Upload 10 files

app.py

@@ -0,0 +1,19 @@
+
+import gradio as gr
+from predict import predict_gradio
+
+title = "Mohammad Ali Food101 Classification🍔"
+description = "This demo is a related to classification of 101 different foods"
+
+
+demo = gr.Interface(
+    predict_gradio,
+    inputs = gr.Image(type="pil"),
+    outputs = [gr.Label(num_top_classes = 5 , label = "All Predictions in 5 most value predicted classes"),
+               gr.Label(num_top_classes = 1 , label = "Model Predicts Image as a")],
+    examples=['examples/3243342.jpg', 'examples/1652678.jpg', 'examples/1046933.jpg', 'examples/1840999.jpg', 'examples/168855.jpg'],
+    title = title,
+    description = description
+)
+
+demo.launch()

efficient_model_101.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:968ae5f37d30877631a9ba567f8f428febf42e7df8c39da6702c7e63075e7eb1
+size 31906417

model.py

@@ -0,0 +1,9 @@
+import torch
+import torchvision
+from torchvision.models import EfficientNet_B2_Weights , efficientnet_b2
+
+efficient_weight = EfficientNet_B2_Weights.DEFAULT
+
+efficient_transformer = efficient_weight.transforms()
+
+efficient_model = torch.load("efficient_model_101.pth").to("cpu")

predict.py

@@ -0,0 +1,122 @@
+import torch
+import torchvision
+from PIL import Image
+
+
+FOOD101_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']
+
+def predict_gradio(image:PIL.Image):
+
+  image = efficient_transformer(image)
+
+  efficient_model.eval()
+
+  with torch.no_grad():
+    pred = efficient_model(torch.unsqueeze(image , dim = 0))
+
+  prediction_per_labels = {FOOD101_CLASS_NAMES[i]: float(torch.sigmoid(pred[0][i])) for i in range(len(FOOD101_CLASS_NAMES))}
+
+  prediction = FOOD101_CLASS_NAMES[torch.argmax(pred).item()]
+
+  return prediction_per_labels , prediction

requirements.txt

@@ -0,0 +1,4 @@
+torch==2.0.1
+torchvision==0.15.2
+gradio==3.37.0
+PIL==8.4.0