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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
# Load the ONNX model
model_path = Path("model/rd2l_predictor.onnx")
session = ort.InferenceSession(model_path)
def process_player_data(player_id, mmr, comf_1, comf_2, comf_3, comf_4, comf_5):
"""Process player data similar to training pipeline"""
try:
# Clean player ID from URL if needed
if "/" in player_id:
player_id = player_id.split("/")[-1]
# Create initial player series
player_data = {
"player_id": player_id,
"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
try:
hero_stats = hero_information(player_id)
# Merge hero stats with player data
player_data.update(hero_stats.to_dict())
except Exception as e:
return f"Error fetching hero data: {str(e)}"
# Convert to DataFrame for consistency with training
df = pd.DataFrame([player_data])
# Ensure columns match training data
required_columns = [col.name for col in session.get_inputs()[0].type.tensor_type.shape.dim]
for col in required_columns:
if col not in df.columns:
df[col] = 0
# Reorder columns to match model input
df = df[required_columns]
return df
except Exception as e:
return f"Error processing player data: {str(e)}"
def predict_cost(user_id, mmr, comf_1, comf_2, comf_3, comf_4, comf_5):
"""Main prediction function for Gradio interface"""
try:
# Process input data
processed_data = process_player_data(user_id, mmr, comf_1, comf_2, comf_3, comf_4, comf_5)
if isinstance(processed_data, str): # Error occurred
return processed_data
# Make prediction
input_name = session.get_inputs()[0].name
prediction = session.run(None, {input_name: processed_data.values.astype(np.float32)})[0]
# Format prediction
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 making prediction: {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)")
],
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()
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