Create app.py

app.py

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+from PIL import Image
+import torch
+import gradio as gr
+
+inference = torch.load('fine_tune_resnet.pth')
+inference.eval()
+
+def classifier(image):
+    output = inference(test_transform(Image.open('/content/1000-ml-plastic-water-bottle-500x500.webp')).unsqueeze(0))
+    _, prediction = torch.max(output,1)
+    confidence = round(torch.softmax(output,1).max().item(),4)*100
+
+    return f'{label_dict[prediction.item()]} (Confidence: {confidence}%)'
+
+iface = gr.Interface(fc=classifier,
+                    inputs=gr.Image(type="pil"),
+                    outputs='text')
+iface.launch(share=True)