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| import gradio as gr | |
| import pandas as pd | |
| from xgboost import Booster, DMatrix | |
| import numpy as np | |
| # Define the mapping of card names to IDs (placeholder example) | |
| card_numbers = { | |
| "Card 1": 1, | |
| "Card 2": 2, | |
| "Card 3": 3, | |
| "Card 4": 4, | |
| "Card 5": 5, | |
| "Card 6": 6, | |
| "Card 7": 7, | |
| "Card 8": 8, | |
| # Add all 181 cards here... | |
| } | |
| MODEL_PATH = "clash_royale_model/model.json" | |
| def deck_to_ids(deck, mapping): | |
| """Convert card names to IDs based on the mapping.""" | |
| return [mapping.get(card, 0) - 1 for card in deck] # Zero-based indices | |
| def preprocess_deck(deck): | |
| """Prepare the selected deck for the model.""" | |
| # Convert cards to IDs | |
| deck_ids = deck_to_ids(deck, card_numbers) | |
| # Perform one-hot encoding | |
| num_choices = 181 # Total number of cards | |
| one_hot = np.zeros(num_choices, dtype=int) | |
| one_hot[np.array(deck_ids)] = 1 # Set 1 for selected cards | |
| # Add additional features (placeholder for now) | |
| trophy_difference = 0 # Placeholder for trophy difference | |
| elixir_leaked = 0 # Placeholder for leaked elixir | |
| # Combine features | |
| features = np.concatenate(([trophy_difference, elixir_leaked], one_hot)) | |
| return pd.DataFrame([features]) | |
| def load_model(model_path): | |
| """Load the saved XGBoost model.""" | |
| model = Booster() | |
| model.load_model(model_path) | |
| return model | |
| # Load the model at startup | |
| model = load_model(MODEL_PATH) | |
| def predict_outcome(opponent_deck): | |
| """Make a prediction based on the opponent's deck.""" | |
| # Prepare the opponent deck data | |
| deck_data = preprocess_deck(opponent_deck) | |
| # Make the prediction | |
| dmatrix = DMatrix(deck_data) # Convert data to DMatrix format | |
| prediction = model.predict(dmatrix) | |
| # Interpret the prediction | |
| result = f"Probability of Winning: {prediction[0] * 100:.2f}%" | |
| return result | |
| # List of cards for selection | |
| card_list = list(card_numbers.keys()) | |
| # Create the Gradio interface | |
| with gr.Blocks() as interface: | |
| gr.Markdown("## Clash Royale Prediction") | |
| gr.Markdown("Select the 8 cards from the opponent's deck to predict the probability of winning!") | |
| opponent_deck = gr.CheckboxGroup( | |
| choices=card_list, | |
| label="Select 8 cards from the opponent's deck:", | |
| info="Select exactly 8 cards." | |
| ) | |
| result = gr.Textbox(label="Prediction Result:", interactive=False) | |
| def validate_and_predict(deck): | |
| """Validate the number of selected cards and make a prediction.""" | |
| if len(deck) != 8: | |
| return "Error: Select exactly 8 cards." | |
| return predict_outcome(deck) | |
| predict_button = gr.Button("Make Prediction") | |
| predict_button.click(validate_and_predict, inputs=[opponent_deck], outputs=[result]) | |
| # Launch the interface | |
| interface.launch() | |