updated the location and hopefully now grabbing all info:

app.py

@@ -22,13 +22,14 @@ def load_model():
         
         MODEL = ort.InferenceSession(str(model_path))
         
-        # Load feature columns from a saved reference - you'll need to create this
+        # Load feature columns from prediction data
         try:
-            FEATURE_COLUMNS = pd.read_csv("model/feature_columns.csv")["columns"].tolist()
+            FEATURE_COLUMNS = pd.read_csv("result_prediction_data_prepped.csv").columns.tolist()
         except:
-            # Fallback to basic features if feature columns file not found
-            FEATURE_COLUMNS = ["player_id", "mmr", "p1", "p2", "p3", "p4", "p5", 
-                             "total_games_played", "total_winrate"]
+            try:
+                FEATURE_COLUMNS = pd.read_csv("prediction_data_prepped.csv").columns.tolist()
+            except:
+                return "Error: Could not find prediction data files to determine feature structure"
         
         return "Model loaded successfully"
     except Exception as e:
@@ -52,11 +53,28 @@ def process_player_data(player_id, mmr, comf_1, comf_2, comf_3, comf_4, comf_5):
             "p5": int(comf_5)
         }
         
+        # Read the example row from prediction_data_prepped.csv to get the expected structure
+        try:
+            pred_data = pd.read_csv("prediction_data_prepped.csv")
+            if not pred_data.empty:
+                # Get column structure from the first row
+                for col in pred_data.columns:
+                    if col not in player_data:
+                        player_data[col] = 0
+        except Exception as e:
+            print(f"Warning - Error reading prediction data template: {str(e)}")
+        
         # Get hero statistics using OpenDota API
         try:
             hero_stats = hero_information(player_id)
-            # Merge hero stats with player data
             player_data.update(hero_stats.to_dict())
+            
+            # Add season identifier to match training data format
+            player_season = f"{player_id}_S34"  # Assuming current season is 34
+            temp_dict = {}
+            temp_dict[player_season] = 1.0  # Set current season flag to 1.0
+            player_data.update(temp_dict)
+            
         except Exception as e:
             print(f"Warning - Error fetching hero data: {str(e)}")
             # If hero stats fail, add placeholder values
@@ -68,7 +86,20 @@ def process_player_data(player_id, mmr, comf_1, comf_2, comf_3, comf_4, comf_5):
         # Convert to DataFrame for consistency with training
         df = pd.DataFrame([player_data])
         
-        # Ensure all required columns exist
+        # Load reference data structure if available
+        try:
+            ref_data = pd.read_csv("result_prediction_data_prepped.csv")
+            if not ref_data.empty:
+                # Get all columns from reference data
+                for col in ref_data.columns:
+                    if col not in df.columns:
+                        df[col] = 0
+                # Reorder columns to match reference data
+                df = df[ref_data.columns]
+        except Exception as e:
+            print(f"Warning - Error matching reference data structure: {str(e)}")
+            
+        # Final check for required columns
         if FEATURE_COLUMNS:
             for col in FEATURE_COLUMNS:
                 if col not in df.columns:
@@ -76,6 +107,9 @@ def process_player_data(player_id, mmr, comf_1, comf_2, comf_3, comf_4, comf_5):
             # Reorder columns to match model input
             df = df[FEATURE_COLUMNS]
         
+        print(f"Processed data columns: {df.columns.tolist()}")
+        print(f"Number of features: {len(df.columns)}")
+        
         return df
     except Exception as e:
         return f"Error processing player data: {str(e)}"