app file

app.py

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+import gradio as gr
+import torch
+import torch.nn.functional as F
+from torchvision import transforms, models
+from PIL import Image
+import requests
+
+# Load ImageNet labels
+LABELS_URL = "https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt"
+labels = requests.get(LABELS_URL).text.strip().split("\n")
+
+# Load model (change this to your model path)
+model = models.resnet50(weights=None)
+
+# If using your converted FP16 model:
+# state = torch.load("model_cpu.pt", map_location="cpu")
+# def to_fp32(obj):
+#     if isinstance(obj, torch.Tensor) and obj.dtype == torch.float16:
+#         return obj.float()
+#     if isinstance(obj, dict):
+#         return {k: to_fp32(v) for k, v in obj.items()}
+#     return obj
+# model.load_state_dict(to_fp32(state))
+
+# For demo, using pretrained weights
+model = models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V1)
+model.eval()
+
+# 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):
+    if image is None:
+        return {}
+    
+    img = transform(image).unsqueeze(0)
+    
+    with torch.no_grad():
+        outputs = model(img)
+        probs = F.softmax(outputs, dim=1)[0]
+    
+    top5_probs, top5_indices = torch.topk(probs, 5)
+    
+    return {labels[idx]: float(prob) for prob, idx in zip(top5_probs, top5_indices)}
+
+# Gradio interface
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Upload Image"),
+    outputs=gr.Label(num_top_classes=5, label="Predictions"),
+    title="🖼️ ImageNet 1K Classifier",
+    description="Upload an image to classify it into one of 1000 ImageNet categories.",
+    examples=[
+        ["https://upload.wikimedia.org/wikipedia/commons/thumb/4/4d/Cat_November_2010-1a.jpg/1200px-Cat_November_2010-1a.jpg"],
+    ],
+    theme=gr.themes.Soft(),
+)
+
+if __name__ == "__main__":
+    demo.launch()