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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=[["cat_dog.png",70.1,40.0,256]])
interface.launch() |