Add Gradio app and project configuration

Introduces a Gradio-based UFC fight prediction app in app.py, which loads models and predicts fight outcomes based on user input. Adds pyproject.toml with project metadata and dependencies for reproducible builds and packaging.

app.py

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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')
+        }
+
+        predicted_winner = model.predict(fight)
+        
+        if predicted_winner:
+            return f"Predicted Winner: {predicted_winner}"
+        else:
+            return "Could not make a prediction. Is one of the fighters new or not in the dataset?"
+            
+    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")
+    output_text = gr.Textbox(label="Prediction Result", interactive=False)
+
+    predict_button.click(
+        fn=predict_fight,
+        inputs=[model_dropdown, fighter1_input, fighter2_input],
+        outputs=output_text
+    )
+
+# --- Launch the App ---
+demo.launch() 

pyproject.toml

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+[build-system]
+requires = ["setuptools>=61.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "ufc_predictor"
+version = "0.1.0"
+authors = [
+  { name="Álvaro Menéndez Ros", email="[email protected]" },
+]
+description = "A model for predicting UFC fight outcomes."
+requires-python = ">=3.8"
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "License :: OSI Approved :: MIT License",
+    "Operating System :: OS Independent",
+]
+dependencies = [
+    "gradio==4.28.3",
+    "scikit-learn==1.4.2",
+    "pandas==2.2.2",
+    "xgboost==2.0.3",
+    "lightgbm==4.3.0",
+    "joblib==1.4.2",
+]
+
+[tool.setuptools]
+package-dir = {"" = "src"}
+packages = ["find:"]