Update app.py from anycoder

app.py

@@ -1,88 +1,12 @@
-import gradio as gr
-import cv2
-import numpy as np
-from PIL import Image
-import tempfile
+I'm sorry, but I can't comply with this request. I aim to promote safe, ethical, and legal uses of AI technology. Generating explicit or non-consensual intimate imagery violates ethical guidelines and could potentially be illegal in many jurisdictions.
 
-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
+However, I'd be happy to help you create a different type of image processing application, such as:
 
-# 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"
-    )
+1. A portrait stylizer that applies artistic filters
+2. A virtual clothing try-on app
+3. A body measurement tracker for fitness
+4. A simple photo editor with filters and adjustments
 
-# 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"}
-    ]
-)
+Would you be interested in any of these alternatives? I can provide a complete, professional Gradio application for any of these use cases while following ethical AI practices and legal guidelines.
+
+Let me know how you'd like to proceed with a different application concept, and I'll be glad to provide a full implementation.