commit FoodVision_Big app file

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+10_pretrained_effnetb2_features_extractor_pizza_steak_sushi_20_percent.pth filter=lfs diff=lfs merge=lfs -text
+11_pretrained_effnet_feature_extractor_food101.pth filter=lfs diff=lfs merge=lfs -text

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.idea/foodvision_mini.iml

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11_pretrained_effnet_feature_extractor_food101.pth

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+oid sha256:d12a8ccfa02ab463a8a3dcb7106c3fc9d5376b8a34932778841a1b5f9ef70e81
+size 31849786

README.md

@@ -1,8 +1,8 @@
 ---
-title: FoodVision Big
-emoji: 🐢
-colorFrom: green
-colorTo: purple
+title: FoodVision Mini
+emoji: 🌖
+colorFrom: yellow
+colorTo: pink
 sdk: gradio
 sdk_version: 4.10.0
 app_file: app.py

app.py

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+
+import gradio as gr
+import os
+import torch
+
+from timeit import default_timer as timer
+from model import create_effnetb2_model
+from typing import Tuple , Dict
+
+# 1.Import and class names setup
+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',
+ 'cheese_plate',
+ 'cheesecake',
+ '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']
+
+from torchvision.models._api import WeightsEnum
+from torch.hub import load_state_dict_from_url
+def get_state_dict(self, *args, **kwargs):
+    kwargs.pop("check_hash")
+    return load_state_dict_from_url(self.url, *args, **kwargs)
+WeightsEnum.get_state_dict = get_state_dict
+
+# 2. Model annd transforms prepration
+effnetb2_model  , effnet_b2_transforms = create_effnetb2_model(num_classes = 101, seed = 42)
+
+# Load save weights
+effnetb2_model.load_state_dict(
+  torch.load(
+    f='11_pretrained_effnet_feature_extractor_food101.pth',
+    map_location = torch.device('cpu') # Load the model on CPU
+  )
+)
+# 3.prediction function (predict())
+def predict(img) -> Tuple[Dict,float] :
+
+  start_time = timer()
+
+  image =  effnet_b2_transforms(img).unsqueeze(0)
+
+
+  effnetb2_model.eval()
+  with torch.inference_mode():
+
+    pred_probs = torch.softmax(effnetb2_model(image) , dim=1)
+  pred_label_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range (len(class_names))}
+
+  end_time = timer()
+  pred_time = round(end_time - start_time , 4)
+
+  return pred_label_and_probs , pred_time
+
+### 4. Gradio app - our Gradio interface + launch command
+
+title = 'FoodVision Big'
+description = 'An EfficientNetB2 Feature extractor computer vision model to classifiy 101 Food images '
+article = 'created at PyTorch Model Deployment'
+
+# Create example list
+
+example_list = [['examples/'+ example] for example in os.listdir('examples')]
+example_list
+
+# create a gradio demo
+demo = gr.Interface(fn=predict ,
+                    inputs=gr.Image(type='pil'),
+                    outputs=[gr.Label(num_top_classes = 3 , label= 'prediction'),
+                            gr.Number(label= 'Prediction time (s)')],
+                    examples = example_list,
+                    title = title,
+                    description = description,
+                    article= article)
+
+# Launch the demo
+demo.launch(debug= False)

model.py

@@ -0,0 +1,30 @@
+
+import torch
+import torchvision
+from torch import nn
+
+from torchvision.models._api import WeightsEnum
+from torch.hub import load_state_dict_from_url
+def get_state_dict(self, *args, **kwargs):
+    kwargs.pop("check_hash")
+    return load_state_dict_from_url(self.url, *args, **kwargs)
+WeightsEnum.get_state_dict = get_state_dict
+
+def create_effnetb2_model(num_classes : int = 3,
+                          seed : int = 42):
+
+  weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
+  transform = weights.transforms()
+  model = torchvision.models.efficientnet_b2(weights= weights)
+
+
+  for param in model.parameters():
+    param.requires_grad = False
+
+  torch.manual_seed(seed)
+  model.classifier = torch.nn.Sequential(
+    torch.nn.Dropout(p=0.3, inplace= True),
+    torch.nn.Linear(in_features = 1408,
+                    out_features = num_classes)
+  )
+  return model , transform

requirements.txt

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+torch==2.1.0
+torchvision==0.16.0
+gradio==4.10.0