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import streamlit as st
import cv2
import numpy as np
from tensorflow.keras.preprocessing import image
import tensorflow as tf
# Load the saved model (replace with your model filename)
model = tf.keras.models.load_model('cat_dog_classifier.keras')
# Image dimensions for the model
img_width, img_height = 224, 224
def preprocess_image(img):
"""Preprocesses an image for prediction."""
img = cv2.resize(img, (img_width, img_height))
img = img.astype('float32') / 255.0
img = np.expand_dims(img, axis=0)
return img
def predict_class(image):
"""Predicts image class and probabilities."""
preprocessed_img = preprocess_image(image)
prediction = model.predict(preprocessed_img)
class_names = ['cat', 'dog'] # Adjust class names according to your model
return class_names[np.argmax(prediction)], np.max(prediction)
def display_results(class_name, probability):
"""Displays prediction results in a progress bar style."""
st.write(f"**Predicted Class:** {class_name}")
# Create a progress bar using st.progress
progress = st.progress(0)
for i in range(100):
progress.progress(i + 1)
if i == int(probability * 100):
break
st.write(f"**Probability:** {probability:.2f}")
def main():
"""Main app function."""
st.title("Image Classifier")
st.write("Upload an image to classify it as cat or dog.")
uploaded_file = st.file_uploader("Choose an image...", type="jpg")
if uploaded_file is not None:
image = cv2.imdecode(np.frombuffer(uploaded_file.read(), np.uint8), cv2.IMREAD_COLOR)
st.image(image, caption="Uploaded Image", use_column_width=True)
predicted_class, probability = predict_class(image)
display_results(predicted_class, probability)
main()
# if __name__ == '__main__':
# main()
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