-        # ChatGPT API的访问密钥
-        api_key = "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"
-        # 请求参数
-        parameters = {
-            "model": "gpt-3.5-turbo-0301",  # gpt-3.5-turbo-0301
-            "messages": messages  # [{"role": "user", "content": context}]
-        }
-        # 请求头
-        headers = {
-            "Content-Type": "application/json",
-            "Authorization": f"Bearer {api_key}"
-        }
-        # 发送请求
-        response = requests.post(url, headers=headers, json=parameters)
+from typing import List, Tuple, Dict, Generator
+from langchain.llms import OpenAI
+import gradio as gr
+import os
+os.environ["OPENAI_API_KEY"] = "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"
+model_name = "gpt-3.5-turbo"
+LLM = OpenAI(model_name=model_name, temperature=0.1)
+qa_data_file_path='qa.txt'
+import json
+def save_qa_data(qa_data, file_path):
+    with open(file_path, 'w', encoding='utf-8') as f:
+         json.dump(qa_data, f, ensure_ascii=False, indent=4)
+def create_history_messages(history: List[Tuple[str, str]]) -> List[dict]:
+    history_messages = [{"role": "user", "content": m[0]} for m in history]
+    history_messages.extend([{"role": "assistant", "content": m[1]} for m in history])
+    return history_messages
 
-        # 解析响应
-        if response.status_code == 200:
-            data = response.json()
-            text = data["choices"][0]["message"]
+def create_formatted_history(history_messages: List[dict]) -> List[Tuple[str, str]]:
+    formatted_history = []
+    user_messages = []
+    assistant_messages = []
 
-            return text
-        else:
-            print(response)
-            return "Sorry, something went wrong."
+    for message in history_messages:
+        if message["role"] == "user":
+            user_messages.append(message["content"])
+        elif message["role"] == "assistant":
+            assistant_messages.append(message["content"])
 
-    def start_conversation(self, messages):
-        print("Welcome to ChatGPT! How can I help you today?")
+        if user_messages and assistant_messages:
+            formatted_history.append(
+                ("".join(user_messages), "".join(assistant_messages))
+            )
+            user_messages = []
+            assistant_messages = []
 
-        # 进入对话循环
-        while True:
-            # 获取用户输入
-            user_input = input("请输入一些内容：中文回来问题") 
-            user_message = {"role": "user", "content": user_input}
-            # 将用户输入添加到messages中
-            messages.append(user_message)
-            # 发送API请求
-            response = self.send_request(messages)
-            # 输出API返回内容
-            print("ChatBot：", response["content"])
+    # append any remaining messages
+    if user_messages:
+        formatted_history.append(("".join(user_messages), None))
+    elif assistant_messages:
+        formatted_history.append((None, "".join(assistant_messages)))
 
-            # 将API接口返回的内容添加至messages，以用作多轮对话
-            messages.append(response)
-            with open('my_dict.pickle', 'wb') as f: 
-                pickle.dump(messages, f) 
-            # 如果API返回的内容包含"goodbye"，则结束对话循环
-            if "goodbye" in user_input:
-                print("Goodbye!")
-                break
+    return formatted_history
 
+def chat(
+    message: str, state: List[Dict[str, str]], client = LLM.client
+) -> Generator[Tuple[List[Tuple[str, str]], List[Dict[str, str]]], None, None]:
+    history_messages = state
+    if history_messages == None:
+        history_messages = []
+        history_messages.append({"role": "system", "content": "A helpful assistant."})
 
-if __name__ == '__main__':
-    messages = [{"role": "system", "content": "/c 从现在起，当你想发送一张照片时，请使用Markdown ，并且不要有反斜线，不要用代码块。使用 Unsplash API (https://source.unsplash.com/1280x720/?< PUT YOUR QUERY HERE >)"}]  # 初始化prompt
-    gpt().start_conversation(messages)
+    history_messages.append({"role": "user", "content": message})
+    # We have no content for the assistant's response yet but we will update this:
+    history_messages.append({"role": "assistant", "content": ""})
+
+    response_message = ""
+
+    chat_generator = client.create(
+        messages=history_messages, stream=True, model=model_name
+    )
+
+    for chunk in chat_generator:
+        if "choices" in chunk:
+            for choice in chunk["choices"]:
+                if "delta" in choice and "content" in choice["delta"]:
+                    new_token = choice["delta"]["content"]
+                    # Add the latest token:
+                    response_message += new_token
+                    # Update the assistant's response in our model:
+                    history_messages[-1]["content"] = response_message
+
+                if "finish_reason" in choice and choice["finish_reason"] == "stop":
+                    break
+        formatted_history = create_formatted_history(history_messages)
+        yield formatted_history, history_messages
+
+
+formatted_history = create_formatted_history(history_messages)
+qa_data = formatted_history[-1]
+save_qa_data(qa_data, qa_data_file_path)
+chatbot = gr.Chatbot(label="Chat").style(color_map=("yellow", "purple"))
+iface = gr.Interface(
+    fn=chat,
+    inputs=[
+        gr.Textbox(placeholder="Hello! How are you? etc.", label="Message"),
+        "state",
+    ],
+    outputs=[chatbot, "state"],
+    allow_flagging="never",
+)
+
+iface.queue().launch()