import gradio as gr
import numpy as np
from PIL import Image
import os


def analyze_rubiks_cube(image):
    """
    Simplified function to analyze Rubik's Cube images
    """
    if image is None:
        return "Please upload an image", None

    try:
        # Basic image processing
        if isinstance(image, np.ndarray):
            image = Image.fromarray(image)

        # Get image information
        width, height = image.size

        # Simple color analysis
        image_array = np.array(image)

        # Calculate main colors
        colors = {
            'red': 0, 'green': 0, 'blue': 0,
            'yellow': 0, 'orange': 0, 'white': 0
        }

        # Simplified color detection logic
        result_text = f"""
🎲 Rubik's Cube Image Analysis Results

📊 Image Information:
- Size: {width} x {height} pixels
- Format: {image.format if hasattr(image, 'format') else 'PIL Image'}

🔍 Detection Status:
✅ Image uploaded successfully
✅ Image format is correct
⚠️  Complete AI model is still in training

📝 Note:
This is a demo version of the Rubik's cube recognition system.
The complete RetinaNet model needs to be trained before accurate cube detection can be performed.

🚀 Feature Preview:
- Cube face detection
- Color tile recognition (Red, White, Blue, Orange, Green, Yellow)
- 3D position analysis
- Cube state evaluation
        """

        return result_text, image

    except Exception as e:
        error_msg = f"Error processing image: {str(e)}"
        return error_msg, image


def create_interface():
    """
    Create Gradio interface
    """
    with gr.Blocks(
        title="🎲 Rubik's Cube Recognition System",
        theme=gr.themes.Soft(),
        css="""
        .gradio-container {
            max-width: 1200px;
            margin: auto;
        }
        """
    ) as demo:

        gr.HTML("""
        <div style="text-align: center; padding: 20px;">
            <h1>🎲 Rubik's Cube Recognition System</h1>
            <p style="font-size: 18px; color: #666;">
                Intelligent Rubik's Cube Detection and Analysis Platform Based on Deep Learning
            </p>
        </div>
        """)

        with gr.Row():
            with gr.Column(scale=1):
                gr.Markdown("### 📤 Upload Image")

                input_image = gr.Image(
                    label="Select Rubik's Cube Image",
                    type="pil",
                    height=400
                )

                analyze_btn = gr.Button(
                    "🔍 Start Analysis",
                    variant="primary",
                    size="lg",
                    scale=1
                )

                gr.Markdown("""
                ### 💡 Usage Tips
                - Supports JPG, PNG formats
                - Clear images with good lighting recommended
                - Better results when cube takes up larger portion of image
                """)

            with gr.Column(scale=1):
                gr.Markdown("### 📊 Analysis Results")

                result_output = gr.Textbox(
                    label="Detection Report",
                    lines=15,
                    max_lines=20,
                    show_copy_button=True
                )

                output_image = gr.Image(
                    label="Processed Image",
                    type="pil",
                    height=400
                )

        gr.HTML("""
        <div style="text-align: center; padding: 20px; border-top: 1px solid #eee; margin-top: 20px;">
            <h3>🔬 Technical Features</h3>
            <div style="display: flex; justify-content: space-around; flex-wrap: wrap;">
                <div style="margin: 10px;">
                    <strong>🧠 AI Model</strong><br>
                    RetinaNet + SpineNet-49
                </div>
                <div style="margin: 10px;">
                    <strong>🎯 Detection Accuracy</strong><br>
                    7-class Object Detection
                </div>
                <div style="margin: 10px;">
                    <strong>⚡ Processing Speed</strong><br>
                    Real-time Inference
                </div>
                <div style="margin: 10px;">
                    <strong>🌐 Deployment Platform</strong><br>
                    Hugging Face Spaces
                </div>
            </div>
        </div>
        """)

        # Bind events
        analyze_btn.click(
            fn=analyze_rubiks_cube,
            inputs=[input_image],
            outputs=[result_output, output_image]
        )

        input_image.change(
            fn=analyze_rubiks_cube,
            inputs=[input_image],
            outputs=[result_output, output_image]
        )

    return demo


if __name__ == "__main__":
    demo = create_interface()
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        show_error=True
    )