app.py

import gradio as gr
import cv2
import gradio as gr
import torch
from torchvision import transforms
import requests
from PIL import Image


model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()
#标题
title = "抽取式问答"
#标题下的描述，支持md格式
description = "输入上下文与问题后，点击submit按钮，可从上下文中抽取出答案，赶快试试吧！"

# Download human-readable labels for ImageNet.
# response = requests.get("http://git.io/JJkYN")
# labels = response.text.split("\n")
# 打开文件
file = open('label.txt', 'r')
# 读取文件内容
labels = file.readlines()
def to_black(inp,long,lat,Area):
    inp = Image.fromarray(inp.astype('uint8'), 'RGB')
    inp = transforms.ToTensor()(inp).unsqueeze(0)
    with torch.no_grad():
        prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
    return {labels[i]: float(prediction[i]) for i in range(1000)}

outputs = gr.outputs.Label(num_top_classes=3)
interface = gr.Interface(fn=to_black,
                         inputs=["image",
                                 gr.Number(label="longitude"),
                                 gr.Number(label="latitude"),
                                 gr.Slider(256, 512,label='Area')],
                         outputs=outputs,
                         title=title,
                         description=description,
                        examples=[["gradio/未命名1688700109.png",70.1,40.0,256]])
interface.launch()