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import tensorflow as tf
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import numpy as np
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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(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(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 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 |
