import gradio as gr
from size_rules import evaluate_size, apply_fit_preference

def predict_size(chest, waist, bicep, shoulder, fit_pref):
    data = {
        "chest": chest,
        "waist": waist,
        "bicep": bicep,
        "shoulder": shoulder
    }
    
    base_size, base_reason = evaluate_size(data)
    final_size, fit_reason = apply_fit_preference(base_size, fit_pref, data)
    
    explanation = {
        "recommended_size": final_size,
        "fit_preference": fit_pref,
        "confidence": "high",
        "reason": f"{base_reason} {fit_reason}"
    }
    
    html_output = f"""
    <div style="
        background-color: #d1fae5; 
        color: #065f46; 
        font-size: 32px; 
        font-weight: bold; 
        text-align: center; 
        border: 2px solid #10b981; 
        padding: 20px; 
        border-radius: 8px;
    ">
        {final_size}
    </div>
    """
    
    return html_output, explanation



with gr.Blocks(title="AI Size Recommendation Engine") as demo:
    gr.Markdown("# AI Size Recommendation Engine")
    gr.Markdown("Enter your body measurements (in inches) to get a deterministic size recommendation.")
    
    

    
    with gr.Row():
        with gr.Column():
            chest = gr.Number(label="Chest (inches)", value=38.0, step=0.5)
            waist = gr.Number(label="Waist (inches)", value=32.0, step=0.5)
            bicep = gr.Number(label="Bicep (inches)", value=13.0, step=0.5)
            shoulder = gr.Number(label="Shoulder (inches)", value=46.0, step=0.5)
            fit = gr.Dropdown(choices=["Slim", "Regular", "Loose"], label="Fit Preference", value="Regular")
            submit_btn = gr.Button("Predict Size", variant="primary")
            
        with gr.Column():
            gr.Markdown("### Recommended Size") 
            output_size = gr.HTML()
            output_json = gr.JSON(label="Explainable AI Output")
            


    # Prediction event
    submit_btn.click(
        fn=predict_size,
        inputs=[chest, waist, bicep, shoulder, fit],
        outputs=[output_size, output_json]
    )

if __name__ == "__main__":
    demo.launch()