import streamlit as st
import train
# import tr
# import prd
x = st.slider('Select a value')
st.write(x, 'squared is', x * 86)

# import streamlit as st
# import cv2
# import numpy as np
# from tensorflow.keras.preprocessing import image
# import tensorflow as tf
# from huggingface_hub import from_pretrained_keras

# model = from_pretrained_keras("okeowo1014/catsanddogsmodel")

# # 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()