Update utils.py

utils.py

@@ -1,94 +1,118 @@
-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]
-
+import tensorflow as tf
+import numpy as np
+import torch
+import torch.nn as nn
+import timm
+from torchvision import transforms
+import os
+
+import requests
+import json
+
+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']
+
+##########################################################################
+#                    TENSORFLOW FUNCTIONS                                #
+##########################################################################
+
+def load_prepare_image_tf(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_tf(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
+
+##########################################################################
+#                    PyTorch FUNCTIONS                                   #
+##########################################################################
+
+def get_model_pt(model_path):
+    model = timm.create_model('vit_base_patch16_224', pretrained=False)
+    model.head = nn.Linear(in_features=768, out_features=len(classes), bias=True)
+    model.load_state_dict(torch.load('models/ViT-101-1.pt', map_location='cpu'))
+    return model
+
+def load_prepare_image_pt(input_image):
+    normalize = transforms.Normalize(
+        [0.485, 0.456, 0.406], 
+        [0.229, 0.224, 0.225]
+    )
+    img_transform = transforms.Compose([
+        transforms.Resize((225, 225)),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        normalize,
+    ])
+    input_image = img_transform(input_image).unsqueeze(0)
+    return input_image
+    
+
+def model_pred_pt(input_image, model_path):
+    model = get_model_pt(model_path)
+    probs = model(input_image)
+    y_preds = torch.softmax(probs, dim=1).detach().numpy().argmax()
+    pred = classes[y_preds]
+    return pred
+
+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