feat: gpt接口

.gitattributes

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+# Auto detect text files and perform LF normalization
+* text=auto

Dockerfile

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+# Read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
+# you will also find guides on how best to write your Dockerfile
+
+FROM python:3.9
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

LICENSE

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+MIT License
+
+Copyright (c) 2023-2024 牛爷爷信息技术（南京）有限公司
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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----
-title: Joe Llm Api
-emoji: 🌍
-colorFrom: yellow
-colorTo: pink
-sdk: docker
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

demo.py

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+from openai import OpenAI
+from fastapi import FastAPI, HTTPException, Request
+from fastapi.responses import StreamingResponse
+
+
+
+
+client = OpenAI(
+    api_key="sk-u2QYG3NlYTQ6eE9uNoWFhUCSyS71oY5K43rtKexk9f4XZ7Zv",
+    base_url="https://api.chatanywhere.tech/v1"
+)
+app = FastAPI()
+
+
+@app.post("/gpt-35-api")
+async def gpt_35_api_http(request: Request):
+    """
+    处理/gpt-35-api的POST请求，对应原gpt_35_api函数的功能，以非流式调用方式向GPT-3.5-Turbo模型发送对话消息并获取回答。
+
+    Returns:
+        dict: 包含模型生成回答的字典，格式为{"result": 回答内容}
+    """
+    try:
+        # 从请求体中获取JSON数据
+        json_data = await request.json()
+        messages = json_data.get("messages")
+
+        # 使用OpenAI的API客户端向GPT-3.5-Turbo模型发送请求以创建一个完成对象
+        completion = client.chat.completions.create(
+            model="gpt-3.5-turbo",
+            messages=messages,
+            temperature= 0.8,
+            top_p= 0.9,
+            frequency_penalty= 0.3,
+            presence_penalty= 0.2,
+        )
+        # 从完成对象的选择列表中获取第一个选择（通常就是模型生成的回答），并返回其消息内容作为响应
+        return {"result": completion.choices[0].message.content}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"调用GPT-3.5-Turbo时出错: {e}")
+
+@app.post("/gpt-35-api-stream")
+async def gpt_35_api_stream_http(request: Request):
+    """
+    处理/gpt-35-api-stream的POST请求，对应原gpt_35_api_stream函数的功能，以流式传输的方式向GPT-3.5-Turbo模型发送对话消息并实时获取回答。
+
+    Returns:
+    StreamingResponse: 以流式响应的方式返回模型生成的回答内容，可实时展示给用户
+    """
+    try:
+        # 从请求体中获取JSON数据
+        json_data = await request.json()
+        messages = json_data.get("messages")
+
+        # 使用OpenAI的API客户端向GPT-3.5-Turbo模型发送请求以创建一个流式传输对象
+        stream = client.chat.completions.create(
+            model='gpt-3.5-turbo',
+            messages=messages,
+            stream=True,
+            temperature= 0.8,
+            top_p= 0.9,
+            frequency_penalty= 0.3,
+            presence_penalty= 0.2,
+        )
+
+        async def generate():
+            for chunk in chunk_stream:
+                if chunk.choices[0].delta.content is not None:
+                    yield chunk.choices[0].delta.content
+
+        return StreamingResponse(generate(), media_type="text/plain")
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"调用GPT-3.5-Turbo时出错: {e}")
+
+@app.post("/gpt-4o-mini")
+async def call_gpt_4_mini_http(request: Request):
+    """
+    处理/call-gpt-4-mini的POST请求，对应原call_gpt_4_mini函数的功能，使用GPT-4 Mini模型根据给定的对话消息生成回复。
+
+    Returns:
+        dict: 包含模型生成回答的字典，格式为{"result": 回答内容} 或 {"error": 错误信息}
+    """
+    try:
+        # 从请求体中获取JSON数据
+        json_data = await request.json()
+        messages = json_data.get("messages")
+
+        # 定义你的请求参数
+        response = client.chat.completions.create(
+            model="gpt-4o-mini",
+            messages=messages,
+            temperature= 0.8,
+            top_p= 0.9,
+            frequency_penalty= 0.3,
+            presence_penalty= 0.2,
+        )
+        print(response)
+        print(type(response.choices))
+        message_content = response.choices[0].message.content
+        return {"result": message_content.strip()}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"调用GPT-4 Mini时出错: {e}")
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="0.0.0.0", port=7860)

requirements.txt

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+openai
+requests
+flask
+uvicorn
+fastapi