Delete Classification_app.py

Classification_app.py

@@ -1,112 +0,0 @@
-
-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()