update sth. at 2024-12-23 23:39:16

Dockerfile

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+# 使用官方的 Python 3.11 作为基础镜像
+FROM python:3.11-slim
+
+# 设置工作目录
+WORKDIR /app
+
+# 复制当前目录的所有文件到容器中的 /app 目录
+COPY . /app
+
+# 安装依赖
+RUN pip install --no-cache-dir -r requirements.txt
+
+# 暴露端口 7860
+EXPOSE 7860
+
+# 运行 Flask 应用
+CMD ["python", "app241224.py"]

app241224.py

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+"""
+curl -X GET http://localhost:7680/api/models
+
+curl -X POST http://127.0.0.1:7680/hf/v1/chat/completions \
+-H "Content-Type: application/json" \
+-d '{
+  "prompt": "你是什么模型?"
+}'
+
+
+"""
+import json
+import uuid  # 用于生成随机用户 ID
+
+import requests
+from flask import Flask, request, Response
+
+app = Flask(__name__)
+# 全局字典用于保存用户的上下文对话
+user_contexts = {}
+MAX_HISTORY_LENGTH = 15  # 最大上下文历史长度
+
+
+def get_models():
+    models = {
+        "object": "list",
+        "data": [
+            {"id": "Qwen2.5-72B", "object": "model", "created": 0, "owned_by": "Qwen"},
+            {"id": "Llama-3.1-Nemotron-70B", "object": "model", "created": 0, "owned_by": "Nemotron"},
+            {"id": "NVLM-D-72B", "object": "model", "created": 0, "owned_by": "NVDIA"},
+            {"id": "DeepSeek-Coder-V2", "object": "model", "created": 0, "owned_by": "DeepSeek"},
+            {"id": "Qwen2.5-Coder-32B", "object": "model", "created": 0, "owned_by": "Qwen"},
+        ]
+    }
+    return json.dumps(models)
+
+
+def chat_completion(
+        user_prompt, user_id: str = None, system_prompt="You are a helpful assistant.", model="Qwen2.5-72B",
+        project="DecentralGPT", stream=False, temperature=0.3, max_tokens=1024, top_p=0.5,
+        frequency_penalty=0, presence_penalty=0):
+    """处理用户请求并保留上下文"""
+    url = 'https://usa-chat.degpt.ai/api/v0/chat/completion/proxy'
+    headers = {
+        'accept': 'application/json',
+        'accept-language': 'en-US,en;q=0.9,zh-CN;q=0.8,zh;q=0.7',
+        'content-type': 'application/json',
+        'dnt': '1',
+        'origin': 'https://www.degpt.ai',
+        'priority': 'u=1, i',
+        'referer': 'https://www.degpt.ai/',
+        'sec-ch-ua': 'Google Chrome";v="131", "Chromium";v="131", "Not_A Brand";v="24"',
+        'sec-ch-ua-mobile': '?0',
+        'sec-ch-ua-platform': '"macOS"',
+        'sec-fetch-dest': 'empty',
+        'sec-fetch-mode': 'cors',
+        'sec-fetch-site': 'same-site',
+        'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36'
+    }
+
+    # 初始化或更新用户的对话历史
+    if user_id is not None:
+        if user_id not in user_contexts:
+            user_contexts[user_id] = [{"role": "system", "content": system_prompt}]
+
+        user_contexts[user_id].append({"role": "user", "content": user_prompt})
+
+        # 检查是否需要修剪历史记录，保留 `system` 提示词
+        while len(user_contexts[user_id]) > MAX_HISTORY_LENGTH:
+            # 删除最早的用户问题和系统回复，但保留 `system` 提示词
+            if len(user_contexts[user_id]) > 2:
+                # 检查删除的条目是否有匹配的系统回复，如果没有，只删除用户输入
+                if user_contexts[user_id][2]["role"] == "user":
+                    user_contexts[user_id] = [user_contexts[user_id][0]] + user_contexts[user_id][2:]
+                else:
+                    user_contexts[user_id] = [user_contexts[user_id][0]] + user_contexts[user_id][2:]
+            else:
+                break
+
+        messages = user_contexts[user_id]
+    else:
+        # 如果没有提供 user_id，不保留上下文
+        messages = [{"role": "system", "content": system_prompt}, {"role": "user", "content": user_prompt}]
+
+    payload = {
+        "model": model,
+        "messages": messages,
+        "project": project,
+        "stream": stream,
+        "temperature": temperature,
+        "max_tokens": max_tokens,
+        "top_p": top_p,
+        "frequency_penalty": frequency_penalty,
+        "presence_penalty": presence_penalty
+    }
+
+    try:
+        response = requests.post(url, headers=headers, json=payload)
+        response.encoding = 'utf-8'
+        response.raise_for_status()
+        ## print(response.text)
+
+        # 获取响应并添加到上下文
+        response_content = response.json()["choices"][0]["message"]["content"]
+        # print(
+        #     f"=========== {user_id}:{user_prompt} ====================\r\n请求内容:{messages}\r\n完整响应:{response.text}")
+
+        # 将系统的回复添加到用户上下文中
+        if user_id is not None:
+            user_contexts[user_id].append({"role": "assistant", "content": response_content})
+        return response.text
+    except requests.exceptions.RequestException as e:
+        print(f"请求失败: {e}")
+        return "请求失败，请检查网络或参数配置。"
+    except (KeyError, IndexError) as e:
+        print(f"解析响应时出错: {e}")
+        return "解析响应内容失败。"
+    return {}
+
+
+@app.route('/api/models', methods=['GET'])
+@app.route('/api/v1/models', methods=['GET'])
+@app.route('/hf/v1/models', methods=['GET'])
+def models():
+    """返回可用模型列表"""
+    return get_models()
+
+
+@app.route('/api/chat/completion', methods=['POST'])
+@app.route('/api/v1/chat/completions', methods=['POST'])
+@app.route('/hf/v1/chat/completions', methods=['POST'])
+def chat_completion_api():
+    """处理用户请求并保留上下文"""
+    data = request.json
+    user_prompt = data.get("prompt")
+    user_id = data.get("user_id", str(uuid.uuid4()))  # 如果未提供 user_id，生成随机值
+
+    response_content = chat_completion(
+        user_prompt,
+        user_id=user_id
+    )
+
+    # maybe \uxxxx
+    # return jsonify(response_content)
+    ## maybe \"xxx\"
+    # return Response(
+    #     json.dumps(response_content, ensure_ascii=False),
+    #     content_type="application/json; charset=utf-8"
+    # )
+    # support Chinese
+    if isinstance(response_content, str):  # 如果已经是 JSON 字符串
+        return Response(response_content, content_type="application/json; charset=utf-8")
+
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=7860)

requirements.txt

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+Flask==2.2.3
+requests==2.31.0