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import gradio as gr
import os
import torch
import numpy
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']
print(numpy.__version__)
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_fine_tune.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 FineTune last 4 Sequential layers of EfficientNetB2 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)
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