util/app.py

# app.py
import gradio as gr
import pandas as pd
import requests
import xgboost as xgb
from huggingface_hub import hf_hub_download

from Recent_match_scrapper import get_multiple_matches_stats
from Meta_scrapper import get_meta_stats
from Leaderboard_scrapper import scrape_leaderboards
from connection_check import check_connection
from helper import merge_stats, filter_leaderboard, get_player_list
from Player_scrapper import get_multiple_player_stats, get_player_stats
from feature_eng import create_champion_features
from Weekly_meta_scrapper import get_weekly_meta
from app_training_df_getter import create_app_user_training_df
from sklearn.metrics import top_k_accuracy_score

import os
import time
from selenium import webdriver
from selenium.webdriver.chrome.service import Service
from selenium.webdriver.chrome.options import Options
from webdriver_manager.chrome import ChromeDriverManager
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from helper import format_summoner_name

# Define champion list for dropdowns
CHAMPIONS = [
    "Aatrox", "Ahri", "Akali", "Akshan", "Alistar", "Amumu", "Anivia", "Annie", "Aphelios", "Ashe",
    "Aurelion Sol", "Azir", "Bard", "Bel'Veth", "Blitzcrank", "Brand", "Braum", "Caitlyn", "Camille",
    "Cassiopeia", "Cho'Gath", "Corki", "Darius", "Diana", "Dr. Mundo", "Draven", "Ekko", "Elise",
    "Evelynn", "Ezreal", "Fiddlesticks", "Fiora", "Fizz", "Galio", "Gangplank", "Garen", "Gnar",
    "Gragas", "Graves", "Gwen", "Hecarim", "Heimerdinger", "Illaoi", "Irelia", "Ivern", "Janna",
    "Jarvan IV", "Jax", "Jayce", "Jhin", "Jinx", "Kai'Sa", "Kalista", "Karma", "Karthus", "Kassadin",
    "Katarina", "Kayle", "Kayn", "Kennen", "Kha'Zix", "Kindred", "Kled", "Kog'Maw", "KSante", "LeBlanc",
    "Lee Sin", "Leona", "Lillia", "Lissandra", "Lucian", "Lulu", "Lux", "Malphite", "Malzahar", "Maokai",
    "Master Yi", "Milio", "Miss Fortune", "Mordekaiser", "Morgana", "Naafiri", "Nami", "Nasus", "Nautilus",
    "Neeko", "Nidalee", "Nilah", "Nocturne", "Nunu & Willump", "Olaf", "Orianna", "Ornn", "Pantheon",
    "Poppy", "Pyke", "Qiyana", "Quinn", "Rakan", "Rammus", "Rek'Sai", "Rell", "Renata Glasc", "Renekton",
    "Rengar", "Riven", "Rumble", "Ryze", "Samira", "Sejuani", "Senna", "Seraphine", "Sett", "Shaco",
    "Shen", "Shyvana", "Singed", "Sion", "Sivir", "Skarner", "Sona", "Soraka", "Swain", "Sylas",
    "Syndra", "Tahm Kench", "Taliyah", "Talon", "Taric", "Teemo", "Thresh", "Tristana", "Trundle",
    "Tryndamere", "Twisted Fate", "Twitch", "Udyr", "Urgot", "Varus", "Vayne", "Veigar", "Vel'Koz",
    "Vex", "Vi", "Viego", "Viktor", "Vladimir", "Volibear", "Warwick", "Wukong", "Xayah", "Xerath",
    "Xin Zhao", "Yasuo", "Yone", "Yorick", "Yuumi", "Zac", "Zed", "Zeri", "Ziggs", "Zilean", "Zoe", "Zyra"
]

# Load model
try:
    model_path = hf_hub_download(
        repo_id="ivwhy/champion-predictor-model",
        filename="champion_predictor.json"
    )
    model = xgb.Booster()
    model.load_model(model_path)
except Exception as e:
    print(f"Error loading model: {e}")
    model = None

def get_user_training_df(player_opgg_url):
    try:

        # Add input validation
        if not player_opgg_url or not isinstance(player_opgg_url, str):
            return "Invalid URL provided"
        
         # Add debugging print
        print(f"Processing URL: {player_opgg_url}")

        training_df = create_app_user_training_df(player_opgg_url)
        return training_df
    except Exception as e:

        # Add more detailed error information
        import traceback
        error_trace = traceback.format_exc()
        print(f"Full error trace:\n{error_trace}")
        return f"Error getting training data: {str(e)}"

        #return f"Error getting training data: {e}"

def show_stats(player_opgg_url):
    """Display player statistics and recent matches"""
    if not player_opgg_url:
        return "Please enter a player link to OPGG", None
    
    try:
        training_features = get_user_training_df(player_opgg_url)
        
        if isinstance(training_features, str):  # Error message
            return training_features, None

        wins = training_features['result'].sum()
        losses = len(training_features) - wins
        winrate = f"{(wins / len(training_features)) * 100:.0f}%"
        favorite_champions = (
            training_features['champion']
            .value_counts()
            .head(3)
            .index.tolist()
        )

        stats_html = f"""
        <div style='padding: 20px; background: #f5f5f5; border-radius: 10px;'>
            <h3>Player Stats</h3>
            <p>Wins: {wins} | Losses: {losses}</p>
            <p>Winrate: {winrate}</p>
            <p>Favorite Champions: {', '.join(favorite_champions)}</p>
        </div>
        """
        
        return stats_html, None
    except Exception as e:
        return f"Error processing stats: {e}", None

def predict_champion(player_opgg_url, *champions):
    """Make prediction based on selected champions"""
    if not player_opgg_url or None in champions:
        return "Please fill in all fields"
    
    try:
        if model is None:
            return "Model not loaded properly"

        features = get_user_training_df(player_opgg_url)
        if isinstance(features, str):  # Error message
            return features
            
        prediction = model.predict(features)
        predicted_champion = CHAMPIONS[prediction[0]]
        return f"Predicted champion: {predicted_champion}"
    except Exception as e:
        return f"Error making prediction: {e}"

# Define your interface
with gr.Blocks() as demo:
    gr.Markdown("# League of Legends Champion Prediction")
    
    with gr.Row():
        player_opgg_url = gr.Textbox(label="OPGG Player URL")
        show_button = gr.Button("Show Player Stats")
    
    with gr.Row():
        stats_output = gr.HTML(label="Player Statistics")
        recent_matches = gr.HTML(label="Recent Matches")
    
    with gr.Row():
        champion_dropdowns = [
            gr.Dropdown(choices=CHAMPIONS, label=f"Champion {i+1}")
            for i in range(9)
        ]
    
    with gr.Row():
        predict_button = gr.Button("Predict")
        prediction_output = gr.Text(label="Prediction")
        
    # Set up event handlers
    show_button.click(
        fn=show_stats,
        inputs=[player_opgg_url],
        outputs=[stats_output, recent_matches]
    )
    
    predict_button.click(
        fn=predict_champion,
        inputs=[player_opgg_url] + champion_dropdowns,
        outputs=prediction_output
    )

# Enable queuing
demo.queue()

# For local testing
if __name__ == "__main__":
    demo.launch()


''' code graveyard

    def get_player_stats(player_opgg_url):
        """Get player statistics from API"""
        # Placeholder - implement actual API call

        return {
            'wins': 120,
            'losses': 80,
            'winrate': '60%',
            'favorite_champions': ['Ahri', 'Zed', 'Yasuo']
        }

    def get_recent_matches(player_opgg_url):
        """Get recent match history"""
        # Placeholder - implement actual API call
        return pd.DataFrame({
            'champion': ['Ahri', 'Zed', 'Yasuo'],
            'result': ['Win', 'Loss', 'Win'],
            'kda': ['8/2/10', '4/5/3', '12/3/7']
        })

    def prepare_features(player_opgg_url, champions):
        """Prepare features for model prediction"""
        # Placeholder - implement actual feature engineering
        features = []  # Transform champions into model features
        return pd.DataFrame([features])
   

'''