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| 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 training_data_prep import list_format, modification, league_money, df_gen | |
| from feature_engineering import heroes, hero_information | |
| def prepare_single_player_data(user_id, mmr, comf_1, comf_2, comf_3, comf_4, comf_5): | |
| """Creates a draft sheet format DataFrame for a single player""" | |
| # Create player data in the format expected by the original pipeline | |
| player_data = { | |
| 'Discord ID': ['N/A'], # Placeholder for Discord ID | |
| 'Dotabuff Link': [user_id], | |
| 'MMR': [mmr], | |
| 'Comfort (Pos 1)': [comf_1], | |
| 'Comfort (Pos 2)': [comf_2], | |
| 'Comfort (Pos 3)': [comf_3], | |
| 'Comfort (Pos 4)': [comf_4], | |
| 'Comfort (Pos 5)': [comf_5], | |
| 'Player statement': ['N/A'] # Placeholder for player statement | |
| } | |
| return pd.DataFrame(player_data) | |
| def prepare_mock_captains(): | |
| """Creates a minimal captains DataFrame for the league money calculation""" | |
| captains_data = { | |
| 'Name': ['Mock Captain'], | |
| 'Dotabuff': ['N/A'], | |
| 'MMR': [3000], | |
| "Buck's Bucks": [100], | |
| 'Crub Cents': [100], | |
| 'Remaining': [100] | |
| } | |
| return pd.DataFrame(captains_data) | |
| def predict_cost(user_id, mmr, comf_1, comf_2, comf_3, comf_4, comf_5): | |
| """Main prediction function for Gradio interface""" | |
| try: | |
| # Create a single-player draft sheet | |
| draft_df = prepare_single_player_data(user_id, mmr, comf_1, comf_2, comf_3, comf_4, comf_5) | |
| captains_df = prepare_mock_captains() | |
| # Use the original pipeline functions | |
| money = league_money(captains_df, 'prediction') | |
| prepped_data = df_gen(draft_df, money, 'prediction') | |
| # Load and use the model | |
| model_path = Path("model/rd2l_forest.onnx") | |
| session = ort.InferenceSession(str(model_path)) | |
| # Get hero information using the original feature engineering | |
| try: | |
| player_id = modification(user_id) # Use the original modification function | |
| hero_stats = hero_information(player_id) | |
| # Add hero stats to prepped data | |
| for col, value in hero_stats.items(): | |
| prepped_data[col] = value | |
| except Exception as e: | |
| print(f"Warning - Error fetching hero data: {str(e)}") | |
| # Make prediction | |
| input_name = session.get_inputs()[0].name | |
| prediction = session.run(None, {input_name: prepped_data.values.astype(np.float32)})[0] | |
| predicted_cost = round(float(prediction[0]), 2) | |
| return f"""Predicted Cost: {predicted_cost} | |
| 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} | |
| 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() | |