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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='', use_column_width=True)
st.write(f'Predicted class: **{predicted_class}**', unsafe_allow_html=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='', use_column_width=True)
st.write(f'Predicted class: **{predicted_class}**', unsafe_allow_html=True)
if __name__ == '__main__':
main()
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