Upload app.py with huggingface_hub

app.py

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+import torch
+import torchvision.models as models
+import gradio as gr
+from huggingface_hub import hf_hub_download
+from PIL import Image
+from torchvision import transforms
+
+# ✅ Download model checkpoint from Hugging Face Hub
+model_path = hf_hub_download(
+    repo_id="PrachiY/image-classification-model",
+    filename="clothing1m.pth.tar"
+)
+
+# ✅ Load the Model
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = models.resnet50(pretrained=False)
+
+checkpoint = torch.load(model_path, map_location=device)
+
+if "model" in checkpoint:
+    model.load_state_dict(checkpoint["model"], strict=False)
+elif "state_dict" in checkpoint:
+    model.load_state_dict(checkpoint["state_dict"], strict=False)
+else:
+    model.load_state_dict(checkpoint, strict=False)
+
+model.fc = torch.nn.Linear(2048, 21)
+model.to(device)
+model.eval()
+
+# ✅ Define Clothing1M Class Labels
+class_labels = [
+    "T-shirt", "Shirt", "Knitwear", "Chiffon", "Sweater", "Hoodie", "Windbreaker",
+    "Jacket", "Downcoat", "Suits", "Shawl", "Dress", "Vest", "Underwear",
+    "Hat", "Sock", "Jeans", "Sweatpants", "Trousers", "Shorts", "Skirt"
+]
+
+# ✅ Image Preprocessing
+def preprocess_image(image):
+    transform = transforms.Compose([
+        transforms.Resize((224, 224)),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+    ])
+    return transform(image).unsqueeze(0).to(device)
+
+# ✅ Prediction Function
+def predict(image):
+    image_tensor = preprocess_image(image)
+    with torch.no_grad():
+        output = model(image_tensor)
+        predicted_class_idx = output.argmax(dim=1).item()
+
+        if predicted_class_idx >= len(class_labels):
+            return f"Predicted Class: Unknown (Index {predicted_class_idx} out of range)"
+
+        return f"Predicted Class: {class_labels[predicted_class_idx]}"
+
+# ✅ Gradio Interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Clothing1M Image Classifier",
+    description="Upload an image to classify it into one of 21 clothing categories."
+)
+
+if __name__ == "__main__":
+    interface.launch()