Update app.py

app.py

@@ -15,26 +15,50 @@ transform = transforms.Compose([
     transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
 ])
 
-classes = ['Fruta própria para o consumo', 'Fruta imprópria para o consumo']
+categories = ['Fruta própria para o consumo', 'Fruta imprópria para o consumo']
 
 import gradio as gr
 from PIL import Image
 
-# Define the function to make predictions
-def predict(image):
-    image = transform(image).unsqueeze(0).to(device)
-    model.eval()
+
+def inference(input_image):
+    preprocess = transforms.Compose([
+        transforms.Resize(256),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    ])
+    input_tensor = preprocess(input_image)
+    input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model
+
+    # move the input and model to GPU for speed if available
+    if torch.cuda.is_available():
+        input_batch = input_batch.to('cuda')
+        model.to('cuda')
+
     with torch.no_grad():
-        output = model(image)
-        _, predicted = torch.max(output.data, 1)
-        return classes[predicted.item()]
+        output = model(input_batch)
+    # The output has unnormalized scores. To get probabilities, you can run a softmax on it.
+    probabilities = torch.nn.functional.softmax(output[0], dim=0)
+
+
+    # Show top categories per image
+    top5_prob, top5_catid = torch.topk(probabilities, 2)
+    result = {}
+    for i in range(top5_prob.size(0)):
+        result[categories[top5_catid[i]]] = top5_prob[i].item()
+    return result
+
+inputs = gr.inputs.Image(type='pil')
+outputs = gr.outputs.Label(type="confidences",num_top_classes=5)
+
+title = "ResNet"
+description = "Gradio demo for ResNet, Deep residual networks pre-trained on ImageNet. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
 
-# Define the input and output components
-image_input = gr.inputs.Image(type="pil", label="Upload Image")
-label_output = gr.outputs.Label()
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1512.03385' target='_blank'>Deep Residual Learning for Image Recognition</a> | <a href='https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py' target='_blank'>Github Repo</a></p>"
 
-# Create the interface
-interface = gr.Interface(fn=predict, inputs=image_input, outputs=label_output)
+examples = [
+            ['dog.jpg']
+]
 
-# Launch the interface
-interface.launch()
+gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch()