app.py

import streamlit as st
import numpy as np
from tensorflow.keras.preprocessing import image
from keras.models import load_model


# Define the dictionary of classes and load the model
CLASSES = {
    'french_bulldog': 0,
    'german_shepherd': 1,
    'golden_retriever': 2,
    'poodle': 3,
    'yorkshire_terrier': 4
}
MODEL_PATH = 'best_model.h5'
model = load_model(MODEL_PATH)


# Define a function to make predictions on a given image
def predict_breed(image_file):
    img = image.load_img(image_file, target_size=(256, 256))
    x = image.img_to_array(img)
    x = np.expand_dims(x, axis=0)
    x = x / 255.
    preds = model.predict(x)
    class_idx = np.argmax(preds[0])
    predicted_class = [k for k, v in CLASSES.items() if v == class_idx][0]
    return predicted_class


# Create the Streamlit app
def main():
    st.title('Dog Breed Classification')
    uploaded_file = st.file_uploader('Choose an image of a dog', key='file_uploader', type=['jpg', 'jpeg', 'png'])
    if uploaded_file is not None:
        image_file = uploaded_file.name
        with open(image_file, 'wb') as f:
            f.write(uploaded_file.getbuffer())
        predicted_class = predict_breed(image_file)
        st.image(uploaded_file, caption=f'Predicted class: {predicted_class}', use_column_width=True)

    # Add a button to trigger image upload
    if st.button('Upload and Predict'):
        uploaded_file = st.file_uploader('Choose an image of a dog', key='file_uploader_2', type=['jpg', 'jpeg', 'png'])
        if uploaded_file is not None:
            image_file = uploaded_file.name
            with open(image_file, 'wb') as f:
                f.write(uploaded_file.getbuffer())
            predicted_class = predict_breed(image_file)
            st.image(uploaded_file, caption=f'Predicted class: {predicted_class}', use_column_width=True)
            

if __name__ == '__main__':
    main()