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README.md

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+---
+title: Test Image
+emoji: 💻
+colorFrom: indigo
+colorTo: green
+sdk: gradio
+sdk_version: 5.1.0
+app_file: app.py
+pinned: false
+license: mit
+short_description: test image and slide bar
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import numpy as np
+import gradio as gr
+import cv2
+
+# Define a function to apply a more aesthetically pleasing sharpening filter to an image
+def enhance(input_img, brightness):
+    # Convert the image to float32 for better precision
+    input_img = input_img.astype(np.float32) / 255.0
+    
+    # Define a more subtle sharpening filter
+    sharpen_filter = np.array([
+        [0, -1, 0],
+        [-1, 5, -1],
+        [0, -1, 0]
+    ])
+    
+    # Apply the filter to the image
+    sharpened_img = cv2.filter2D(input_img, -1, sharpen_filter)
+    
+    # Apply a slight Gaussian blur to smooth out the sharpening effect
+    smoothed_img = cv2.GaussianBlur(sharpened_img, (3, 3), 0)
+    
+    # Adjust brightness
+    smoothed_img = np.clip(smoothed_img + brightness, 0, 1)
+    
+    # Normalize the image to the range [0, 1]
+    smoothed_img = smoothed_img / smoothed_img.max()
+    
+    # Convert back to uint8 for display
+    smoothed_img = (smoothed_img * 255).astype(np.uint8)
+    
+    return smoothed_img
+
+# Create a Gradio interface for the enhancement function
+demo = gr.Interface(
+    enhance, 
+    inputs=[gr.Image(), gr.Slider(-1.0, 1.0, value=0.0, label="Brightness")], 
+    outputs="image"
+)
+
+# Launch the Gradio app
+demo.launch(show_error=True)

requirements.txt

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+opencv-python