Added examples

app.py

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+import gradio as gr
+from torchvision.models import resnet50, ResNet50_Weights
+from torchvision import transforms
+import torch.nn as nn
+import torch
+
+@staticmethod
+def create_model_from_checkpoint():
+    # Loads a model from a checkpoint
+    model = resnet50()
+    model.fc = nn.Linear(model.fc.in_features, 3)
+    model.load_state_dict(torch.load("best_model"))
+    model.eval()
+    return model
+
+def prep_image(img):
+    transform = transforms.Compose([
+        transforms.Resize(256),
+        transforms.CenterCrop(224),
+        transforms.ToTensor()
+    ])
+
+    transform_normalize = transforms.Normalize(
+        mean=[0.485, 0.456, 0.406],
+        std=[0.229, 0.224, 0.225]
+    )
+
+    transformed_img = transform(img)
+
+    input = transform_normalize(transformed_img)
+    input = input.unsqueeze(0)
+    return input
+
+model = create_model_from_checkpoint()
+labels = [ "benign", "malignant", "normal" ]
+
+def predict(img):
+    input = prep_image(img)
+    with torch.no_grad():
+        prediction = torch.nn.functional.softmax(model(input)[0], dim=0)
+        confidences = {labels[i]: float(prediction[i]) for i in range(3)}    
+    return confidences
+
+ui = gr.Interface(fn=predict, 
+                inputs=gr.Image(type="pil"),
+                outputs=gr.Label(num_top_classes=3),
+                examples=["benign (52).png", "benign (243).png", "malignant (127).png", "malignant (201).png", "normal (81).png", "normal (101).png"]).launch()
+ui.launch(share=True)