readme updated

app.py

@@ -17,110 +17,110 @@ model.load_model(model_path)
 # 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_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 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])
+    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
+    # 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...
-]
+    # 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 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}"
+    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
-    )
+    # 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