added app.py, requirements.txt, and readme.md

README.md

@@ -1,13 +1,32 @@
+# README.md
+"""
 ---
-title: Lol Champion Pick Predictor
-emoji: 🏃
-colorFrom: gray
-colorTo: pink
+title: League of Legends Champion Predictor
+emoji: 🎮
+colorFrom: blue
+colorTo: red
 sdk: gradio
-sdk_version: 5.9.1
+sdk_version: 4.19.2
 app_file: app.py
 pinned: false
-short_description: Predicts LOL Champion pick
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# League of Legends Champion Prediction
+
+This Gradio app predicts the final champion pick in a League of Legends game based on the current team composition.
+
+## Features
+- Player statistics lookup
+- Recent match history display
+- Champion prediction based on current team composition
+- Interactive interface with champion selection dropdowns
+
+## Usage
+1. Enter player name
+2. Click "Show Stats" to view player statistics
+3. Select 9 champions using the dropdowns
+4. Click "Predict" to get the prediction for the final champion
+
+## Model
+This app uses an XGBoost model trained on League of Legends match data to predict the final champion pick.
+"""

app.py

@@ -0,0 +1,140 @@
+# app.py
+import gradio as gr
+import pandas as pd
+import requests
+import xgboost as xgb
+from huggingface_hub import hf_hub_download
+
+# Download the model from Hugging Face Hub
+model_path = hf_hub_download(
+    repo_id="YOUR_USERNAME/YOUR_MODEL_REPO",  # Replace with your model repo
+    filename="model.json"  # Replace with your model filename
+)
+model = xgb.Booster()
+model.load_model(model_path)
+
+# Rest of your code remains the same as before, but remove demo.launch()
+# Define your interface
+with gr.Blocks() as demo:
+   # Assuming you have these helper functions implemented
+def get_player_stats(player_name):
+    """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_name):
+    """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_name, champions):
+    """Prepare features for model prediction"""
+    # Placeholder - implement actual feature engineering
+    features = []  # Transform champions into model features
+    return pd.DataFrame([features])
+
+# Load the model from Hugging Face
+model = xgb.Booster()  # Initialize model
+# model.load_model("path_to_your_model")  # Load your model
+
+# Define champion list for dropdowns
+CHAMPIONS = [
+    "Aatrox", "Ahri", "Akali", "Alistar", "Amumu",
+    # Add more champions...
+]
+
+def show_stats(player_name):
+    """Display player statistics and recent matches"""
+    if not player_name:
+        return "Please enter a player name", None
+    
+    stats = get_player_stats(player_name)
+    recent = get_recent_matches(player_name)
+    
+    stats_html = f"""
+    <div style='padding: 20px; background: #f5f5f5; border-radius: 10px;'>
+        <h3>Player Stats: {player_name}</h3>
+        <p>Wins: {stats['wins']} | Losses: {stats['losses']}</p>
+        <p>Winrate: {stats['winrate']}</p>
+        <p>Favorite Champions: {', '.join(stats['favorite_champions'])}</p>
+    </div>
+    """
+    
+    return stats_html, recent.to_html(index=False)
+
+def predict_champion(player_name, *champions):
+    """Make prediction based on selected champions"""
+    if not player_name or None in champions:
+        return "Please fill in all fields"
+    
+    # Prepare features
+    features = prepare_features(player_name, champions)
+    
+    # Make prediction
+    prediction = model.predict(features)
+    
+    # Get predicted champion name
+    predicted_champion = CHAMPIONS[prediction[0]]  # Adjust based on your model output
+    
+    return f"Predicted champion: {predicted_champion}"
+
+# Create Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# League of Legends Champion Prediction")
+    
+    with gr.Row():
+        player_name = gr.Textbox(label="Player Name")
+        show_button = gr.Button("Show 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_name],
+        outputs=[stats_output, recent_matches]
+    )
+    
+    predict_button.click(
+        fn=predict_champion,
+        inputs=[player_name] + champion_dropdowns,
+        outputs=prediction_output
+    )
+
+# Add this line at the end
+demo.queue()  # Enable queuing for better handling of multiple users
+
+
+
+# .gitignore
+"""
+__pycache__/
+*.py[cod]
+*$py.class
+.env
+.venv
+env/
+venv/
+.DS_Store
+"""