app.py

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

# Global variables for model and feature columns
MODEL = None

def load_model():
    """Load the ONNX model"""
    global MODEL
    try:
        model_path = Path("model/rd2l_forest.onnx")
        if not model_path.exists():
            return "Model file not found at: " + str(model_path)
        
        MODEL = ort.InferenceSession(str(model_path))
        return "Model loaded successfully"
    except Exception as e:
        return f"Error loading model: {str(e)}"

def process_player_data(player_id, mmr, comf_1, comf_2, comf_3, comf_4, comf_5):
    """Process player data with correct feature structure"""
    try:
        # Clean player ID from URL if needed
        if "/" in player_id:
            player_id = player_id.split("/")[-1]
            
        # Create initial data structure with basic features
        data = {
            'mmr': float(mmr),
            'p1': int(comf_1),
            'p2': int(comf_2),
            'p3': int(comf_3),
            'p4': int(comf_4),
            'p5': int(comf_5),
            'count': 0,
            'mean': 0,
            'std': 0,
            'min': 0,
            'max': 0,
            'sum': 0,
            'total_games_played': 0,
            'total_winrate': 0
        }
        
        # Add hero-specific features
        for i in range(1, 139):  # Add all possible hero IDs
            data[f'games_{i}'] = 0
            data[f'winrate_{i}'] = 0
            
        # Get hero statistics from OpenDota
        try:
            hero_stats = hero_information(player_id)
            data['total_games_played'] = hero_stats['total_games_played']
            data['total_winrate'] = hero_stats['total_winrate']
            
            # Update hero-specific stats
            for key, value in hero_stats.items():
                if key in data:
                    data[key] = value
        except Exception as e:
            print(f"Warning - Error fetching hero data: {str(e)}")
        
        # Convert to DataFrame
        df = pd.DataFrame([data])
        
        print(f"Processed data shape: {df.shape}")
        print(f"Number of features: {len(df.columns)}")
        print(f"First few columns: {list(df.columns)[:5]}")
        
        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:
        # Check if model is loaded
        if MODEL is None:
            result = load_model()
            if not result.startswith("Model loaded"):
                return result
        
        # 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
        try:
            input_name = MODEL.get_inputs()[0].name
            prediction = MODEL.run(None, {input_name: processed_data.values.astype(np.float32)})[0]
            predicted_cost = round(float(prediction[0]), 2)
        except Exception as e:
            return f"Error during prediction: {str(e)}\nProcessed data shape: {processed_data.shape}"
        
        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)}"

# 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"""
)

if __name__ == "__main__":
    print("===== Application Startup =====")
    print(load_model())
    demo.launch(server_name="0.0.0.0")