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| import tensorflow as tf | |
| import streamlit as st | |
| # import cv2 | |
| from PIL import Image | |
| from streamlit_image_select import image_select | |
| # import os | |
| def load_and_prep_image(filename, img_shape=224, scale=True): | |
| """ | |
| Reads in an image from filename, turns it into a tensor and reshapes into | |
| (224, 224, 3). | |
| Parameters | |
| ---------- | |
| filename (str): string filename of target image | |
| img_shape (int): size to resize target image to, default 224 | |
| scale (bool): whether to scale pixel values to range(0, 1), default True | |
| """ | |
| # # Read in the image | |
| # img = tf.io.read_file(filename) | |
| # # Decode it into a tensor | |
| # img = tf.io.decode_image(img) | |
| img = tf.convert_to_tensor(filename) | |
| # Resize the image | |
| img = tf.image.resize(img, [img_shape, img_shape]) | |
| if scale: | |
| # Rescale the image (get all values between 0 and 1) | |
| return img/255. | |
| else: | |
| return img | |
| 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'] | |
| #load model | |
| def cache_model(model_name): | |
| model = tf.keras.models.load_model(model_name) | |
| return (model) | |
| model = cache_model("101_food_class_100_percent_saved_big_model") | |
| # model = tf.keras.models.load_model("101_food_class_100_percent_saved_big_model") | |
| st.write(""" | |
| # Food Classification App | |
| """ | |
| ) | |
| st.write("""#### ***Upload food image and this app will classify the uploaded image from one of the mentioned categories.***""") | |
| st.write(""" | |
| **Some major food categories** | |
| ``` | |
| pizza, cup_cakes, donuts, samosa, ice_cream, french_fries, waffles etc. | |
| ``` | |
| for full categories list please visit the [link](https://github.com/gourav300/food_app) | |
| """) | |
| ### load file | |
| uploaded_file = st.file_uploader("Upload an image file for above mentioned Food Categories or select a food image", type=["jpg", "png", "jpeg"]) | |
| test_img = image_select( | |
| label="Select a sample food image", | |
| images=[ | |
| "test_images/Cardamom-Saffron-Cupcakes-1.jpg", | |
| "test_images/doughnut_1.jpg", | |
| "test_images/frenchfries.jpg", | |
| "test_images/Punjabi-Samosa-2.jpg", | |
| "test_images/dumpling.jpg", | |
| "test_images/pizza_1.jpg", | |
| "test_images/waffle.jpg", | |
| "test_images/pancake.jpg" | |
| ], | |
| captions=["Cupcake", "Doughnut", "French fries", "Samosa", "Dumpling", "Pizza", "Waffle", "Pancake"], | |
| ) | |
| # st.set_option('deprecation.showfileUploaderEncoding', False) | |
| if uploaded_file is not None: | |
| image = Image.open(uploaded_file) | |
| st.image(image,width = 500)#, use_column_width=True) | |
| else: | |
| image = Image.open(test_img) | |
| st.image(image, width = 500)#,use_column_width=True) | |
| # Load the image and make predictions | |
| img = load_and_prep_image(image, scale=False) # don't scale images for EfficientNet predictions | |
| pred_prob = model.predict(tf.expand_dims(img, axis=0)) # model accepts tensors of shape [None, 224, 224, 3] | |
| pred_class = class_names[pred_prob.argmax()] # find the predicted class | |
| st.write(f"## Predicted food: {pred_class}, with Probability: {pred_prob.max():.2f}") | |