Add application file

app.py

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+import streamlit as st
+from PIL import Image
+import matplotlib.pyplot as plt
+import tensorflow_hub as hub
+import tensorflow as tf
+import numpy as np
+from tensorflow import keras
+from tensorflow.keras.models import load_model
+from tensorflow.keras import preprocessing
+import time
+fig = plt.figure()
+
+with open("custom.css") as f:
+    st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
+
+st.title('Bag Classifier')
+
+st.markdown("Welcome to this simple web application that classifies bags. The bags are classified into six different classes namely: Backpack, Briefcase, Duffle, Handbag and Purse.")
+
+
+def main():
+    file_uploaded = st.file_uploader("Choose File", type=["png","jpg","jpeg"])
+    class_btn = st.button("Classify")
+    if file_uploaded is not None:    
+        image = Image.open(file_uploaded)
+        st.image(image, caption='Uploaded Image', use_column_width=True)
+        
+    if class_btn:
+        if file_uploaded is None:
+            st.write("Invalid command, please upload an image")
+        else:
+            with st.spinner('Model working....'):
+                plt.imshow(image)
+                plt.axis("off")
+                predictions = predict(image)
+                time.sleep(1)
+                st.success('Classified')
+                st.write(predictions)
+                st.pyplot(fig)
+
+
+def predict(image):
+    classifier_model = "base_dir.h5"
+    IMAGE_SHAPE = (224, 224,3)
+    model = load_model(classifier_model, compile=False, custom_objects={'KerasLayer': hub.KerasLayer})
+    test_image = image.resize((224,224))
+    test_image = preprocessing.image.img_to_array(test_image)
+    test_image = test_image / 255.0
+    test_image = np.expand_dims(test_image, axis=0)
+    class_names = [
+          'Backpack',
+          'Briefcase',
+          'Duffle', 
+          'Handbag', 
+          'Purse']
+    predictions = model.predict(test_image)
+    scores = tf.nn.softmax(predictions[0])
+    scores = scores.numpy()
+    results = {
+          'Backpack': 0,
+          'Briefcase': 0,
+          'Duffle': 0, 
+          'Handbag': 0, 
+          'Purse': 0
+}
+
+    
+    result = f"{class_names[np.argmax(scores)]} with a { (100 * np.max(scores)).round(2) } % confidence." 
+    return result
+
+
+
+
+
+
+
+
+
+    
+
+if __name__ == "__main__":
+    main()
+
+