Update app.py

app.py

@@ -5,22 +5,16 @@ import joblib
 import pickle
 
 # --- 1. LOAD MODEL & DATA ---
-# Pastikan file-file .pkl ini ada di folder yang sama saat di-upload ke Hugging Face
-try:
-    model = joblib.load('xgboost_valorant_model.pkl')
-    with open('player_knowledge_base.pkl', 'rb') as f:
-        knowledge_base = pickle.load(f)
-    with open('training_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 = []
+model = joblib.load('xgboost_valorant_model.pkl')
+
+with open('player_knowledge_base.pkl', 'rb') as f:
+    knowledge_base = pickle.load(f)
+
+with open('training_columns.pkl', 'rb') as f:
+    training_columns = pickle.load(f)
 
 # Daftar Map & Agent (Sesuaikan dengan data Anda)
-MAP_LIST = ['Ascent', 'Bind', 'Breeze', 'Fracture', 'Haven', 'Icebox', 'Lotus', 'Pearl', 'Split', 'Sunset', 'Abyss']
+MAP_LIST = ['Ascent', 'Bind', 'Breeze', 'Fracture', 'Haven', 'Icebox', 'Lotus', 'Pearl', 'Split', 'Sunset', 'Abyss', 'Corrode']
 AGENT_LIST = ['Jett', 'Raze', 'Reyna', 'Phoenix', 'Yoru', 'Neon', 'Iso', # Duelist
               'Sova', 'Fade', 'Breach', 'Skye', 'Kayo', 'Gekko',       # Initiator
               'Omen', 'Brimstone', 'Viper', 'Astra', 'Harbor', 'Clove', 'Waylay', # Controller
@@ -39,26 +33,26 @@ def predict_match(map_name, t1_side,
     
     # 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
+        # Jika player/agent ada di database, ambil statsnya
+        # Jika tidak (player baru), pakai nilai default (WR=0.5, Exp=0)
+        key = (player, agent)
         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
+    # Team 1
     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
         
+        # Isi ke DataFrame (Cek apakah kolom ada di training_columns agar tidak error)
         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:
@@ -68,7 +62,7 @@ def predict_match(map_name, t1_side,
             
         t1_general_wrs.append(stats['General_WR'])
 
-    # Team 2 Inputs
+    # Team 2 (Lakukan hal sama)
     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)
@@ -78,14 +72,12 @@ def predict_match(map_name, t1_side,
     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
@@ -94,98 +86,46 @@ def predict_match(map_name, t1_side,
         t2_avg = np.mean(t2_general_wrs)
         input_data.at[0, 'WR_Diff'] = t1_avg - t2_avg
 
-    # E. One-Hot Encoding Manual
+    # E. One-Hot Encoding Manual (Set Nilai 1)
     # Map
     map_col = f'MAP_{map_name}'
     if map_col in training_columns:
         input_data.at[0, map_col] = 1
         
-    # Start Side
+    # Start Side (Jika ada fitur ini)
     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:
+    if 'T2_StartSide_Defense' in training_columns: # Biasanya kebalikan T1
         input_data.at[0, 'T2_StartSide_Defense'] = 1 if t1_side == 'Attack' else 0
 
-    # Agent OHE (Slot Based)
-    # T1 Agents
+
+        
+    # Karena OHE manual agak ribet mencocokkan nama kolom tepatnya, 
+    # kita gunakan cara pintar: Loop semua input agent, cari kolom yang sesuai di training_columns
+    
+    # Set 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
+    # Set 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 ---
+    # Convert ke float agar aman
     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)}"
+    prob = model.predict_proba(input_data)[0]
+    win_prob_t1 = prob[1] # Probabilitas kelas 1 (T1 Win)
+    
+    winner = "TEAM 1" if win_prob_t1 > 0.5 else "TEAM 2"
+    confidence = win_prob_t1 if win_prob_t1 > 0.5 else 1 - win_prob_t1
+    
+    return f"{winner} WIN ({confidence:.1%} Confidence)"
 
 # --- 3. UI GRADIO ---
 with gr.Blocks(title="Valorant Match Predictor") as demo:
@@ -201,28 +141,36 @@ with gr.Blocks(title="Valorant Match Predictor") as demo:
         # 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_n = gr.Textbox(label="P1 Name")
             t1_p1_a = gr.Dropdown(AGENT_LIST, label="P1 Agent")
-            t1_p2_n = gr.Textbox(label="P2 Name", placeholder="e.g. mindfreak")
+            
+            t1_p2_n = gr.Textbox(label="P2 Name")
             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_n = gr.Textbox(label="P1 Name")
             t2_p1_a = gr.Dropdown(AGENT_LIST, label="P1 Agent")
-            t2_p2_n = gr.Textbox(label="P2 Name", placeholder="e.g. Zekken")
+            
+            t2_p2_n = gr.Textbox(label="P2 Name")
             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")