Bismillah

app.py

@@ -0,0 +1,199 @@
+import gradio as gr
+import pandas as pd
+import numpy as np
+import joblib
+import pickle
+
+# --- 1. LOAD MODEL & DATA ---
+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']
+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
+              'Sage', 'Cypher', 'Killjoy', 'Chamber', 'Deadlock', 'Vyse', 'Tejo', '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):
+        # 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
+    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:
+            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 (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)
+    ]
+    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 (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 (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: # Biasanya kebalikan T1
+        input_data.at[0, 'T2_StartSide_Defense'] = 1 if t1_side == 'Attack' else 0
+
+    # Agent OHE
+    # T1 Agents
+    for _, agent in t1_inputs:
+        # Jika pakai logika "T1_P1_Agent_Jett" (Slot Based)
+        # Atau "T1_Has_Jett" (Aggregated Based) - Sesuaikan dengan model terakhir Anda!
+        
+        # Asumsi: Masih pakai Slot Based (sesuai kode terakhir di chat)
+        # Kita loop kolom training untuk mencocokkan
+        pass 
+        
+    # 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
+            
+    # 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
+
+    # --- 3. PREDIKSI ---
+    # Convert ke float agar aman
+    input_data = input_data.astype(float)
+    
+    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:
+    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()

player_knowledge_base.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ce5ba5393c4c113c8bb0db130ed399bb13ae35fd0b6bcd1016f0b6e0d4b84c65
+size 2173681

training_columns.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:56c542d7d9ff130f501a64e53f9f6297ea8a296bfe93f96776739518d5b19fdf
+size 6120

xgboost_valorant_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b0bb94f3bb8e18c6f9e31ddd398863cc02775c2b4152ff9b5590f436e287facc
+size 6088068