Added files

app.py

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+import gradio as gr
+import torch
+from torchvision import transforms, models
+from PIL import Image
+from torch import nn
+
+model_name = "b0"
+if model_name == "b4":
+    IMAGE_RESIZE_SHAPE = 384
+    IMAGE_FINAL_SHAPE = 380
+    BATCH_SIZE = 32
+    FEATURE_SHAPE = 1792
+
+if model_name == "b0":
+    IMAGE_RESIZE_SHAPE = 256
+    IMAGE_FINAL_SHAPE = 224
+    BATCH_SIZE = 32
+    FEATURE_SHAPE = 1280
+
+
+def load_labels(label_text_path):
+    with open(label_text_path, "r") as f:
+        lables = [line.strip() for line in f.readlines()]
+    label_dict = {i: lables[i] for i in range(len(lables))}
+    return label_dict
+
+
+label_dict = load_labels("labels.txt")
+
+# Load PyTorch model
+model_params = torch.load("food101.pt", map_location=torch.device("cpu"))
+if model_name == "b4":
+    model = models.efficientnet_b4()
+if model_name == "b0":
+    model = models.efficientnet_b0()
+
+model.eval()
+for params in model.parameters():
+    params.requires_grad = False
+model.classifier[1] = nn.Linear(in_features=FEATURE_SHAPE, out_features=101)
+model.load_state_dict(model_params)
+
+# Define image transformation
+normalize = transforms.Normalize(
+    mean=[0.485, 0.456, 0.406],
+    std=[0.229, 0.224, 0.225],
+)
+
+transform = transforms.Compose(
+    [
+        transforms.Resize(IMAGE_RESIZE_SHAPE),
+        transforms.CenterCrop(IMAGE_FINAL_SHAPE),
+        transforms.ToTensor(),
+        normalize,
+    ]
+)
+
+
+# Define prediction function
+def predict_image_class(image):
+    # Load image
+    image = Image.fromarray(image.astype("uint8"), "RGB")
+
+    # Apply transformation
+    transformed_image = transform(image)
+
+    # Add batch dimension
+    transformed_image = transformed_image.unsqueeze(0)
+
+    # Disable gradient calculation
+    with torch.no_grad():
+        # Make prediction
+        output = model(transformed_image)
+    _, indices = torch.sort(output, descending=True)
+
+    percentage = torch.nn.functional.softmax(output, dim=1)[0]
+    # create a dictionary of top 10 classes
+    top_10 = {}
+    for idx in indices[0][:10]:
+        top_10[label_dict[idx.item()]] = percentage[idx].item()
+    return top_10
+
+
+# Define Gradio interface
+description = "This is a demo of EfficientNet trained on Food101 dataset.\
+    Upload an image of food and it will predict the class of the food."
+inputs = gr.inputs.Image()
+outputs = gr.outputs.Label(num_top_classes=10)
+gradio_app = gr.Interface(
+    fn=predict_image_class,
+    inputs=inputs,
+    outputs=outputs,
+    title="FoodVision",
+    description=description,
+    theme="snehilsanyal/scikit-learn",
+    examples=[
+        ["examples/pizza.jpg"],
+        ["examples/samosa.jpg"],
+    ],
+)
+
+# Run Gradio app
+gradio_app.launch(
+    server_port=7860,
+)

food101.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f5bda7bcad0d9284628d7d3a22e9d2179b594f8dcab2c1d2c1e3456779d571b6
+size 16844913

labels.txt

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+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

requirements.txt

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+gradio
+pytorch
+torchvision
+pillow