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| import gradio as gr | |
| import joblib | |
| from datetime import datetime | |
| import os | |
| import sys | |
| # --- Path and Module Setup --- | |
| # Add the 'src' directory to the system path so we can import our custom modules. | |
| sys.path.append(os.path.join(os.path.dirname(__file__), 'src')) | |
| # Although these models are not called directly, they MUST be imported here. | |
| # joblib.load() needs these class definitions in scope to deserialize the model files correctly. | |
| from src.predict.models import ( | |
| BaseMLModel, | |
| EloBaselineModel, | |
| LogisticRegressionModel, | |
| XGBoostModel, | |
| SVCModel, | |
| RandomForestModel, | |
| BernoulliNBModel, | |
| LGBMModel | |
| ) | |
| # Import the configuration variable for the models directory for consistency. | |
| from src.config import MODELS_DIR | |
| # --- Gradio App Setup --- | |
| if not os.path.exists(MODELS_DIR): | |
| os.makedirs(MODELS_DIR) | |
| print(f"Warning: Models directory not found. Created a dummy directory at '{MODELS_DIR}'.") | |
| # Get a list of available models | |
| available_models = [f for f in os.listdir(MODELS_DIR) if f.endswith(".joblib")] | |
| if not available_models: | |
| print(f"Warning: No models found in '{MODELS_DIR}'. The dropdown will be empty.") | |
| available_models.append("No models found") | |
| # --- Prediction Function --- | |
| def predict_fight(model_name, fighter1_name, fighter2_name): | |
| """ | |
| Loads the selected model and predicts the winner of a fight. | |
| """ | |
| if model_name == "No models found" or not fighter1_name or not fighter2_name: | |
| return "Please select a model and enter both fighter names." | |
| model_path = os.path.join(MODELS_DIR, model_name) | |
| try: | |
| print(f"Loading model: {model_name}") | |
| model = joblib.load(model_path) | |
| fight = { | |
| 'fighter_1': fighter1_name, | |
| 'fighter_2': fighter2_name, | |
| 'event_date': datetime.now().strftime('%B %d, %Y') | |
| } | |
| prediction_result = model.predict(fight) | |
| if prediction_result and prediction_result.get('winner'): | |
| winner = prediction_result['winner'] | |
| prob = prediction_result['probability'] | |
| return winner, f"{prob:.1%}" | |
| else: | |
| return "Could not make a prediction.", "" | |
| except FileNotFoundError: | |
| return f"Error: Model file '{model_name}' not found.", "" | |
| except Exception as e: | |
| print(f"An error occurred during prediction: {e}") | |
| return f"An error occurred: {e}", "" | |
| # --- Gradio Interface --- | |
| with gr.Blocks(theme=gr.themes.Soft()) as demo: | |
| gr.Markdown("# 🥋 UFC Fight Predictor 🥊") | |
| gr.Markdown("Select a prediction model and enter two fighter names to predict the outcome.") | |
| with gr.Column(): | |
| model_dropdown = gr.Dropdown( | |
| label="Select Model", | |
| choices=available_models, | |
| value=available_models[0] if available_models else None | |
| ) | |
| with gr.Row(): | |
| fighter1_input = gr.Textbox(label="Fighter 1", placeholder="e.g., Jon Jones") | |
| fighter2_input = gr.Textbox(label="Fighter 2", placeholder="e.g., Stipe Miocic") | |
| predict_button = gr.Button("Predict Winner") | |
| with gr.Column(): | |
| winner_output = gr.Textbox(label="Predicted Winner", interactive=False) | |
| prob_output = gr.Textbox(label="Confidence", interactive=False) | |
| predict_button.click( | |
| fn=predict_fight, | |
| inputs=[model_dropdown, fighter1_input, fighter2_input], | |
| outputs=[winner_output, prob_output] | |
| ) | |
| # --- Launch the App --- | |
| demo.launch() |