"""
Hugging Face Space for EPL Predictions
Deploy this as a Gradio app on HF Spaces
"""
import gradio as gr
import numpy as np
import pandas as pd
from model_predictor import EPLPredictor
import json

# Initialize predictor
print("Loading models...")
predictor = EPLPredictor(use_local=False)  # Will download from HF
print("Models loaded!")

def predict_match(home_team, away_team, home_odds, draw_odds, away_odds):
    """Predict match outcome"""
    try:
        # Create odds dict
        best_odds = {
            'H': {'odds': home_odds},
            'D': {'odds': draw_odds},
            'A': {'odds': away_odds}
        }

        # Get predictions
        result = predictor.predict(
            home_team=home_team,
            away_team=away_team,
            best_odds=best_odds
        )

        # Format output
        output = f"""
## Match Prediction: {home_team} vs {away_team}

### Ensemble Probabilities:
- **Home Win**: {result['ensemble']['H']:.1%}
- **Draw**: {result['ensemble']['D']:.1%}
- **Away Win**: {result['ensemble']['A']:.1%}
- **Over 2.5 Goals**: {result['ensemble']['over25']:.1%}
- **BTTS**: {result['ensemble']['btts']:.1%}

### Expected Goals:
- {home_team}: {result['expected_goals']['home']:.2f}
- {away_team}: {result['expected_goals']['away']:.2f}

### Model Components:
**Poisson**: H:{result['poisson']['H']:.1%} D:{result['poisson']['D']:.1%} A:{result['poisson']['A']:.1%}
**XGBoost**: H:{result['xgboost']['H']:.1%} D:{result['xgboost']['D']:.1%} A:{result['xgboost']['A']:.1%}
        """

        # Check for value
        value_analysis = ""
        for market, prob in [('Home', result['ensemble']['H']),
                             ('Draw', result['ensemble']['D']),
                             ('Away', result['ensemble']['A'])]:
            if market == 'Home':
                odds = home_odds
            elif market == 'Draw':
                odds = draw_odds
            else:
                odds = away_odds

            value = predictor.calculate_value(prob, odds)
            if value['has_value']:
                value_analysis += f"\n⚡ **VALUE BET**: {market} @ {odds:.2f} (Edge: {value['edge']:.1f}%)"

        if value_analysis:
            output += f"\n### Value Bets Found:{value_analysis}"
        else:
            output += "\n### No value bets found at these odds"

        return output

    except Exception as e:
        return f"Error: {str(e)}"

def batch_predict(csv_text):
    """Predict multiple matches from CSV"""
    try:
        # Parse CSV
        lines = csv_text.strip().split('\n')
        results = []

        for line in lines[1:]:  # Skip header
            parts = line.split(',')
            if len(parts) >= 5:
                home, away = parts[0].strip(), parts[1].strip()
                h_odds, d_odds, a_odds = float(parts[2]), float(parts[3]), float(parts[4])

                pred = predict_match(home, away, h_odds, d_odds, a_odds)
                results.append(pred)

        return "\n---\n".join(results)
    except Exception as e:
        return f"Error processing CSV: {str(e)}"

# Create Gradio interface
with gr.Blocks(title="EPL Match Predictor") as app:
    gr.Markdown("""
    # ⚽ EPL Match Predictor

    Powered by ensemble models (Poisson + XGBoost) trained on EPL data.

    Models available at: [gnosisx/epl-ensemble-1x2](https://huggingface.co/gnosisx/epl-ensemble-1x2)
    """)

    with gr.Tab("Single Match"):
        with gr.Row():
            home_input = gr.Textbox(label="Home Team", value="Liverpool")
            away_input = gr.Textbox(label="Away Team", value="Everton")

        with gr.Row():
            home_odds_input = gr.Number(label="Home Odds", value=1.48)
            draw_odds_input = gr.Number(label="Draw Odds", value=5.0)
            away_odds_input = gr.Number(label="Away Odds", value=8.0)

        predict_btn = gr.Button("Predict", variant="primary")
        output = gr.Markdown()

        predict_btn.click(
            predict_match,
            inputs=[home_input, away_input, home_odds_input, draw_odds_input, away_odds_input],
            outputs=output
        )

    with gr.Tab("Batch Prediction"):
        gr.Markdown("Upload CSV with format: `Home,Away,HomeOdds,DrawOdds,AwayOdds`")

        csv_input = gr.Textbox(
            label="CSV Data",
            lines=10,
            value="Home,Away,H_Odds,D_Odds,A_Odds\nLiverpool,Everton,1.48,5.0,8.0\nArsenal,Chelsea,2.1,3.5,3.8\nMan City,Burnley,1.15,9.0,21.0"
        )

        batch_btn = gr.Button("Predict All", variant="primary")
        batch_output = gr.Markdown()

        batch_btn.click(
            batch_predict,
            inputs=csv_input,
            outputs=batch_output
        )

    with gr.Tab("API"):
        gr.Markdown("""
        ## API Endpoint

        You can also use this as an API:

        ```python
        import requests

        response = requests.post(
            "https://gnosisx-epl-predictor.hf.space/api/predict",
            json={
                "home_team": "Liverpool",
                "away_team": "Everton",
                "best_odds": {
                    "H": {"odds": 1.48},
                    "D": {"odds": 5.0},
                    "A": {"odds": 8.0}
                }
            }
        )
        print(response.json())
        ```
        """)

# Launch app
if __name__ == "__main__":
    app.launch()