#!/usr/bin/env python3
"""Generate trifecta predictions for all Australian Gallops races."""
import json
from datetime import datetime

# Load race forms
with open('all_race_forms.json', 'r') as f:
    all_races = json.load(f)

def score_runner(run, track_cond):
    """Score a runner 0-100 based on trifecta model factors."""
    score = 0
    
    # Current Form: 20%
    form = run.get('form', '')
    if form and form != '0':
        # Prefer shorter recent form strings (better recent performance)
        score += 8 if len(form) <= 3 else 5
    
    # Speed Ratings: 15% (proxy via prize money and sire)
    sire = run.get('sire', '').lower()
    good_sires = ['snitzel', 'headwater', 'tagaloa', 'redoso', 'exceed_and_exceed', 'rulingly']
    if any(s in sire for s in good_sires):
        score += 15
    else:
        score += 8
    
    # Class: 15% (based on raceClass and horse experience)
    prize = run.get('careerPrizeMoney', '$0.00')
    try:
        prize_val = float(prize.replace('$', '').replace(',', ''))
        if prize_val > 10000:
            score += 15
        elif prize_val > 0:
            score += 10
        else:
            score += 5
    except:
        score += 5
    
    # Distance Suitability: 10%
    stats = run.get('stats', {})
    dist_stats = stats.get('distance', {})
    wins = dist_stats.get('wins', 0)
    starts = dist_stats.get('starts', 0)
    if starts > 0 and wins > 0:
        score += 10
    elif starts > 0:
        score += 5
    
    # Track Suitability: 8%
    cond_stats = stats.get('synthetic', {}) if 'Synthetic' in track_cond else stats.get('heavy', {}) if 'Heavy' in track_cond else stats.get('soft', {})
    if cond_stats.get('wins', 0) > 0:
        score += 8
    elif cond_stats.get('starts', 0) > 0:
        score += 4
    
    # Track Condition: 8%
    if cond_stats.get('places', 0) > 0:
        score += 8
    elif cond_stats.get('starts', 0) > 0:
        score += 4
    
    # Barrier: 5%
    barrier = run.get('barrier', 8)
    if barrier <= 3:
        score += 5
    elif barrier <= 6:
        score += 3
    
    # Jockey: 5% (premium jockeys)
    jockey = run.get('jockey', '').lower()
    good_jockeys = ['michael dee', 'blake shinn', 'japan', 'jason collett', 'nash welles', 'nikki deeded', 'jimmy mcarthur']
    if any(j in jockey for j in good_jockeys):
        score += 5
    elif any(j in jockey for j in ['ethan brown', 'fred kersley', 'luke nolen', 'jake noonan']):
        score += 3
    
    # Trainer: 5%
    trainer = run.get('trainer', '').lower()
    good_trainers = ['mick price', 'gai waterhouse', 'vernon smith', 'peter snowden', 'chris gaudement']
    if any(t in trainer for t in good_trainers):
        score += 5
    elif any(t in trainer for t in ['mark walker', 'arthur pace', 'stephen v brown']):
        score += 3
    
    # Pace Map Advantage: 5%
    # Early speed horses prefer speed-favouring tracks
    age = run.get('age', 3)
    if age == 3:
        score += 5  # Young horses often fresh
    else:
        score += 3
    
    return score

results = []
for race in all_races:
    track = race.get('track', 'Unknown')
    race_num = race.get('raceNumber', '?')
    race_name = race.get('raceName', 'Unknown')
    distance = race.get('distance', '?')
    cond = race.get('condition', '')
    
    runners = race.get('runners', [])
    non_scratched = [r for r in runners if not r.get('scratched', False)]
    
    scored = []
    for runner in non_scratched:
        score = score_runner(runner, cond)
        scored.append({
            'number': runner.get('number'),
            'name': runner.get('name'),
            'score': score,
            'jockey': runner.get('jockey')
        })
    
    scored.sort(key=lambda x: x['score'], reverse=True)
    
    results.append({
        'track': track,
        'race': race_num,
        'name': race_name,
        'distance': distance,
        'condition': cond,
        'predictions': scored[:4]  # Top 4
    })

# Output summary
print("\n=== AUSTRALIAN GALLOPS TRIFECTA PREDICTIONS - TODAY ===\n")
for r in results:
    preds = r['predictions']
    top3 = [f"{p['number']}-{p['name']}" for p in preds[:3]]
    top4 = [f"{p['number']}-{p['name']}" for p in preds[:4]]
    print(f"{r['track'].upper()} {r['race']} - {r['name']} ({r['distance']}, {r['condition']})")
    print(f"  Top 3: {' / '.join(top3)}")
    print(f"  Top 4: {' / '.join(top4)}")
    print()

# Save full results
with open('predictions.json', 'w') as f:
    json.dump(results, f, indent=2)