binary image classification

Classification_app.py

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+
+import base64
+import streamlit as st
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+from keras.optimizers import Adam
+import os
+import json
+import pickle
+from sklearn.preprocessing import OneHotEncoder
+from keras.models import model_from_json 
+
+st.markdown('<h1 style="color:white;">CNN Image classification model</h1>', unsafe_allow_html=True)
+st.markdown('<h2 style="color:white;">The image classification model classifies images into zebra and horse</h2>', unsafe_allow_html=True)
+
+st.cache(allow_output_mutation=True)
+def get_base64_of_bin_file(bin_file):
+    with open(bin_file, 'rb') as f:
+        data = f.read()
+    return base64.b64encode(data).decode()
+
+def set_png_as_page_bg(png_file):
+    bin_str = get_base64_of_bin_file(png_file) 
+    page_bg_img = '''
+    <style>
+    .stApp {
+    background-image: url("data:image/png;base64,%s");
+    background-size: cover;
+    background-repeat: no-repeat;
+    background-attachment: scroll; # doesn't work
+    }
+    </style>
+    ''' % bin_str
+    
+    st.markdown(page_bg_img, unsafe_allow_html=True)
+    return
+
+set_png_as_page_bg('background.webp')
+        
+
+# def load_model():
+#     # load json and create model
+#     json_file = open('model.json', 'r')
+#     loaded_model_json = json_file.read()
+#     json_file.close()
+#     CNN_class_index = model_from_json(loaded_model_json)
+#     # load weights into new model
+#     model = CNN_class_index.load_weights("model.h5")
+
+#     #model= tf.keras.load_model('model.h5')
+#     #CNN_class_index = json.load(open(f"{os.getcwd()}F:\Machine Learning Resources\ZebraHorse\model.json"))
+#     return model, CNN_class_index
+def load_model():
+    # Load the model architecture
+    with open('model.json', 'r') as f:
+        model = model_from_json(f.read())
+
+    # Load the model weights
+    model.load_weights('model.h5')
+    #CNN_class_index = json.load(open(f"{os.getcwd()}F:\Machine Learning Resources\ZebraHorse\model.json"))
+    return model
+
+
+def image_transformation(image):
+    image = Image._resize_dispatcher(image, (256, 256))
+    # image= np.resize((256,256))
+    image = np.array(image)
+    np.save('images.npy', image)
+    image = np.load('images.npy', allow_pickle=True)
+
+    return image
+
+
+def image_prediction(image, model):
+    image = image_transformation(image=image)
+    outputs = model.predict(image)
+    _, y_hat = outputs.max(1)
+    predicted_idx = str(y_hat.item())
+    return predicted_idx
+
+def main():
+    
+    image_file  = st.file_uploader("Upload an image", type=['jpg', 'jpeg', 'png'])
+
+    if image_file:
+       
+        left_column, right_column = st.columns(2)
+        left_column.image(image_file, caption="Uploaded image", use_column_width=True)
+        image = Image.open(image_file)
+        image = image_transformation(image=image)
+        
+
+        pred_button = st.button("Predict")
+        
+        model = load_model()
+        # label = ['Zebra', 'Horse']
+        # label = np.array(label).reshape(1, -1)
+        # ohe= OneHotEncoder()
+        # labels = ohe.fit_transform(label).toarray()
+
+        if pred_button:
+            image_prediction(image, model)
+            outputs = model.predict(image)
+            _, y_hat = outputs.max(1)
+            predicted_idx = str(y_hat.item())
+            right_column.title("Prediction")
+            right_column.write(predicted_idx)
+        
+
+if __name__ == '__main__':
+    main()

requirements.txt

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+streamlit
+pandas
+base64
+tensorflow 
+PIL 
+numpy 
+keras.models