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app.py

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+import gradio as gr
+import cv2
+import numpy as np
+from PIL import Image
+import tempfile
+
+def apply_image_transformations(img, transform, scale_factor=1.0):
+    """Apply various image transformations"""
+    img = np.array(img)
+    
+    if transform == "cartoon":
+        # Apply cartoon effect
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        gray = cv2.medianBlur(gray, 5)
+        edges = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, 
+                                     cv2.THRESH_BINARY, 9, 9)
+        
+        color = cv2.bilateralFilter(img, 9, 300, 300)
+        return cv2.bitwise_and(color, color, mask=edges)
+    
+    elif transform == "oil_painting":
+        # Apply oil painting effect
+        res = cv2.xphoto.oilPainting(img, 7, 1)
+        return res
+    
+    elif transform == "pixelate":
+        # Pixelation effect
+        height, width = img.shape[:2]
+        small = cv2.resize(img, (32, 32), interpolation=cv2.INTER_LINEAR)
+        return cv2.resize(small, (width, height), interpolation=cv2.INTER_NEAREST)
+    
+    elif transform == "sketch":
+        # Pencil sketch effect
+        gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        inverted = cv2.bitwise_not(gray_img)
+        blurred = cv2.GaussianBlur(inverted, (21, 21), 0)
+        inverted_blurred = cv2.bitwise_not(blurred)
+        return cv2.divide(gray_img, inverted_blurred, scale=256.0)
+    
+    return img  # Return original if no transformation selected
+
+# Gradio interface
+with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue")) as demo:
+    gr.Markdown("# 🖼️ Professional Image Transformer")
+    gr.Markdown("Upload your photo and apply various artistic transformations")
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            image_input = gr.Image(type="pil", label="Upload Image")
+            transform_type = gr.Radio(
+                choices=["cartoon", "oil_painting", "pixelate", "sketch"],
+                value="cartoon",
+                label="Transformation Style"
+            )
+            transform_btn = gr.Button("Apply Transformation", variant="primary")
+        
+        with gr.Column(scale=2):
+            output_image = gr.Image(label="Transformed Image", interactive=False)
+    
+    # Examples gallery
+    gr.Examples(
+        examples=[
+            ["examples/portrait1.jpg", "cartoon"],
+            ["examples/landscape1.jpg", "oil_painting"],
+            ["examples/group1.jpg", "pixelate"]
+        ],
+        inputs=[image_input, transform_type],
+        outputs=output_image,
+        fn=apply_image_transformations,
+        cache_examples=True
+    )
+    
+    # Transformation function
+    transform_btn.click(
+        fn=apply_image_transformations,
+        inputs=[image_input, transform_type],
+        outputs=output_image,
+        api_name="transform"
+    )
+
+# Launch configuration
+demo.launch(
+    title="Image Transformer Pro",
+    css="footer {visibility: hidden}",
+    footer_links=[
+        {"label": "Built with AnyCoder", "url": "https://huggingface.co/spaces/akhaliq/anycoder"}
+    ]
+)

requirements.txt

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+Pillow
+cv2
+gradio
+numpy