import gradio as gr
import pandas as pd
import numpy as np
import joblib
import pickle

try:
    model = joblib.load('xgboost_valorant_model.pkl')
    
    # Sesuaikan nama file dengan output training
    with open('player_stats.pkl', 'rb') as f: 
        knowledge_base = pickle.load(f)
        
    with open('model_columns.pkl', 'rb') as f:
        training_columns = pickle.load(f)
        
    print("✅ Model dan Data berhasil dimuat.")
except Exception as e:
    print(f"❌ Error loading files: {e}")
    # Dummy data untuk mencegah crash saat development lokal tanpa file
    knowledge_base = {}
    training_columns = []

# Daftar Map & Agent (Sesuaikan dengan data Anda)
MAP_LIST = ['Ascent', 'Bind', 'Breeze', 'Fracture', 'Haven', 'Icebox', 'Lotus', 'Pearl', 'Split', 'Sunset', 'Abyss', 'Corrode']
AGENT_LIST = [
    'Jett', 'Raze', 'Reyna', 'Phoenix', 'Yoru', 'Neon', 'Iso', 'Waylay', # Duelist
    'Sova', 'Fade', 'Breach', 'Skye', 'Kayo', 'Gekko', 'Tejo',           # Initiator
    'Omen', 'Brimstone', 'Viper', 'Astra', 'Harbor', 'Clove',            # Controller
    'Sage', 'Cypher', 'Killjoy', 'Chamber', 'Deadlock', 'Vyse', 'Veto'   # Sentinel
]

# --- 2. FUNGSI PREDIKSI ---
def predict_match(map_name, t1_side, 
                  t1_p1_name, t1_p1_agent, t1_p2_name, t1_p2_agent, 
                  t1_p3_name, t1_p3_agent, t1_p4_name, t1_p4_agent, t1_p5_name, t1_p5_agent,
                  t2_p1_name, t2_p1_agent, t2_p2_name, t2_p2_agent, 
                  t2_p3_name, t2_p3_agent, t2_p4_name, t2_p4_agent, t2_p5_name, t2_p5_agent):
    
    # A. Buat DataFrame Input Kosong
    input_data = pd.DataFrame(columns=training_columns)
    input_data.loc[0] = 0 # Isi baris pertama dengan 0 semua
    
    # B. Helper: Ambil Stats dari Knowledge Base
    def get_stats(player, agent):
        # Normalisasi input nama player (strip whitespace)
        player_clean = player.strip() if player else ""
        key = (player_clean, agent)
        
        # Ambil stats, default 0.5 jika tidak ada
        stats = knowledge_base.get(key, {'Agent_WR': 0.5, 'General_WR': 0.5, 'Exp': 0})
        return stats

    # C. Isi Fitur Winrate (Skenario 4 Logic)
    # Team 1 Inputs
    t1_inputs = [
        (t1_p1_name, t1_p1_agent), (t1_p2_name, t1_p2_agent), (t1_p3_name, t1_p3_agent),
        (t1_p4_name, t1_p4_agent), (t1_p5_name, t1_p5_agent)
    ]
    t1_general_wrs = []
    
    for i, (p_name, p_agent) in enumerate(t1_inputs):
        stats = get_stats(p_name, p_agent)
        idx = i + 1
        
        if f'T1_P{idx}_Agent_WR' in training_columns:
            input_data.at[0, f'T1_P{idx}_Agent_WR'] = stats['Agent_WR']
        if f'T1_P{idx}_General_WR' in training_columns:
            input_data.at[0, f'T1_P{idx}_General_WR'] = stats['General_WR']
        if f'T1_P{idx}_Agent_Exp' in training_columns:
            input_data.at[0, f'T1_P{idx}_Agent_Exp'] = stats['Exp']
            
        t1_general_wrs.append(stats['General_WR'])

    # Team 2 Inputs
    t2_inputs = [
        (t2_p1_name, t2_p1_agent), (t2_p2_name, t2_p2_agent), (t2_p3_name, t2_p3_agent),
        (t2_p4_name, t2_p4_agent), (t2_p5_name, t2_p5_agent)
    ]
    t2_general_wrs = []
    
    for i, (p_name, p_agent) in enumerate(t2_inputs):
        stats = get_stats(p_name, p_agent)
        idx = i + 1
        
        if f'T2_P{idx}_Agent_WR' in training_columns:
            input_data.at[0, f'T2_P{idx}_Agent_WR'] = stats['Agent_WR']
        if f'T2_P{idx}_General_WR' in training_columns:
            input_data.at[0, f'T2_P{idx}_General_WR'] = stats['General_WR']
        if f'T2_P{idx}_Agent_Exp' in training_columns:
            input_data.at[0, f'T2_P{idx}_Agent_Exp'] = stats['Exp']
            
        t2_general_wrs.append(stats['General_WR'])

    # D. Hitung WR Diff
    if 'WR_Diff' in training_columns:
        t1_avg = np.mean(t1_general_wrs)
        t2_avg = np.mean(t2_general_wrs)
        input_data.at[0, 'WR_Diff'] = t1_avg - t2_avg

    # E. One-Hot Encoding Manual
    # Map
    map_col = f'MAP_{map_name}'
    if map_col in training_columns:
        input_data.at[0, map_col] = 1
        
    # Start Side
    if 'T1_StartSide_Defense' in training_columns:
        input_data.at[0, 'T1_StartSide_Defense'] = 1 if t1_side == 'Defense' else 0
    if 'T2_StartSide_Defense' in training_columns:
        input_data.at[0, 'T2_StartSide_Defense'] = 1 if t1_side == 'Attack' else 0

    # Agent OHE (Slot Based)
    # T1 Agents
    for i, (_, agent) in enumerate(t1_inputs):
        col_name = f'T1_P{i+1}_Agent_{agent}'
        if col_name in training_columns:
            input_data.at[0, col_name] = 1
            
    # T2 Agents
    for i, (_, agent) in enumerate(t2_inputs):
        col_name = f'T2_P{i+1}_Agent_{agent}'
        if col_name in training_columns:
            input_data.at[0, col_name] = 1

    # ==============================================================================
    # --- BAGIAN DEBUGGING YANG DITAMBAHKAN ---
    # Log ini akan muncul di Terminal / Logs Hugging Face, bukan di UI
    print("\n" + "="*40)
    print(f"🔍 DEBUGGING PREDICTION INPUT")
    print(f"Map: {map_name} | T1 Side: {t1_side}")
    print("-" * 40)
    
    print("🔵 TIM 1 STATS:")
    for i, (p, a) in enumerate(t1_inputs):
        # Ambil nilai yang sudah masuk ke input_data
        wr = input_data.at[0, f'T1_P{i+1}_Agent_WR']
        gen_wr = input_data.at[0, f'T1_P{i+1}_General_WR']
        exp = input_data.at[0, f'T1_P{i+1}_Agent_Exp']
        
        status = "✅ FOUND" if wr != 0.5 or gen_wr != 0.5 else "❌ NOT FOUND (Using Default)"
        print(f"   P{i+1}: {p:<15} ({a:<8}) | WR: {wr:.2f} | GenWR: {gen_wr:.2f} | Exp: {exp:<3} -> {status}")

    print("-" * 40)
    print("🔴 TIM 2 STATS:")
    for i, (p, a) in enumerate(t2_inputs):
        wr = input_data.at[0, f'T2_P{i+1}_Agent_WR']
        gen_wr = input_data.at[0, f'T2_P{i+1}_General_WR']
        exp = input_data.at[0, f'T2_P{i+1}_Agent_Exp']
        
        status = "✅ FOUND" if wr != 0.5 or gen_wr != 0.5 else "❌ NOT FOUND (Using Default)"
        print(f"   P{i+1}: {p:<15} ({a:<8}) | WR: {wr:.2f} | GenWR: {gen_wr:.2f} | Exp: {exp:<3} -> {status}")

    if 'WR_Diff' in training_columns:
        print("-" * 40)
        print(f"📊 Final WR_Diff (T1 - T2): {input_data.at[0, 'WR_Diff']:.4f}")
        if input_data.at[0, 'WR_Diff'] > 0:
            print("   -> Tim 1 Secara Statistik Lebih Unggul.")
        elif input_data.at[0, 'WR_Diff'] < 0:
            print("   -> Tim 2 Secara Statistik Lebih Unggul.")
        else:
            print("   -> Kekuatan Statistik Seimbang (Netral).")
            
    print("="*40 + "\n")
    # ==============================================================================

    # --- 3. PREDIKSI ---
    input_data = input_data.astype(float)
    
    try:
        prob = model.predict_proba(input_data)[0]
        win_prob_t1 = prob[1]
        
        if win_prob_t1 > 0.5:
            winner = "🔵 TEAM 1 WINS"
            confidence = win_prob_t1
            color = "blue"
        else:
            winner = "🔴 TEAM 2 WINS"
            confidence = 1 - win_prob_t1
            color = "red"
            
        # Format Output Debugging ke UI juga (Optional)
        debug_msg = "✅ Data found for players."
        # Cek jika banyak player not found (WR_Diff 0)
        if 'WR_Diff' in input_data and input_data.at[0, 'WR_Diff'] == 0:
             debug_msg = "⚠️ WARNING: Player stats not found (WR_Diff = 0). Check spelling!"

        return f"{winner}\nConfidence: {confidence:.1%}\n({debug_msg})"
        
    except Exception as e:
        return f"Error during prediction: {str(e)}"

# --- 3. UI GRADIO ---
with gr.Blocks(title="Valorant Match Predictor") as demo:
    gr.Markdown("# 🔮 Valorant AI Predictor (Skenario 4)")
    gr.Markdown("Prediksi pemenang berdasarkan sejarah performa player dan komposisi agent.")
    
    with gr.Row():
        with gr.Column():
            map_input = gr.Dropdown(MAP_LIST, label="Select Map")
            side_input = gr.Radio(["Attack", "Defense"], label="Team 1 Start Side", value="Attack")
    
    with gr.Row():
        # Team 1 Inputs
        with gr.Column():
            gr.Markdown("### 🔵 Team 1 Roster")
            t1_p1_n = gr.Textbox(label="P1 Name", placeholder="e.g. f0rsakeN")
            t1_p1_a = gr.Dropdown(AGENT_LIST, label="P1 Agent")
            t1_p2_n = gr.Textbox(label="P2 Name", placeholder="e.g. mindfreak")
            t1_p2_a = gr.Dropdown(AGENT_LIST, label="P2 Agent")
            t1_p3_n = gr.Textbox(label="P3 Name")
            t1_p3_a = gr.Dropdown(AGENT_LIST, label="P3 Agent")
            t1_p4_n = gr.Textbox(label="P4 Name")
            t1_p4_a = gr.Dropdown(AGENT_LIST, label="P4 Agent")
            t1_p5_n = gr.Textbox(label="P5 Name")
            t1_p5_a = gr.Dropdown(AGENT_LIST, label="P5 Agent")

        # Team 2 Inputs
        with gr.Column():
            gr.Markdown("### 🔴 Team 2 Roster")
            t2_p1_n = gr.Textbox(label="P1 Name", placeholder="e.g. Tenz")
            t2_p1_a = gr.Dropdown(AGENT_LIST, label="P1 Agent")
            t2_p2_n = gr.Textbox(label="P2 Name", placeholder="e.g. Zekken")
            t2_p2_a = gr.Dropdown(AGENT_LIST, label="P2 Agent")
            t2_p3_n = gr.Textbox(label="P3 Name")
            t2_p3_a = gr.Dropdown(AGENT_LIST, label="P3 Agent")
            t2_p4_n = gr.Textbox(label="P4 Name")
            t2_p4_a = gr.Dropdown(AGENT_LIST, label="P4 Agent")
            t2_p5_n = gr.Textbox(label="P5 Name")
            t2_p5_a = gr.Dropdown(AGENT_LIST, label="P5 Agent")

    btn = gr.Button("🚀 PREDICT WINNER", variant="primary")
    output = gr.Label(label="Prediction Result")
    
    # Connect Button
    btn.click(
        fn=predict_match,
        inputs=[
            map_input, side_input,
            t1_p1_n, t1_p1_a, t1_p2_n, t1_p2_a, t1_p3_n, t1_p3_a, t1_p4_n, t1_p4_a, t1_p5_n, t1_p5_a,
            t2_p1_n, t2_p1_a, t2_p2_n, t2_p2_a, t2_p3_n, t2_p3_a, t2_p4_n, t2_p4_a, t2_p5_n, t2_p5_a
        ],
        outputs=output
    )

demo.launch()