init

utils.py

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+import tensorflow as tf
+import numpy as np
+import os
+
+import requests
+import json
+# def view_and_predict(target_dir, target_class, model_path):
+
+#     # Reading image and plotting image
+#     img = tf.io.read_file(target_folder + '/' + random_image[0])
+#     img = tf.io.decode_image(img)
+#     img = tf.image.resize(img,(224,224))
+#     img_show = img/255.
+
+#     pred = model_pred(model_path, img, class_names)
+
+#     plt.imshow(img_show)
+#     plt.title(f"Real Label: {target_class},   prediction: {pred}")
+#     plt.axis('off');
+
+#     return img
+
+
+classes = ['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']
+
+def load_prepare_image(filepath, img_size, rescale=False):
+    img = tf.io.decode_image(filepath, channels=3)
+    img = tf.image.resize(img, img_size)
+
+    if rescale:
+        return img/255.
+    else:
+        return img
+
+def model_pred(model_path, img, class_names=classes):
+    # Load TFLite model and allocate tensors.
+    interpreter = tf.lite.Interpreter(model_path=model_path)
+    #allocate the tensors
+    interpreter.allocate_tensors()
+
+    input_tensor= np.array(np.expand_dims(img,0), dtype=np.float32)
+    input_index = interpreter.get_input_details()[0]["index"]
+
+    # setting input tensor
+    interpreter.set_tensor(input_index, input_tensor)
+
+    #Run the inference
+    interpreter.invoke()
+    output_details = interpreter.get_output_details()
+
+    # output data of image
+    output_data = interpreter.get_tensor(output_details[0]['index'])
+
+    pred = output_data.argmax()
+
+    food_name = class_names[pred]
+
+    return food_name
+
+def fetch_recipe(food_name):
+    url = "https://recipesapi2.p.rapidapi.com/recipes/"+food_name
+    querystring = {"maxRecipes":"1"}
+
+    headers = {
+        'x-rapidapi-host': "recipesapi2.p.rapidapi.com",
+        'x-rapidapi-key': "f6f6823b91msh9e92fed91d5356ap136f5djsn494d8f582fb3"
+        }
+
+    response = requests.request("GET", url, headers=headers, params=querystring)
+    json_data = json.loads(response.text)
+
+    recipe_data = json_data['data'][0]
+
+    return recipe_data