Create app.py

app.py

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+import gradio as gr
+import torch
+import torch.nn.functional as F
+from PIL import Image
+import json
+from torchvision import transforms
+from model import resnet50
+
+# Load class labels from local file
+with open("imagenet_classes.json", "r") as f:
+    class_labels = json.load(f)
+
+# Load model
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = resnet50(num_classes=1000, drop_path_rate=0.0, use_blurpool=True)
+model.load_state_dict(torch.load("best_resnet50_imagenet_1k.pt", map_location=device))
+model.to(device)
+model.eval()
+
+# Image preprocessing
+transform = 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])
+])
+
+def predict(image):
+    # Preprocess
+    img_tensor = transform(image).unsqueeze(0).to(device)
+    
+    # Predict
+    with torch.no_grad():
+        outputs = model(img_tensor)
+        probabilities = F.softmax(outputs, dim=1)[0]
+    
+    # Get top 5 predictions
+    top5_prob, top5_idx = torch.topk(probabilities, 5)
+    
+    # Format results
+    results = {class_labels[idx]: float(prob) for idx, prob in zip(top5_idx, top5_prob)}
+    return results
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=5),
+    title="ImageNet ResNet50 Classifier (71% Accuracy)",
+    description="ResNet50 trained on ImageNet with improved stem, BlurPool, and progressive resizing. Achieved 71% top-1 accuracy under $30 budget.",
+    examples=[]
+)
+
+if __name__ == "__main__":
+    demo.launch()