Evaluation to current

app.py

@@ -23,15 +23,21 @@ games_ids = list(range(1, 24)) + [25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36
 EXPECTED_COLUMNS.extend([f'games_{i}' for i in games_ids])
 EXPECTED_COLUMNS.extend([f'winrate_{i}' for i in games_ids])
 
+def load_reference_data(player_id):
+    """Load reference prediction data for comparison"""
+    try:
+        ref_df = pd.read_csv(f"{player_id}.csv")
+        return ref_df.iloc[-1][f"{player_id}_S34"]
+    except Exception as e:
+        print(f"Could not load reference data: {e}")
+        return None
+
 def prepare_single_player_data(user_id, mmr, comf_1, comf_2, comf_3, comf_4, comf_5):
     """Creates a DataFrame in the expected format for the model"""
     try:
         # Extract player_id from URL if needed
         player_id = user_id.split("/")[-1] if "/" in user_id else user_id
         
-        # Get hero statistics using OpenDota API
-        hero_stats = hero_information(player_id)
-        
         # Create initial data dictionary with zeros for all columns
         data = {col: 0 for col in EXPECTED_COLUMNS}
         
@@ -45,6 +51,9 @@ def prepare_single_player_data(user_id, mmr, comf_1, comf_2, comf_3, comf_4, com
             'p5': int(comf_5),
         })
         
+        # Get hero statistics using OpenDota API
+        hero_stats = hero_information(player_id)
+        
         # Add hero statistics
         if hero_stats is not None:
             data['total_games_played'] = hero_stats.get('total_games_played', 0)
@@ -61,6 +70,11 @@ def prepare_single_player_data(user_id, mmr, comf_1, comf_2, comf_3, comf_4, com
             bucks_stats = captains_df["Buck's Bucks"].describe()
             cents_stats = captains_df["Crub Cents"].describe()
             
+            # Print stats for debugging
+            print("\nLeague Money Statistics:")
+            print("Buck's Bucks stats:", bucks_stats)
+            print("Crub Cents stats:", cents_stats)
+            
             # Combine stats from both currencies
             combined_stats = {
                 'count': bucks_stats['count'] + cents_stats['count'],
@@ -70,10 +84,10 @@ def prepare_single_player_data(user_id, mmr, comf_1, comf_2, comf_3, comf_4, com
                 'max': max(bucks_stats['max'], cents_stats['max']),
                 'sum': bucks_stats['count'] * bucks_stats['mean'] + cents_stats['count'] * cents_stats['mean']
             }
+            print("Combined stats:", combined_stats)
             data.update(combined_stats)
         except Exception as e:
             print(f"Error reading captains data: {e}")
-            # Fallback to mock data if file read fails
             stats = {
                 'count': 1,
                 'mean': mmr / 200,
@@ -93,6 +107,14 @@ def prepare_single_player_data(user_id, mmr, comf_1, comf_2, comf_3, comf_4, com
         print(f"DataFrame shape: {df.shape}")
         print("Missing columns:", set(EXPECTED_COLUMNS) - set(df.columns))
         
+        # Print key feature values for debugging
+        print("\nKey feature values:")
+        print(f"MMR: {df['mmr'].iloc[0]}")
+        print(f"Position comfort: {df[['p1', 'p2', 'p3', 'p4', 'p5']].iloc[0].tolist()}")
+        print(f"Money stats: {df[['count', 'mean', 'std', 'min', 'max', 'sum']].iloc[0].tolist()}")
+        print(f"Total games: {df['total_games_played'].iloc[0]}")
+        print(f"Total winrate: {df['total_winrate'].iloc[0]}")
+        
         return df
         
     except Exception as e:
@@ -102,6 +124,10 @@ def prepare_single_player_data(user_id, mmr, comf_1, comf_2, comf_3, comf_4, com
 def predict_cost(user_id, mmr, comf_1, comf_2, comf_3, comf_4, comf_5):
     """Main prediction function for Gradio interface"""
     try:
+        # Extract player_id for reference data
+        player_id = user_id.split("/")[-1] if "/" in user_id else user_id
+        reference_cost = load_reference_data(player_id)
+        
         # Prepare the player data
         processed_data = prepare_single_player_data(user_id, mmr, comf_1, comf_2, comf_3, comf_4, comf_5)
         
@@ -113,8 +139,8 @@ def predict_cost(user_id, mmr, comf_1, comf_2, comf_3, comf_4, comf_5):
         session = ort.InferenceSession(str(model_path))
         
         # Debug information
+        print("\nModel Input Information:")
         print("Processed data shape:", processed_data.shape)
-        print("Processed data columns:", processed_data.columns.tolist())
         
         # Make prediction
         input_name = session.get_inputs()[0].name
@@ -125,7 +151,14 @@ def predict_cost(user_id, mmr, comf_1, comf_2, comf_3, comf_4, comf_5):
         total_games = hero_stats.get('total_games_played', 'N/A')
         total_winrate = hero_stats.get('total_winrate', 'N/A')
         
-        return f"""Predicted Cost: {predicted_cost}
+        comparison = ""
+        if reference_cost is not None:
+            diff = abs(predicted_cost - reference_cost)
+            comparison = f"""
+Reference Cost: {reference_cost}
+Difference: {diff:.2f} ({(diff/reference_cost*100):.1f}% {'higher' if predicted_cost > reference_cost else 'lower'})"""
+        
+        return f"""Predicted Cost: {predicted_cost}{comparison}
 
 Player Details:
 - MMR: {mmr}