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app.py

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+import gradio as gr
+import torch
+import torchvision.transforms as transforms
+import timm
+import torch.nn as nn
+from huggingface_hub import hf_hub_download
+
+# ── class names ───────────────────────────────────────
+class_names = [
+    'apple_pie', 'baby_back_ribs', 'baklava', 'beef_carpaccio', 'beef_tartare',
+    'beet_salad', 'beignets', 'bibimbap', 'bread_pudding', 'breakfast_burrito',
+    'bruschetta', 'caesar_salad', 'cannoli', 'caprese_salad', 'carrot_cake',
+    'ceviche', 'cheesecake', 'cheese_plate', 'chicken_curry', 'chicken_quesadilla',
+    'chicken_wings', 'chocolate_cake', 'chocolate_mousse', 'churros', 'clam_chowder',
+    'club_sandwich', 'crab_cakes', 'creme_brulee', 'croque_madame', 'cup_cakes',
+    'deviled_eggs', 'donuts', 'dumplings', 'edamame', 'eggs_benedict',
+    'escargots', 'falafel', 'filet_mignon', 'fish_and_chips', 'foie_gras',
+    'french_fries', 'french_onion_soup', 'french_toast', 'fried_calamari', 'fried_rice',
+    'frozen_yogurt', 'garlic_bread', 'gnocchi', 'greek_salad', 'grilled_cheese_sandwich',
+    'grilled_salmon', 'guacamole', 'gyoza', 'hamburger', 'hot_and_sour_soup',
+    'hot_dog', 'huevos_rancheros', 'hummus', 'ice_cream', 'lasagna',
+    'lobster_bisque', 'lobster_roll_sandwich', 'macaroni_and_cheese', 'macarons', 'miso_soup',
+    'mussels', 'nachos', 'omelette', 'onion_rings', 'oysters',
+    'pad_thai', 'paella', 'pancakes', 'panna_cotta', 'peking_duck',
+    'pho', 'pizza', 'pork_chop', 'poutine', 'prime_rib',
+    'pulled_pork_sandwich', 'ramen', 'ravioli', 'red_velvet_cake', 'risotto',
+    'samosa', 'sashimi', 'scallops', 'seaweed_salad', 'shrimp_and_grits',
+    'spaghetti_bolognese', 'spaghetti_carbonara', 'spring_rolls', 'steak', 'strawberry_shortcake',
+    'sushi', 'tacos', 'takoyaki', 'tiramisu', 'tuna_tartare', 'waffles'
+]
+
+# ── model architecture ────────────────────────────────
+class Food101ModelV2(nn.Module):
+    def __init__(self, output_shape: int):
+        super().__init__()
+        self.base = timm.create_model("efficientnet_b2", pretrained=False)
+        in_features = self.base.classifier.in_features
+        self.base.classifier = nn.Sequential(
+            nn.Linear(in_features, 512),
+            nn.BatchNorm1d(512),
+            nn.ReLU(),
+            nn.Dropout(0.4),
+            nn.Linear(512, output_shape)
+        )
+
+    def forward(self, x):
+        return self.base(x)
+
+# ── load model ────────────────────────────────────────
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+model_path = hf_hub_download(
+    repo_id="Zalaid/food-classifier",
+    filename="checkpoint_food101_stage3.pth"
+)
+
+model = Food101ModelV2(output_shape=101).to(device)
+checkpoint = torch.load(model_path, map_location=device)
+model.load_state_dict(checkpoint["model_state_dict"])
+model.eval()
+print(f"Model loaded! Best acc: {checkpoint['best_test_acc']:.4f}")
+
+# ── transform ─────────────────────────────────────────
+transform = transforms.Compose([
+    transforms.Resize(280),
+    transforms.CenterCrop(260),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                         std=[0.229, 0.224, 0.225]),
+])
+
+# ── predict function ──────────────────────────────────
+def predict(image):
+    img_tensor = transform(image).unsqueeze(0).to(device)
+    with torch.inference_mode():
+        output = model(img_tensor)
+        probs  = torch.softmax(output, dim=1)[0]
+    top5_probs, top5_idxs = torch.topk(probs, 5)
+    return {class_names[i]: p.item() for i, p in zip(top5_idxs, top5_probs)}
+
+# ── gradio app ────────────────────────────────────────
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Upload a food image"),
+    outputs=gr.Label(num_top_classes=5, label="Top 5 Predictions"),
+    title="🍕 Food-101 Classifier",
+    description="Upload any food image — model will predict what food it is! Trained on 101 categories using EfficientNet-B2.",
+    theme=gr.themes.Soft(),
+)
+
+demo.launch()

checkpoint_food101_stage3.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ded24e1401d637f3f6bf4f455fefa04ffc580a3d725cffaa2fa6399d9e3daa86
+size 90920923

requirements.txt

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+gradio
+torch
+torchvision
+timm
+huggingface_hub