Create app.py

app.py

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+import torch
+import torch.nn as nn
+from torchvision import models,transforms
+from PIL import Image
+import gradio as gr
+from torchvision.transforms import transforms
+
+
+# model=models.resnet18(pretrained=True)
+# model.fc=nn.Linear(model.fc.in_features,10)
+t=transforms.Compose([  transforms.ToTensor(),
+                        transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5)),
+                        transforms.RandomHorizontalFlip(0.5),
+                        transforms.RandomRotation(10),
+                        ])
+class_name=[f"c{i}" for i in range(1,10)]
+
+model=torch.load("model.pth")
+print(model)
+def predict(image):
+    image=t(image).unsqueeze(0)
+    with torch.no_grad():
+        output=model(image)
+        _,predicted=torch.max(output,1)
+        predicted_class=predicted.item()
+        return predicted_class
+
+interface=gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="cifar dataset prediction",
+    description="upload an image to get its class"
+)
+
+interface.launch(share=True)