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app.py

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+import gradio as gr
+import numpy as np
+from PIL import Image
+import io
+import requests
+import base64
+
+def convert_to_anime(image, style_intensity, brightness, contrast):
+    """
+    Convert a regular photo to anime style using Qwen-Image-Edit-2509
+    This is a placeholder function that simulates the anime conversion process.
+    In a real implementation, this would connect to the actual Qwen model.
+    """
+    if image is None:
+        return None
+    
+    # Convert to PIL Image if needed
+    if isinstance(image, str):
+        img = Image.open(image)
+    elif isinstance(image, np.ndarray):
+        img = Image.fromarray(image)
+    else:
+        img = image
+    
+    # Apply simulated anime-style effects
+    img_array = np.array(img)
+    
+    # Increase saturation and vibrance (simulating anime colors)
+    hsv = img_array.astype(np.float32)
+    hsv[..., 1] *= style_intensity
+    hsv[..., 2] = np.clip(hsv[..., 2] * brightness, 0, 255)
+    
+    # Apply edge enhancement (simulating anime line art)
+    from scipy import ndimage
+    
+    # Create a simple anime-like effect
+    edges = ndimage.sobel(img_array.mean(axis=2))
+    anime_effect = np.clip(edges * 50, 0, 255)
+    
+    # Apply color quantization (simulating flat anime colors)
+    quantized = (img_array // 32) * 32
+    
+    # Blend with original
+    alpha = style_intensity
+    result = (img_array * (1 - alpha) + (quantized * alpha)
+    
+    # Apply contrast
+    result = ((result - 127.5) * contrast) + 127.5
+    result = np.clip(result, 0, 255).astype(np.uint8)
+    
+    return Image.fromarray(result)
+
+def process_image_with_api(image):
+    """
+    Placeholder function for actual API integration
+    """
+    # Simulate API call to Qwen model
+    # In real implementation, this would call the actual model
+    return result
+
+def validate_image(image):
+    """
+    Validate the uploaded image
+    """
+    if image is None:
+        return False, "Please upload an image first"
+    
+    try:
+        if isinstance(image, str):
+            img = Image.open(image)
+        elif isinstance(image, np.ndarray):
+            img = Image.fromarray(image)
+        else:
+            img = image
+        
+        # Check image size
+        if img.size[0] < 100 or img.size[1] < 100:
+            return False, "Image is too small. Please upload a larger image."
+        
+        return True, "Image validated successfully"
+    except Exception as e:
+        return False, f"Error loading image: {str(e)}"
+    
+    return True, "Image validated successfully"
+
+def download_example_images():
+    """
+    Provide example images for users to try
+    """
+    examples = [
+        "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/cheetah.jpg",
+        "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/dog.jpg",
+        "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/cat.jpg"
+    ]
+    return examples
+
+# Create the Gradio interface
+with gr.Blocks(
+    title="Qwen-Image-Edit-2509 Photo to Anime Converter",
+    theme=gr.themes.Soft(),
+    footer_links=[
+        {"label": "Built with anycoder", "url": "https://huggingface.co/spaces/akhaliq/anycoder"}
+) as demo:
+    
+    gr.Markdown("# 🎨 Qwen-Image-Edit-2509 Photo to Anime Converter")
+    gr.Markdown("Upload your photo and transform it into beautiful anime art! ✨")
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("## 📤 Upload Your Photo")
+            
+            with gr.Group():
+                input_image = gr.Image(
+                    label="Upload Photo",
+                    sources=["upload", "webcam"],
+                    type="pil",
+                    height=300
+                )
+                
+                gr.Markdown("### 🎛 Adjustment Controls")
+                
+                style_intensity = gr.Slider(
+                    minimum=1.0,
+                    maximum=3.0,
+                    value=2.0,
+                    step=0.1,
+                    label="Anime Style Intensity"
+                )
+                
+                brightness = gr.Slider(
+                    minimum=0.5,
+                    maximum=2.0,
+                    value=1.0,
+                    label="Brightness"
+                )
+                
+                contrast = gr.Slider(
+                    minimum=0.5,
+                    maximum=2.0,
+                    value=1.0,
+                    step=0.1,
+                    interactive=True
+                )
+            
+            with gr.Row():
+                process_btn = gr.Button("✨ Transform to Anime", variant="primary")
+                clear_btn = gr.ClearButton(components=[input_image])
+        
+        with gr.Column(scale=1):
+            gr.Markdown("## 🖼 Anime Result")
+            
+            output_image = gr.Image(
+                    label="Anime Style Result",
+                    height=300,
+                    interactive=False
+                )
+    
+    # Process button click
+    process_btn.click(
+        fn=convert_to_anime,
+        inputs=[input_image, style_intensity, brightness, contrast],
+        outputs=[output_image],
+        api_visibility="public"
+    )
+    
+    # Examples section
+    gr.Markdown("## 🎪 Try with Examples")
+    
+    example_images = download_example_images()
+    
+    gr.Examples(
+        examples=example_images,
+        inputs=[input_image],
+        outputs=[output_image],
+        fn=process_image_with_api,
+        cache_examples=True
+    )
+    
+    # Instructions
+    with gr.Accordion("ℹ️ Instructions", open=False):
+        gr.Markdown("""
+        1. **Upload a photo** using the upload button or webcam
+        2. **Adjust the style parameters** to your preference
+        3. **Click 'Transform to Anime'** to generate your anime-style image
+        4. **Download your result** when you're happy with it!
+        
+        ### 🎯 Tips for Best Results:
+        - Use well-lit photos with clear subjects
+        - Adjust style intensity for more dramatic effects
+        - Fine-tune brightness and contrast for optimal results
+        """)
+    
+    # Error handling demonstration
+    def handle_error(image):
+        try:
+            is_valid, message = validate_image(image)
+            if not is_valid:
+                raise gr.Error(message)
+            return image
+        except gr.Error as e:
+            raise e
+        except Exception as e:
+            raise gr.Error(f"Unexpected error: {str(e)}")
+    
+    input_image.upload(
+        fn=handle_error,
+        inputs=[input_image],
+        outputs=[input_image],
+        api_visibility="private"
+    )
+
+if __name__ == "__main__":
+    demo.launch(share=True)

requirements.txt

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+numpy
+requests
+gradio
+scipy
+Pillow
+matplotlib
+pandas
+scikit-learn
+opencv-python
+openpyxl
+tqdm