import streamlit as st
import tensorflow as tf
import cv2
import numpy as np
from PIL import Image

@st.cache_resource
def load_model():
    return tf.keras.models.load_model('synthetic_underwater_esrgan.h5')

model = load_model()
st.set_page_config(page_title="🌊 Underwater Image Enhancer", layout="wide")

st.title("🌊 Deep Sea Image Super Resolution")
st.write("Upload blurry underwater images to enhance their quality")

uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])

if uploaded_file is not None:
    col1, col2 = st.columns(2)
    with col1:
        st.header("Original Image")
        image = Image.open(uploaded_file)
        st.image(image, use_column_width=True)
    
    if st.button("Enhance Image", type="primary"):
        with st.spinner("Enhancing image..."):
            # Convert to numpy array
            lr_img = np.array(image) / 255.0
            
            # Predict
            sr_img = model.predict(np.expand_dims(lr_img, axis=0))[0]
            sr_img = np.clip(sr_img * 255, 0, 255).astype(np.uint8)
            
            with col2:
                st.header("Enhanced Image")
                st.image(sr_img, use_column_width=True)
                
                # Download button
                st.download_button(
                    label="⬇️ Download Enhanced Image",
                    data=cv2.imencode('.png', cv2.cvtColor(sr_img, cv2.COLOR_RGB2BGR))[1].tobytes(),
                    file_name="enhanced.png",
                    mime="image/png"
                )