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import tensorflow as tf |
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import numpy as np |
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import torch |
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import torch.nn as nn |
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import timm |
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from torchvision import transforms |
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import os |
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import requests |
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import json |
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classes = ['apple pie', 'baby back ribs', 'baklava', 'beef carpaccio', 'beef tartare', |
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'beet salad', 'beignets', 'bibimbap', 'bread pudding', 'breakfast burrito', |
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'bruschetta', 'caesar_salad', 'cannoli', 'caprese salad', 'carrot cake', |
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'ceviche', 'cheese plate', 'cheesecake', 'chicken curry', |
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'chicken quesadilla', 'chicken wings', 'chocolate cake', 'chocolate mousse', |
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'churros', 'clam chowder', 'club sandwich', 'crab cakes', 'creme brulee', |
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'croque madame', 'cup cakes', 'deviled eggs', 'donuts', 'dumplings', 'edamame', |
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'eggs benedict', 'escargots', 'falafel', 'filet mignon', 'fish and chips', |
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'foie gras', 'french fries', 'french onion soup', 'french toast', |
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'fried calamari', 'fried rice', 'frozen yogurt', 'garlic bread', 'gnocchi', |
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'greek salad', 'grilled cheese sandwich', 'grilled salmon', 'guacamole', |
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'gyoza', 'hamburger', 'hot and sour soup', 'hot dog', 'huevos rancheros', |
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'hummus', 'ice cream', 'lasagna', 'lobster bisque', 'lobster roll sandwich', |
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'macaroni and cheese', 'macarons', 'miso soup', 'mussels', 'nachos', |
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'omelette', 'onion rings', 'oysters', 'pad thai', 'paella', 'pancakes', |
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'panna cotta', 'peking duck', 'pho', 'pizza', 'pork chop', 'poutine', |
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'prime rib', 'pulled pork sandwich', 'ramen', 'ravioli', 'red velvet cake', |
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'risotto', 'samosa', 'sashimi', 'scallops', 'seaweed salad', |
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'shrimp and grits', 'spaghetti bolognese', 'spaghetti carbonara', |
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'spring rolls', 'steak', 'strawberry_shortcake', 'sushi', 'tacos', 'takoyaki', |
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'tiramisu', 'tuna tartare', 'waffles'] |
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def load_prepare_image_tf(filepath, img_size, rescale=False): |
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img = tf.io.decode_image(filepath, channels=3) |
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img = tf.image.resize(img, img_size) |
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if rescale: |
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return img/255. |
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else: |
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return img |
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def model_pred_tf(model_path, img, class_names=classes): |
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interpreter = tf.lite.Interpreter(model_path=model_path) |
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interpreter.allocate_tensors() |
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input_tensor= np.array(np.expand_dims(img,0), dtype=np.float32) |
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input_index = interpreter.get_input_details()[0]["index"] |
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interpreter.set_tensor(input_index, input_tensor) |
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interpreter.invoke() |
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output_details = interpreter.get_output_details() |
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output_data = interpreter.get_tensor(output_details[0]['index']) |
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pred = output_data.argmax() |
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food_name = class_names[pred] |
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return food_name |
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def get_model_pt(model_path): |
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model = timm.create_model('vit_base_patch16_224', pretrained=False) |
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model.head = nn.Linear(in_features=768, out_features=len(classes), bias=True) |
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model.load_state_dict(torch.load(model_path, map_location='cpu')) |
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return model |
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def load_prepare_image_pt(input_image): |
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normalize = transforms.Normalize( |
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[0.485, 0.456, 0.406], |
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[0.229, 0.224, 0.225] |
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) |
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img_transform = transforms.Compose([ |
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transforms.Resize((225, 225)), |
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transforms.CenterCrop(224), |
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transforms.ToTensor(), |
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normalize, |
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]) |
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input_image = img_transform(input_image).unsqueeze(0) |
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return input_image |
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def model_pred_pt(input_image, model_path): |
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model = get_model_pt(model_path) |
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probs = model(input_image) |
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y_preds = torch.softmax(probs, dim=1).detach().numpy().argmax() |
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pred = classes[y_preds] |
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return pred |
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def fetch_recipe(food_name): |
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url = "https://recipesapi2.p.rapidapi.com/recipes/"+food_name |
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querystring = {"maxRecipes":"1"} |
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headers = { |
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'x-rapidapi-host': "recipesapi2.p.rapidapi.com", |
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'x-rapidapi-key': "f6f6823b91msh9e92fed91d5356ap136f5djsn494d8f582fb3" |
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} |
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response = requests.request("GET", url, headers=headers, params=querystring) |
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json_data = json.loads(response.text) |
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recipe_data = json_data['data'][0] |
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return recipe_data |
