app.py

from flask import Flask, render_template, request, jsonify
from prediction_system import predict_tumor
import joblib

app = Flask(__name__)

@app.route('/')
def home():
    try:
        feature_names = joblib.load('feature_names.pkl')
        from sklearn.datasets import load_breast_cancer
        data = load_breast_cancer()
        default_vals = data.data.mean(axis=0)
        
        # Package features with default values for the UI
        feature_data = []
        for i, name in enumerate(feature_names):
            feature_data.append({
                "original_name": name,
                "display_name": name.title(),
                "default": round(float(default_vals[i]), 5)
            })
            
        return render_template('index.html', features=feature_data)
    except FileNotFoundError:
        return "<h1>Error: Models not found</h1><p>Please run <code>python model_training.py</code> first.</p>", 500

@app.route('/predict', methods=['POST'])
def predict():
    try:
        user_input_raw = request.json
        # Convert all incoming feature string values to float
        user_input = {k: float(v) for k, v in user_input_raw.items()}
        
        result = predict_tumor(user_input)
        
        return jsonify({
            'success': True,
            'prediction': result['prediction'],
            'malignant_probability': float(result['malignant_probability']),
            'benign_probability': float(result['benign_probability']),
            'explanations': result.get('explanations', {})
        })
    except Exception as e:
        return jsonify({'success': False, 'error': str(e)}), 400

if __name__ == '__main__':
    app.run(debug=True, port=5000)