Code Editor for app.py

import streamlit as st
from PIL import Image
import numpy as np

# Mock model predictions for demonstration

def predict(image, model_type):
    # In actual implementation, load and use your models here
    if model_type == 'KNN':
        return np.random.choice(['cloudy', 'sunrise', 'rainy'])
    elif model_type == 'SVM':
        return np.random.choice(['cloudy', 'sunrise', 'rainy'])

st.title('Image Classification App')
st.write('Upload an image to classify it into one of the following categories: cloudy, sunrise, or rainy.')
model_type = st.selectbox('Select Model', ['KNN', 'SVM'])
upload_image = st.file_uploader('Choose an image...', type=['jpg', 'jpeg', 'png'])

if upload_image is not None:
    image = Image.open(upload_image)
    st.image(image, caption='Uploaded Image.', use_column_width=True)
    st.write('Classifying...')
    label = predict(image, model_type)
    st.write(f'Predicted Class: **{label}**')