Create app.py

app.py

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+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"
+                )