edit the app code

app.py

@@ -2,6 +2,7 @@ import streamlit as st
 import cv2
 import numpy as np
 from PIL import Image
+import io
 
 def process_image(image, operation, rotation_angle=0, blur_value=5):
     img_array = np.array(image)
@@ -17,33 +18,55 @@ def process_image(image, operation, rotation_angle=0, blur_value=5):
     elif operation == "Edge Detection":
         processed_img = cv2.Canny(img_cv, 100, 200)
     elif operation == "Blur":
-        processed_img = cv2.GaussianBlur(img_cv, (blur_value, blur_value), 0)
+        processed_img = cv2.GaussianBlur(img_cv, (blur_value*2+1, blur_value*2+1), 0)
     else:
         processed_img = img_cv
     
     return processed_img
 
 def main():
-    st.title("Image Processing App")
-    uploaded_image = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
+    st.title("🖼️ Image Processing App 🎨")
+    st.markdown("Enhance your images with various effects!")
+    
+    uploaded_image = st.file_uploader("📤 Upload an image", type=["jpg", "png", "jpeg"])
     
     if uploaded_image is not None:
         image = Image.open(uploaded_image)
-        st.image(image, caption="Original Image", use_container_width=True)
         
-        operation = st.selectbox("Select an operation", ["None", "Greyscale", "Rotate", "Edge Detection", "Blur"])
+        st.markdown("### ✏️ Editing Options")
+        col3, col4 = st.columns(2)
+        
+        with col3:
+            operation = st.selectbox("🔧 Select an operation", ["None", "Greyscale", "Rotate", "Edge Detection", "Blur"])
         
         rotation_angle = 0
         blur_value = 5
         
         if operation == "Rotate":
-            rotation_angle = st.slider("Select Rotation Angle", -180, 180, 0)
+            with col4:
+                rotation_angle = st.slider("🔄 Rotation Angle", -180, 180, 0)
         elif operation == "Blur":
-            blur_value = st.slider("Select Blur Intensity", 1, 25, 5, step=2)
+            with col4:
+                blur_value = st.slider("🌫️ Blur Intensity", 1, 25, 5, step=2)
+        
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            st.image(image, caption="🖼️ Original Image", use_container_width=True)
         
         if operation != "None":
             processed_img = process_image(image, operation, rotation_angle, blur_value)
-            st.image(processed_img, caption="Processed Image", use_container_width=True, channels="GRAY" if operation == "Greyscale" or operation == "Edge Detection" else "BGR")
+            
+            with col2:
+                st.image(processed_img, caption="✨ Processed Image", use_container_width=True, channels="GRAY" if operation == "Greyscale" or operation == "Edge Detection" else "BGR")
+                
+            processed_pil = Image.fromarray(cv2.cvtColor(processed_img, cv2.COLOR_BGR2RGB) if operation != "Greyscale" and operation != "Edge Detection" else processed_img)
+            buf = io.BytesIO()
+            processed_pil.save(buf, format="PNG")
+            byte_im = buf.getvalue()
+            
+            st.markdown("### 📥 Download Your Edited Image")
+            st.download_button(label="💾 Download", data=byte_im, file_name="processed_image.png", mime="image/png")
 
 if __name__ == "__main__":
     main()