Customize app.py for best model && Update README.md

Author: 18520339

.gitignore

@@ -1,5 +1,6 @@
 *.zip
 *.hdf5
+!Model\ Implement/ResNet152V2/fine_tune_model_best.hdf5
 test.py
 secret.ini
 Dataset

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2021 Quan Dang
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

Model Implement/InceptionResNetV2/InceptionResNetV2.ipynb

@@ -1,7 +1,14 @@
-# Vietnamese Foods Classification
+# A Dataset for Vietnamese Food Images Recognition
 
-> Dataset: https://www.kaggle.com/quandang/vietnamese-foods
+- Dataset: https://www.kaggle.com/quandang/vietnamese-foods
+- Demo: https://www.youtube.com/watch?v=GV_1TGohFU8
 
+## Publication
+> Paper: https://ieeexplore.ieee.org/abstract/document/9530774
+
+This paper introduces a large dataset of **25136 images of 30 popular Vietnamese foods**. Several machine learning and deep learning image classification techniques have been applied to test the dataset and the results were compared and report. A **decent accuracy of 77.54%** and a high **top 5-accuracy of 96.07%** were achieved. The dataset and the performance comparison of state-of-the-art algorithm tested on the dataset will be useful for ones to develop new food image classification algorithms.
+
+## Implementation process
 1.  Collecting Data: https://git.io/Jthak
 2.  Preprocessing Data:
 
@@ -11,5 +18,30 @@
 
     ![](https://github.com/18520339/vietnamese-foods/blob/main/Google%20Sheets/demo.png?raw=true)
 
-3.  Model Implement: https://git.io/Jth2F
-4.  Deployment: https://share.streamlit.io/18520339/vietnamese-foods/main/app.py
+3.  Model Implement: https://git.io/Jc1Bi
+4.  Model Evaluation: https://git.io/Jc7fL
+5.  Deployment: https://share.streamlit.io/18520339/30VNFoods/main/app.py
+
+## License
+
+MIT License
+
+Copyright (c) 2021 [Quan Dang](https://github.com/18520339)
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to deal
+    in the Software without restriction, including without limitation the rights
+    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in all
+    copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+    SOFTWARE.

Model Implement/ResNet152V2/ResNet152V2.ipynb

@@ -1,16 +1,13 @@
+# https://github.com/chriskhanhtran/vn-food-app/blob/master/app.py
 import numpy as np
 import pandas as pd
-
 import plotly.express as px
 import urllib.request
 
 import streamlit as st
 import streamlit.components.v1 as components
-
 from tensorflow.keras.models import load_model
 from tensorflow.keras.preprocessing import image
-from PIL import Image
-from io import StringIO
 
 
 classes = [
@@ -48,8 +45,8 @@
 
 
 def preprocess_image(img_path):
-    img = image.load_img(img_path, target_size=(224, 224))
-    img = image.img_to_array(img)
+    img = image.load_img(img_path, target_size=(300, 300))
+    img = image.img_to_array(img) / 255
     img = np.expand_dims(img, axis=0)
     return img
 
@@ -70,14 +67,14 @@ def plot_probs(outputs):
 )
 
 st.markdown(
-    """
+    '''
     <center>
         <img 
             src='https://www.google.com/logos/doodles/2020/celebrating-banh-mi-6753651837108330.3-2xa.gif' 
             style='width: 90%;'
         >
     </center><br/>
-    """,
+    ''',
     unsafe_allow_html=True
 )
 
@@ -92,7 +89,8 @@ def plot_probs(outputs):
 if uploaded_file is not None:
     bytes_data = uploaded_file.read()
     st.image(bytes_data, use_column_width=True)
-    with open('./test.jpg', 'wb') as f: f.write(bytes_data)
+    with open('./test.jpg', 'wb') as f: 
+        f.write(bytes_data)
 elif url:
     urllib.request.urlretrieve(url, './test.jpg')
     st.markdown(
@@ -101,27 +99,30 @@ def plot_probs(outputs):
     )
 
 img_test = preprocess_image('./test.jpg')
-model = load_model('Model Implement/model.h5')
+model = load_model('Model Implement/ResNet152V2/fine_tune_model_best.hdf5')
 pred_probs = model.predict(img_test)[0]
+print(pred_probs)
 
 index = np.argmax(pred_probs)
 label = classes[index]
 
 st.markdown(
     f'''
-        <h2 style='text-align: center;'>
-            <a 
-                href='https://en.wikipedia.org/wiki/{label.replace(' ', '%20')}' 
-                style='text-decoration: none;'
-                target='_blank'
-            >
-                 {label}
-            </a>
-             - {pred_probs[index] * 100:.2f}%
-        </h2>
+        <div>
+            <h2 style='text-align: center;'>
+                <a 
+                    href='https://en.wikipedia.org/wiki/{label.replace(' ', '%20')}' 
+                    style='text-decoration: none;'
+                    target='_blank'
+                >
+                     {label}
+                </a>
+                 - {pred_probs[index] * 100:.2f}%
+            </h2>
+        </div>
     ''',
     unsafe_allow_html=True
 )
 
 plot_probs(pred_probs)
-st.markdown("[![](https://img.shields.io/badge/GitHub-View_Repository-blue?logo=GitHub)](https://github.com/18520339/vietnamese-foods)")
+st.markdown('[![](https://img.shields.io/badge/GitHub-View_Repository-blue?logo=GitHub)](https://github.com/18520339/30VNFoods)')