Create app.py

app.py

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+#!streamlit/bin/python
+import streamlit as st
+from pathlib import Path
+import pandas as pd
+import numpy as np
+import tensorflow as tf
+from PIL import Image
+from io import BytesIO
+import json
+#from GDownload import download_file_from_google_drive
+
+@st.cache(allow_output_mutation=True)
+def load_model():
+#    if selected_model == 'PVAN-Stanford':
+#        model_location = '1-q1R5dLfIFW7BbzKuYTjolAoqpjVClsb'
+#        save_dest = Path('saved_model')
+#        save_dest.mkdir(exist_ok=True)
+#        saved_model = Path("saved_model/FerNet_EfficientNet.h5")
+  
+#    elif selected_model == 'PVAN-Tsinghua':
+  # model_location = '1-q1R5dLfIFW7BbzKuYTjolAoqpjVClsb'
+  # save_dest = Path('saved_model')
+  # save_dest.mkdir(exist_ok=True)
+  # saved_model = Path("saved_model/FerNet_EfficientNet.h5")
+  
+  # if not saved_model.exists():
+  #     download_file_from_google_drive(model_location, saved_model)
+  saved_model = str(Path().parent.absolute())+"/saved_models/FerNetEfficientNetB2"
+  saved_model = tf.keras.models.load_model(saved_model)
+  return saved_model
+
+@st.cache
+def load_classes():
+    with open(str(Path().parent.absolute())+'/App/classes_dict.json') as classes:
+        class_names = json.load(classes)
+    return class_names
+
+def load_and_prep_image(filename, img_shape=260):
+  #img = tf.io.read_file(filename)
+  img = np.array(filename)#tf.io.decode_image(filename, channels=3)
+  # Resize our image
+  img = tf.image.resize(img, [img_shape,img_shape])
+  # Scale
+  return img # don't need to resclae images for EfficientNet models in Tensorflow
+
+if __name__ == '__main__':
+  
+  hide_st_style = """
+            <style>
+            footer {visibility: hidden;}
+            header {visibility: hidden;}
+            </style>
+            """
+  st.markdown(hide_st_style, unsafe_allow_html=True)
+
+  st.title("Dog Breeds Detector")
+  
+  options = ['PVAN-Stanford', 'PVAN-Tsinghua']
+  selected_model = st.selectbox('Select a model to use (Default: PVAN-Stanford):', options)
+  
+  saved_model = load_model()
+  class_names = load_classes()
+  
+  st.write("Choose any dog image and get the corresponding breed:")
+
+  uploaded_image = st.file_uploader("Choose an image...")
+    
+  if uploaded_image:
+    uploaded_image = Image.open(uploaded_image)
+    # try:
+    uploaded_image = uploaded_image.convert("RGB")
+    membuf = BytesIO()
+    uploaded_image.save(membuf, format="jpeg")
+    uploaded_image = Image.open(membuf)
+    # finally:
+
+
+    image_for_the_model = load_and_prep_image(uploaded_image)
+    prediction = saved_model.predict(tf.expand_dims(image_for_the_model, axis=0), verbose=0)
+    
+    top_k_proba, top_k_indices = tf.nn.top_k(prediction,k=5)
+    top_5_classes = {top_n+1:class_names[str(top_k)] for top_n, top_k in enumerate(list(tf.squeeze(top_k_indices).numpy()))}
+    top_k_proba = tf.squeeze(top_k_proba).numpy()
+    top_5_classes = pd.DataFrame({"Top-k":top_5_classes.keys(), "Dog Breed": top_5_classes.values(), "Probability": top_k_proba})
+    #top_5_classes.set_index("Top-k", inplace=True)
+    
+    print(tf.argmax(prediction, axis=1).numpy())
+    predicted_breed = class_names[str(tf.argmax(prediction, axis=1).numpy()[0])]
+    predicted_breed = ' '.join(predicted_breed.split('_'))
+    predicted_breed = predicted_breed.title()
+    st.header(f'This dog looks like a {predicted_breed}')
+    
+    col1, col2 = st.columns([1,2])
+    
+    col1.image(uploaded_image,use_column_width=True)
+    col2.bar_chart(top_5_classes, x="Dog Breed", y="Probability")