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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() |