Update app.py

app.py

@@ -1,61 +1,74 @@
+import os
 import streamlit as st
-from PIL import Image, ImageEnhance
+from PIL import Image
 import numpy as np
 import cv2
 
-# Title and Description
+# Ensure OpenCV installation
+try:
+    import cv2
+except ModuleNotFoundError:
+    os.system("pip install opencv-python-headless==4.8.0.74")
+    import cv2
+
+# Streamlit App Title
 st.title("Image Enhancer App")
-st.markdown("""
-### Enhance your images with AI-powered tools and essential filters!
-Upload an image and apply enhancements like brightness, contrast, sharpness, and more.
-""")
 
 # File Uploader
-uploaded_file = st.file_uploader("Upload an Image", type=["jpg", "jpeg", "png"])
+uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
 
-if uploaded_file:
+if uploaded_file is not None:
+    # Load image using PIL and convert to OpenCV format
     image = Image.open(uploaded_file)
-    st.image(image, caption="Original Image", use_column_width=True)
-
-    # Sidebar Filters
-    st.sidebar.header("Enhancement Options")
-
-    # Brightness
-    brightness = st.sidebar.slider("Brightness", 0.5, 3.0, 1.0)
-    enhancer = ImageEnhance.Brightness(image)
-    image = enhancer.enhance(brightness)
-
-    # Contrast
-    contrast = st.sidebar.slider("Contrast", 0.5, 3.0, 1.0)
-    enhancer = ImageEnhance.Contrast(image)
-    image = enhancer.enhance(contrast)
-
-    # Sharpness
-    sharpness = st.sidebar.slider("Sharpness", 0.5, 3.0, 1.0)
-    enhancer = ImageEnhance.Sharpness(image)
-    image = enhancer.enhance(sharpness)
+    image_np = np.array(image)
 
-    # Saturation Adjustment (Using OpenCV for Saturation)
-    saturation = st.sidebar.slider("Saturation", 0.5, 3.0, 1.0)
+    # Display original image
+    st.subheader("Original Image")
+    st.image(image, caption="Uploaded Image", use_column_width=True)
 
-    # Ensure the image is in a valid NumPy format
-    image_np = np.array(image).astype(np.uint8)
+    # Enhance Options
+    st.subheader("Enhancement Options")
+    enhancement = st.selectbox("Choose an enhancement type:", [
+        "Brightness Adjustment",
+        "Contrast Enhancement",
+        "Sharpening",
+        "Denoising",
+        "Edge Detection"
+    ])
 
-    if len(image_np.shape) == 3:  # Check if the image has color channels
-        image_cv = cv2.cvtColor(image_np, cv2.COLOR_RGB2HSV)
-        image_cv[:, :, 1] = np.clip(image_cv[:, :, 1] * saturation, 0, 255)
-        image = Image.fromarray(cv2.cvtColor(image_cv, cv2.COLOR_HSV2RGB))
-    else:
-        st.warning("Saturation adjustment is only available for color images.")
+    if enhancement == "Brightness Adjustment":
+        brightness = st.slider("Adjust Brightness", -100, 100, 0)
+        enhanced_image = cv2.convertScaleAbs(image_np, beta=brightness)
+    elif enhancement == "Contrast Enhancement":
+        contrast = st.slider("Adjust Contrast", 1.0, 3.0, 1.0)
+        alpha = contrast  # Simple contrast control
+        beta = 0          # Simple brightness control
+        enhanced_image = cv2.convertScaleAbs(image_np, alpha=alpha, beta=beta)
+    elif enhancement == "Sharpening":
+        kernel = np.array([[0, -1, 0],
+                           [-1, 5, -1],
+                           [0, -1, 0]])
+        enhanced_image = cv2.filter2D(image_np, -1, kernel)
+    elif enhancement == "Denoising":
+        enhanced_image = cv2.fastNlMeansDenoisingColored(image_np, None, 10, 10, 7, 21)
+    elif enhancement == "Edge Detection":
+        gray_image = cv2.cvtColor(image_np, cv2.COLOR_BGR2GRAY)
+        edges = cv2.Canny(gray_image, 100, 200)
+        enhanced_image = cv2.cvtColor(edges, cv2.COLOR_GRAY2BGR)
 
     # Display Enhanced Image
-    st.image(image, caption="Enhanced Image", use_column_width=True)
+    st.subheader("Enhanced Image")
+    st.image(enhanced_image, caption="Enhanced Image", use_column_width=True)
 
-    # Save Option
-    st.sidebar.subheader("Download")
-    if st.sidebar.button("Download Enhanced Image"):
-        image.save("enhanced_image.png")
-        st.sidebar.write("Image saved as `enhanced_image.png`!")
+    # Download Option
+    st.subheader("Download Enhanced Image")
+    result_image = Image.fromarray(enhanced_image)
+    st.download_button(
+        label="Download Image",
+        data=result_image.tobytes(),
+        file_name="enhanced_image.png",
+        mime="image/png"
+    )
 
-# Footer
-st.markdown("Built by NS!")
+# Hugging Face Integration
+st.markdown("**Note:** Ensure this app is running on Hugging Face Spaces with the required dependencies listed in the `requirements.txt` file.")