Adding the test file

app.py

@@ -26,7 +26,9 @@ 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")
+        ref_df = pd.read_csv(f"{player_id}.csv", encoding='utf-8')
+        print("Reference data columns:", ref_df.columns)
+        print("Reference data values:", ref_df.iloc[-1])
         return ref_df.iloc[-1][f"{player_id}_S34"]
     except Exception as e:
         print(f"Could not load reference data: {e}")
@@ -66,7 +68,7 @@ def prepare_single_player_data(user_id, mmr, comf_1, comf_2, comf_3, comf_4, com
         
         # Get statistics from league data
         try:
-            captains_df = pd.read_csv("S34 Draft Sheet  Captains.csv")
+            captains_df = pd.read_csv("S34 Draft Sheet - Captains.csv", encoding='utf-8')
             bucks_stats = captains_df["Buck's Bucks"].describe()
             cents_stats = captains_df["Crub Cents"].describe()
             
@@ -145,7 +147,9 @@ def predict_cost(user_id, mmr, comf_1, comf_2, comf_3, comf_4, comf_5):
         # Make prediction
         input_name = session.get_inputs()[0].name
         prediction = session.run(None, {input_name: processed_data.values.astype(np.float32)})[0]
+        print("\nPrediction output:", prediction)
         predicted_cost = round(float(prediction[0]), 2)
+        print("Predicted cost:", predicted_cost)
         
         hero_stats = processed_data.iloc[0]
         total_games = hero_stats.get('total_games_played', 'N/A')