added model

util/app.py

@@ -27,63 +27,60 @@ from selenium.webdriver.support.ui import WebDriverWait
 from selenium.webdriver.support import expected_conditions as EC
 from helper import format_summoner_name
 
-# Download the model from Hugging Face Hub
-model_path = hf_hub_download(
-    repo_id="ivwhy/champion-predictor-model",  # Replace with your model repo
-    filename="champion_predictor.json"  # Replace with your model filename
-
-)
-model = xgb.Booster()
-model.load_model(model_path)
-
-# Define your interface
-with gr.Blocks() as demo:
-   # Assuming you have these helper functions implemented
-    def get_user_training_df(player_opgg_url):
-       
-       training_df = create_app_user_training_df(player_opgg_url)
-
-       return training_df
-   
-
-        # 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"
-    ]
-
-
-    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
-        
+# 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:
+        training_df = create_app_user_training_df(player_opgg_url)
+        return training_df
+    except Exception as 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
 
-        # Assume `training_features` is the DataFrame provided
-        # Calculate total wins and losses
         wins = training_features['result'].sum()
         losses = len(training_features) - wins
-
-        # Calculate winrate
         winrate = f"{(wins / len(training_features)) * 100:.0f}%"
-
-        # Calculate favorite champions
         favorite_champions = (
             training_features['champion']
             .value_counts()
@@ -91,75 +88,72 @@ with gr.Blocks() as demo:
             .index.tolist()
         )
 
-        # Create the summary dictionary
-        summary_data = {
-            'wins': wins,
-            'losses': losses,
-            'winrate': winrate,
-            'favorite_champions': favorite_champions
-        }
-
         stats_html = f"""
         <div style='padding: 20px; background: #f5f5f5; border-radius: 10px;'>
-            <h3>Player Stats: {player_opgg_url}</h3>
-            <p>Wins: {stats['wins']} | Losses: {stats['losses']}</p>
-            <p>Winrate: {stats['winrate']}</p>
-            <p>Favorite Champions: {', '.join(stats['favorite_champions'])}</p>
+            <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
+        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"
 
-    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"
-        
-        # Prepare features
         features = get_user_training_df(player_opgg_url, champions)
-        
-        # Make prediction
+        if isinstance(features, str):  # Error message
+            return features
+            
         prediction = model.predict(features)
-        
-        # Get predicted champion name
-        predicted_champion = CHAMPIONS[prediction[0]]  # Adjust based on your model output
-        
+        predicted_champion = CHAMPIONS[prediction[0]]
         return f"Predicted champion: {predicted_champion}"
+    except Exception as e:
+        return f"Error making prediction: {e}"
 
-    # Create Gradio 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 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]
-        )
+# 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 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")
         
-        predict_button.click(
-            fn=predict_champion,
-            inputs=[player_opgg_url] + champion_dropdowns,
-            outputs=prediction_output
-        )
+    # 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
+    )
 
 # Add this line at the end
 demo.queue()  # Enable queuing for better handling of multiple users