app.py

import openai
import requests
import json
import gradio as gr

# 定义headers
headers = {}

def init_apis(openai_api_key, huggingface_api_key):
    # 这个函数用于初始化OpenAI和Hugging Face API
    openai.api_key = openai_api_key
    headers["Authorization"] = f"Bearer {huggingface_api_key}"
    return "APIs initialized successfully."

# 创建用于初始化API的Gradio接口
init_interface = gr.Interface(
    fn=init_apis,
    inputs=[
        gr.inputs.Textbox(label="OpenAI Key", type="password"),
        gr.inputs.Textbox(label="HuggingFace Key", type="password"),
    ],
    outputs="text",
    title="Initialize APIs",
    description="Enter your OpenAI and Hugging Face API keys.",
)

def query(url):
    # 这个函数会向Hugging Face API发送图像URL，并获取图像中检测到的对象
    API_URL = "https://api-inference.huggingface.co/models/facebook/detr-resnet-50"
    response = requests.get(url)
    response.raise_for_status()
    data = response.content
    api_response = requests.request("POST", API_URL, headers=headers, data=data)
    return json.loads(api_response.content.decode("utf-8"))

def process_query(user_query):
    # 这个函数处理图像对象检测任务
    function_descriptions = [
      {
          "name": "目标检测模型",
          "description": "Send an image URL to the Hugging Face API and get the detected objects in the image",
          "parameters": {
              "type": "object",
              "properties": {
                  "url": {
                      "type": "string",
                      "description": "The URL of the image to analyze",
                  }
              },
              "required": ["url"],
          },
        }
    ]

    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo-0613",
        messages=[{"role": "user", "content": user_query}],
        functions=function_descriptions,
        function_call="auto",
    )

    ai_response_message = response["choices"][0]["message"]
    url = eval(ai_response_message['function_call']['arguments']).get("url")
    function_response = query(url=url)

    second_response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo-0613",
        messages=[
            {"role": "user", "content": user_query},
            ai_response_message,
            {
                "role": "function",
                "name": "query",
                "content": json.dumps(function_response),
            },
        ],
    )

    return second_response['choices'][0]['message']['content']

# 创建用于处理查询的Gradio接口
query_interface = gr.Interface(
    fn=process_query,
    inputs=[
        gr.inputs.Textbox(label="Question")
    ],
    outputs="text",
    title="Process Query",
    description="Enter your question. The model will return the detected objects in the image.",
)

init_interface.launch()
query_interface.launch()