"""
Labasni Recommender Service - Hugging Face Space
✅ Compatible Gradio 6.x avec API REST forcée
"""

import gradio as gr
import json
from recommender_model import recommend_outfit_ml

def recommend_outfit_api(clothes_json: str, preference: str, city: str = "Tunis"):
    """
    API endpoint pour les recommandations d'outfit
    """
    try:
        print(f"🔍 Received request - Preference: {preference}, City: {city}")
        print(f"📦 Clothes data length: {len(clothes_json)} chars")
        
        clothes_data = json.loads(clothes_json)
        print(f"✅ Parsed {len(clothes_data)} clothes items")
        
        result = recommend_outfit_ml(clothes_data, preference, city)
        print(f"✅ Recommendation generated: {result.get('success', False)}")
        
        return json.dumps(result, indent=2)
    except json.JSONDecodeError as e:
        error_msg = f"Invalid JSON format: {str(e)}"
        print(f"❌ JSON Error: {error_msg}")
        return json.dumps({
            "success": False,
            "error": error_msg,
            "message": "Le format JSON des vêtements est invalide"
        }, indent=2)
    except Exception as e:
        error_msg = str(e)
        print(f"❌ Error: {error_msg}")
        return json.dumps({
            "success": False,
            "error": error_msg,
            "message": "Erreur lors de la recommandation"
        }, indent=2)

# ✅ Version avec gr.Blocks pour plus de contrôle sur l'API
with gr.Blocks() as demo:
    gr.Markdown("# 🎽 Labasni Outfit Recommender")
    gr.Markdown("Recommandations d'outfits basées sur ML")
    
    with gr.Row():
        with gr.Column():
            clothes_input = gr.Textbox(
                label="Clothes Data (JSON)",
                placeholder='[{"id":"top1","category":"top",...}]',
                lines=10,
                value='[{"id":"top1","category":"top","style":"casual","color":"white","season":"all","score":0.8},{"id":"bottom1","category":"bottom","style":"casual","color":"blue","season":"all","score":0.7},{"id":"shoe1","category":"footwear","style":"casual","color":"black","season":"all","score":0.9}]'
            )
            preference_input = gr.Dropdown(
                choices=["casual", "formal", "sport", "chic", "elegant"],
                label="Preference",
                value="casual"
            )
            city_input = gr.Textbox(label="City", value="Tunis")
            submit_btn = gr.Button("Get Recommendation", variant="primary")
        
        with gr.Column():
            output = gr.Textbox(
                label="Recommended Outfit (JSON)",
                lines=15
            )
    
    # Exemples
    gr.Examples(
        examples=[
            [
                '[{"id":"top1","category":"top","style":"casual","color":"white","season":"all","score":0.8},{"id":"bottom1","category":"bottom","style":"casual","color":"blue","season":"all","score":0.7},{"id":"shoe1","category":"footwear","style":"casual","color":"black","season":"all","score":0.9}]',
                "casual",
                "Tunis"
            ]
        ],
        inputs=[clothes_input, preference_input, city_input]
    )
    
    # Event handler
    submit_btn.click(
        fn=recommend_outfit_api,
        inputs=[clothes_input, preference_input, city_input],
        outputs=output,
        api_name="predict"  # ✅ Force le nom de l'API
    )

if __name__ == "__main__":
    # ✅ Lancer avec l'API activée
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False
    )