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
FEATURE_COLUMNS = None

def load_model():
    """Load the ONNX model and get input features"""
    global MODEL, FEATURE_COLUMNS
    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))
        
        # Load feature columns from a saved reference - you'll need to create this
        try:
            FEATURE_COLUMNS = pd.read_csv("model/feature_columns.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"]
        
        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 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:
            print(f"Warning - Error fetching hero data: {str(e)}")
            # If hero stats fail, add placeholder values
            player_data.update({
                "total_games_played": 0,
                "total_winrate": 0.0
            })
            
        # Convert to DataFrame for consistency with training
        df = pd.DataFrame([player_data])
        
        # Ensure all required columns exist
        if FEATURE_COLUMNS:
            for col in FEATURE_COLUMNS:
                if col not in df.columns:
                    df[col] = 0
            # Reorder columns to match model input
            df = df[FEATURE_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:
        # 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
        
        # Print debug information
        print("Processed data shape:", processed_data.shape)
        print("Processed data columns:", processed_data.columns.tolist())
        
        # 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
                
                ### 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"""
)

# Load model on startup
print(load_model())

if __name__ == "__main__":
    demo.launch()