Create app.py

app.py

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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('gradio/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()