Spaces:
Sleeping
Sleeping
| import gradio as gr | |
| import pandas as pd | |
| import numpy as np | |
| import onnxruntime as ort | |
| import sys | |
| from pathlib import Path | |
| sys.path.append("rd2l_pred") | |
| from feature_engineering import heroes, hero_information | |
| # Define expected columns | |
| EXPECTED_COLUMNS = ['mmr', 'p1', 'p2', 'p3', 'p4', 'p5', 'count', 'mean', 'std', 'min', 'max', 'sum', | |
| 'total_games_played', 'total_winrate'] | |
| # Add games columns | |
| games_ids = list(range(1, 24)) + [25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, | |
| 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, | |
| 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, | |
| 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, | |
| 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, | |
| 107, 108, 109, 110, 111, 112, 113, 114, 119, 120, 121, 123, 126, | |
| 128, 129, 131, 135, 136, 137, 138, 145] | |
| 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 and input features""" | |
| try: | |
| # Read the full CSV to get both input features and prediction | |
| ref_df = pd.read_csv(f"{player_id}.csv", encoding='utf-8', index_col=0) | |
| # Remove the prediction row and convert to a proper format | |
| features = ref_df[ref_df.index != 'Predicted_Cost'].iloc[:, 0] | |
| prediction = ref_df.loc['Predicted_Cost', f"{player_id}_S34"] | |
| print("\nReference data loaded:") | |
| for idx in ['mmr', 'p1', 'p2', 'p3', 'p4', 'p5', 'count', 'mean', 'std', 'min', 'max', 'sum']: | |
| if idx in features.index: | |
| print(f"{idx}: {features[idx]}") | |
| return features, float(prediction) | |
| except Exception as e: | |
| print(f"Could not load reference data: {e}") | |
| return None, 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 | |
| # Create initial data dictionary with zeros for all columns | |
| data = {col: 0 for col in EXPECTED_COLUMNS} | |
| # Fill in the basic features | |
| data.update({ | |
| 'mmr': float(mmr), | |
| 'p1': int(comf_1), | |
| 'p2': int(comf_2), | |
| 'p3': int(comf_3), | |
| 'p4': int(comf_4), | |
| '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) | |
| data['total_winrate'] = hero_stats.get('total_winrate', 0) | |
| # Fill in the games and winrate columns from hero_stats | |
| for key, value in hero_stats.items(): | |
| if key in EXPECTED_COLUMNS: | |
| data[key] = value | |
| # Get statistics from league data | |
| try: | |
| 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() | |
| # 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'], | |
| 'count': 9.0, | |
| # 'mean': (bucks_stats['mean'] + cents_stats['mean']) / 2, | |
| 'mean': 489.3333333333333, | |
| # 'std': (bucks_stats['std'] + cents_stats['std']) / 2, | |
| 'std': 77.4483698989204, | |
| # 'min': min(bucks_stats['min'], cents_stats['min']), | |
| 'min': 352.0, | |
| # 'max': max(bucks_stats['max'], cents_stats['max']), | |
| 'max': 593.0, | |
| # 'sum': bucks_stats['count'] * bucks_stats['mean'] + cents_stats['count'] * cents_stats['mean'] | |
| 'sum': 4404.0 | |
| } | |
| print("Combined stats:", combined_stats) | |
| data.update(combined_stats) | |
| except Exception as e: | |
| print(f"Error reading captains data: {e}") | |
| stats = { | |
| 'count': 1, | |
| 'mean': mmr / 200, | |
| 'std': mmr / 400, | |
| 'min': mmr / 250, | |
| 'max': mmr / 150, | |
| 'sum': mmr / 200 | |
| } | |
| data.update(stats) | |
| # Convert to DataFrame | |
| df = pd.DataFrame([data]) | |
| # Ensure columns are in correct order | |
| df = df[EXPECTED_COLUMNS] | |
| 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: | |
| print(f"Error in data preparation: {e}") | |
| raise e | |
| 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_features, reference_prediction = 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) | |
| print("\nComparing processed data with reference:") | |
| if reference_features is not None: | |
| our_data = processed_data.iloc[0] | |
| for idx in ['mmr', 'p1', 'p2', 'p3', 'p4', 'p5', 'count', 'mean', 'std', 'min', 'max', 'sum']: | |
| our_val = our_data[idx] | |
| ref_val = reference_features[idx] if idx in reference_features.index else "N/A" | |
| print(f"{idx}:") | |
| print(f" Our value: {our_val}") | |
| print(f" Ref value: {ref_val}") | |
| if our_val != ref_val and ref_val != "N/A": | |
| print(f" *** MISMATCH ***") | |
| # Load and use the model | |
| model_path = Path("model/rd2l_forest.onnx") | |
| if not model_path.exists(): | |
| return f"Model file not found at: {model_path}" | |
| session = ort.InferenceSession(str(model_path)) | |
| # 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') | |
| total_winrate = hero_stats.get('total_winrate', 'N/A') | |
| comparison = "" | |
| if reference_prediction is not None: | |
| diff = abs(predicted_cost - reference_prediction) | |
| comparison = f""" | |
| Reference Cost: {reference_prediction} | |
| Difference: {diff:.2f} ({(diff/reference_prediction*100):.1f}% {'higher' if predicted_cost > reference_prediction else 'lower'})""" | |
| return f"""Predicted Cost: {predicted_cost}""" | |
| # return f"""Predicted Cost: {predicted_cost}{comparison} | |
| # | |
| # Player Details: | |
| # - MMR: {mmr} | |
| # - Position Comfort: | |
| # * Pos 1: {comf_1} | |
| # * Pos 2: {comf_2} | |
| # * Pos 3: {comf_3} | |
| # * Pos 4: {comf_4} | |
| # * Pos 5: {comf_5} | |
| # | |
| # Player Statistics: | |
| # - Total Games: {total_games} | |
| # - Overall Winrate: {total_winrate:.1%} if isinstance(total_winrate, float) else 'N/A' | |
| # | |
| # Note: This prediction is based on historical data and player statistics from OpenDota.""" | |
| # | |
| except Exception as e: | |
| return f"Error in prediction pipeline: {str(e)}\n\nDebug info:\n{type(e).__name__}: {str(e)}" | |
| # Create Gradio interface | |
| demo = gr.Interface( | |
| fn=predict_cost, | |
| inputs=[ | |
| gr.Textbox(label="Player ID or Link to OpenDota/Dotabuff", | |
| placeholder="Enter player ID or full profile URL"), | |
| gr.Number(label="MMR", value=3000), | |
| gr.Slider(1, 5, value=3, step=1, label="Comfort (Pos 1)"), | |
| gr.Slider(1, 5, value=3, step=1, label="Comfort (Pos 2)"), | |
| gr.Slider(1, 5, value=3, step=1, label="Comfort (Pos 3)"), | |
| gr.Slider(1, 5, value=3, step=1, label="Comfort (Pos 4)"), | |
| gr.Slider(1, 5, value=3, step=1, label="Comfort (Pos 5)") | |
| ], | |
| examples=[ | |
| ["https://www.dotabuff.com/players/188649776", 6812, 5, 5, 4, 2, 1] | |
| ], | |
| outputs=gr.Textbox(label="Prediction Results"), | |
| title="RD2L Player Cost Predictor", | |
| description="""This tool predicts the auction cost for RD2L players based on their MMR, | |
| position comfort levels, and historical performance data from OpenDota. | |
| Enter a player's OpenDota ID or profile URL along with their current stats | |
| to get a predicted cost.""", | |
| article="""### How it works | |
| - The predictor uses machine learning trained on historical RD2L draft data | |
| - Player statistics are fetched from OpenDota API | |
| - Position comfort levels range from 1 (least comfortable) to 5 (most comfortable) | |
| - Predictions are based on both current stats and historical performance | |
| ### Notes | |
| - MMR should be the player's current solo MMR | |
| - Position comfort should reflect actual role experience | |
| - Predictions are estimates and may vary from actual draft results""" | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |