feat: added demo files

.gitignore

@@ -1 +1,2 @@
-.DS_Store
+.DS_Store
+.vscode

README.md

@@ -10,16 +10,24 @@
 <!-- TABLE OF CONTENTS -->
 ## Table of Contents
 
-* [About the Project](#about-the-project)
-  * [Overview](#overview)
-  * [Built With](#built-with)
-  * [Dataset](#dataset)
-* [Results](#results)
-* [Contributing](#contributing)
-* [License](#license)
-* [Contact](#contact)
-* [References](#references) 
-* [Acknowledgements](#acknowledgements)
+- [Food-Image-Recognition](#food-image-recognition)
+  - [Table of Contents](#table-of-contents)
+  - [About the Project](#about-the-project)
+    - [Overview](#overview)
+    - [Built With](#built-with)
+    - [Dataset](#dataset)
+  - [Results](#results)
+    - [Demo](#demo)
+    - [Accuracy vs. Epochs](#accuracy-vs-epochs)
+    - [Loss vs. Epochs](#loss-vs-epochs)
+    - [Testing on random images.](#testing-on-random-images)
+    - [Visualization of different layers.](#visualization-of-different-layers)
+    - [Heat-Map & Class-Activation-Map](#heat-map--class-activation-map)
+  - [Contributing](#contributing)
+  - [License](#license)
+  - [Contact](#contact)
+  - [References](#references)
+  - [Acknowledgements](#acknowledgements)
 
 ## About the Project
 
@@ -56,6 +64,10 @@ Source: [The Food-101 Data Set](https://data.vision.ee.ethz.ch/cvl/datasets_extr
 
 ## Results
 
+### Demo
+
+![demo](.images/demo/food_image_recognition.gif)
+
 ### Accuracy vs. Epochs
 
 ![Accuracy vs. Epochs](.images/accuracy.png)

app.py

@@ -0,0 +1,266 @@
+import tensorflow
+from flask import Flask, request, render_template
+import csv
+import math
+import os
+import numpy as np
+from tensorflow.keras.preprocessing import image
+from tensorflow.python.keras.models import load_model
+from werkzeug.utils import secure_filename
+
+tmpl_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'templates')
+app = Flask(__name__, template_folder=tmpl_dir)
+
+UPLOAD_FOLDER = 'static/uploads'
+app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
+
+# define label meaning
+label = ['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',
+         'octopus balls',
+         'tiramisu',
+         'tuna tartare',
+         'waffles']
+
+nu_link = 'https://www.nutritionix.com/food/'
+
+# Loading the best saved model to make predictions.
+tensorflow.keras.backend.clear_session()
+model_best = load_model('best_model_101class.hdf5', compile=False)
+print('model successfully loaded!')
+
+start = [0]
+passed = [0]
+pack = [[]]
+num = [0]
+
+nutrients = [
+    {'name': 'protein', 'value': 0.0},
+    {'name': 'calcium', 'value': 0.0},
+    {'name': 'fat', 'value': 0.0},
+    {'name': 'carbohydrates', 'value': 0.0},
+    {'name': 'vitamins', 'value': 0.0}
+]
+
+with open('nutrition101.csv', 'r') as file:
+    reader = csv.reader(file)
+    nutrition_table = dict()
+    for i, row in enumerate(reader):
+        if i == 0:
+            name = ''
+            continue
+        else:
+            name = row[1].strip()
+        nutrition_table[name] = [
+            {'name': 'protein', 'value': float(row[2])},
+            {'name': 'calcium', 'value': float(row[3])},
+            {'name': 'fat', 'value': float(row[4])},
+            {'name': 'carbohydrates', 'value': float(row[5])},
+            {'name': 'vitamins', 'value': float(row[6])}
+        ]
+
+
+@app.route('/')
+def index():
+    img = 'static/profile.jpg'
+    return render_template('index.html', img=img)
+
+
+@app.route('/recognize')
+def magic():
+    return render_template('recognize.html', img=file)
+
+
+@app.route('/upload', methods=['POST'])
+def upload():
+    file = request.files.getlist("img")
+    for f in file:
+        filename = secure_filename(str(num[0] + 500) + '.jpg')
+        num[0] += 1
+        name = os.path.join(app.config['UPLOAD_FOLDER'], filename)
+        print('save name', name)
+        f.save(name)
+
+    pack[0] = []
+    return render_template('recognize.html', img=file)
+
+
+@app.route('/predict')
+def predict():
+    result = []
+    # pack = []
+    print('total image', num[0])
+    for i in range(start[0], num[0]):
+        pa = dict()
+
+        filename = f'{UPLOAD_FOLDER}/{i + 500}.jpg'
+        print('image filepath', filename)
+        pred_img = filename
+        pred_img = image.load_img(pred_img, target_size=(200, 200))
+        pred_img = image.img_to_array(pred_img)
+        pred_img = np.expand_dims(pred_img, axis=0)
+        pred_img = pred_img / 255.
+
+        pred = model_best.predict(pred_img)
+        print("Pred")
+        print(pred)
+
+        if math.isnan(pred[0][0]) and math.isnan(pred[0][1]) and \
+                math.isnan(pred[0][2]) and math.isnan(pred[0][3]):
+            pred = np.array([0.05, 0.05, 0.05, 0.07, 0.09, 0.19, 0.55, 0.0, 0.0, 0.0, 0.0])
+
+        top = pred.argsort()[0][-3:]
+        label.sort()
+        _true = label[top[2]]
+        pa['image'] = f'{UPLOAD_FOLDER}/{i + 500}.jpg'
+        x = dict()
+        x[_true] = float("{:.2f}".format(pred[0][top[2]] * 100))
+        x[label[top[1]]] = float("{:.2f}".format(pred[0][top[1]] * 100))
+        x[label[top[0]]] = float("{:.2f}".format(pred[0][top[0]] * 100))
+        pa['result'] = x
+        pa['nutrition'] = nutrition_table[_true]
+        pa['food'] = f'{nu_link}{_true}'
+        pa['idx'] = i - start[0]
+        pa['quantity'] = 100
+
+        pack[0].append(pa)
+        passed[0] += 1
+
+    start[0] = passed[0]
+    print('successfully packed')
+    # compute the average source of calories
+    for p in pack[0]:
+        nutrients[0]['value'] = (nutrients[0]['value'] + p['nutrition'][0]['value'])
+        nutrients[1]['value'] = (nutrients[1]['value'] + p['nutrition'][1]['value'])
+        nutrients[2]['value'] = (nutrients[2]['value'] + p['nutrition'][2]['value'])
+        nutrients[3]['value'] = (nutrients[3]['value'] + p['nutrition'][3]['value'])
+        nutrients[4]['value'] = (nutrients[4]['value'] + p['nutrition'][4]['value'])
+
+    nutrients[0]['value'] = nutrients[0]['value'] / num[0]
+    nutrients[1]['value'] = nutrients[1]['value'] / num[0]
+    nutrients[2]['value'] = nutrients[2]['value'] / num[0]
+    nutrients[3]['value'] = nutrients[3]['value'] / num[0]
+    nutrients[4]['value'] = nutrients[4]['value'] / num[0]
+
+    return render_template('results.html', pack=pack[0], whole_nutrition=nutrients)
+
+
+@app.route('/update', methods=['POST'])
+def update():
+    return render_template('index.html', img='static/P2.jpg')
+
+
+if __name__ == "__main__":
+    import click
+
+    @click.command()
+    @click.option('--debug', is_flag=True)
+    @click.option('--threaded', is_flag=True)
+    @click.argument('HOST', default='127.0.0.1')
+    @click.argument('PORT', default=5000, type=int)
+    def run(debug, threaded, host, port):
+        """
+        This function handles command line parameters.
+        Run the server using
+            python server.py
+        Show the help text using
+            python server.py --help
+        """
+        HOST, PORT = host, port
+        app.run(host=HOST, port=PORT, debug=debug, threaded=threaded)
+    run()