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import os

import joblib
import numpy as np
import pandas as pd
from flask import Flask, jsonify, render_template_string, request
from flask_cors import CORS
from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier

app = Flask(__name__)
CORS(app)

# Global variables for model and iris data
model = None
iris = None
feature_names = None
target_names = None

def load_or_train_model():
    """Load existing model or train new one if not exists"""
    global model, iris, feature_names, target_names
    
    # Load iris dataset
    iris = load_iris()
    feature_names = iris.feature_names
    target_names = iris.target_names
    
    model_path = 'iris_decision_tree_model.pkl'
    
    if os.path.exists(model_path):
        # Load existing model
        model = joblib.load(model_path)
        print("Model loaded from file")
    else:
        # Train new model
        print("Training new model...")
        X = iris.data
        y = iris.target
        
        X_train, X_test, y_train, y_test = train_test_split(
            X, y, test_size=0.2, random_state=42
        )
        
        model = DecisionTreeClassifier(random_state=42)
        model.fit(X_train, y_train)
        
        # Save model
        joblib.dump(model, model_path)
        
        # Print accuracy
        y_pred = model.predict(X_test)
        accuracy = accuracy_score(y_test, y_pred)
        print(f"Model trained with accuracy: {accuracy:.4f}")

# Initialize model on startup
load_or_train_model()

@app.route('/')
def home():
    html = """
    <!DOCTYPE html>
    <html>
    <head>
        <title>Iris Flower Classification API</title>
        <style>
            body { font-family: Arial, sans-serif; margin: 40px; background-color: #f5f5f5; }
            .container { max-width: 800px; margin: 0 auto; background: white; padding: 30px; border-radius: 10px; box-shadow: 0 0 20px rgba(0,0,0,0.1); }
            h1 { color: #2c3e50; text-align: center; }
            .feature { margin: 10px 0; }
            .feature label { display: inline-block; width: 200px; font-weight: bold; }
            .feature input { padding: 8px; width: 200px; border: 1px solid #ddd; border-radius: 4px; }
            button { background: #3498db; color: white; padding: 10px 20px; border: none; border-radius: 4px; cursor: pointer; margin: 10px 5px; }
            button:hover { background: #2980b9; }
            .result { margin: 20px 0; padding: 15px; background: #ecf0f1; border-radius: 5px; }
            .examples { background: #f8f9fa; padding: 15px; border-radius: 5px; margin: 20px 0; }
        </style>
    </head>
    <body>
        <div class="container">
            <h1>🌸 Iris Flower Classification API</h1>
            <p>Masukkan nilai fitur bunga Iris untuk memprediksi spesiesnya:</p>
            
            <form id="irisForm">
                <div class="feature">
                    <label>Sepal Length (cm):</label>
                    <input type="number" step="0.1" id="sepal_length" placeholder="e.g., 5.1" required>
                </div>
                <div class="feature">
                    <label>Sepal Width (cm):</label>
                    <input type="number" step="0.1" id="sepal_width" placeholder="e.g., 3.5" required>
                </div>
                <div class="feature">
                    <label>Petal Length (cm):</label>
                    <input type="number" step="0.1" id="petal_length" placeholder="e.g., 1.4" required>
                </div>
                <div class="feature">
                    <label>Petal Width (cm):</label>
                    <input type="number" step="0.1" id="petal_width" placeholder="e.g., 0.2" required>
                </div>
                
                <button type="submit">Prediksi Spesies</button>
                <button type="button" onclick="loadExample(1)">Contoh Setosa</button>
                <button type="button" onclick="loadExample(2)">Contoh Versicolor</button>
                <button type="button" onclick="loadExample(3)">Contoh Virginica</button>
            </form>
            
            <div id="result" class="result" style="display:none;">
                <h3>Hasil Prediksi:</h3>
                <p id="prediction"></p>
                <p id="confidence"></p>
            </div>
            
            <div class="examples">
                <h3>Contoh Data:</h3>
                <p><strong>Setosa:</strong> Sepal Length: 5.1, Sepal Width: 3.5, Petal Length: 1.4, Petal Width: 0.2</p>
                <p><strong>Versicolor:</strong> Sepal Length: 7.0, Sepal Width: 3.2, Petal Length: 4.7, Petal Width: 1.4</p>
                <p><strong>Virginica:</strong> Sepal Length: 6.3, Sepal Width: 3.3, Petal Length: 6.0, Petal Width: 2.5</p>
            </div>
        </div>

        <script>
            function loadExample(type) {
                if (type === 1) {
                    document.getElementById('sepal_length').value = 5.1;
                    document.getElementById('sepal_width').value = 3.5;
                    document.getElementById('petal_length').value = 1.4;
                    document.getElementById('petal_width').value = 0.2;
                } else if (type === 2) {
                    document.getElementById('sepal_length').value = 7.0;
                    document.getElementById('sepal_width').value = 3.2;
                    document.getElementById('petal_length').value = 4.7;
                    document.getElementById('petal_width').value = 1.4;
                } else if (type === 3) {
                    document.getElementById('sepal_length').value = 6.3;
                    document.getElementById('sepal_width').value = 3.3;
                    document.getElementById('petal_length').value = 6.0;
                    document.getElementById('petal_width').value = 2.5;
                }
            }
            
            document.getElementById('irisForm').addEventListener('submit', function(e) {
                e.preventDefault();
                
                const data = {
                    sepal_length: parseFloat(document.getElementById('sepal_length').value),
                    sepal_width: parseFloat(document.getElementById('sepal_width').value),
                    petal_length: parseFloat(document.getElementById('petal_length').value),
                    petal_width: parseFloat(document.getElementById('petal_width').value)
                };
                
                fetch('/predict', {
                    method: 'POST',
                    headers: {
                        'Content-Type': 'application/json',
                    },
                    body: JSON.stringify(data)
                })
                .then(response => response.json())
                .then(data => {
                    if (data.error) {
                        alert('Error: ' + data.error);
                    } else {
                        document.getElementById('prediction').innerHTML = 
                            `<strong>Spesies: ${data.species}</strong>`;
                        document.getElementById('confidence').innerHTML = 
                            `Confidence: ${data.confidence}`;
                        document.getElementById('result').style.display = 'block';
                    }
                })
                .catch(error => {
                    alert('Error: ' + error);
                });
            });
        </script>
    </body>
    </html>
    """
    return html

@app.route('/predict', methods=['POST'])
def predict_iris():
    try:
        # Ambil data dari request
        data = request.json
        
        if not data:
            return jsonify({'error': 'No data provided'}), 400
        
        # Validasi input
        required_fields = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']
        for field in required_fields:
            if field not in data:
                return jsonify({'error': f'Missing field: {field}'}), 400
            if not isinstance(data[field], (int, float)):
                return jsonify({'error': f'Invalid value for {field}. Must be a number'}), 400
        
        # Konversi ke array numpy
        features = np.array([[
            data['sepal_length'],
            data['sepal_width'], 
            data['petal_length'],
            data['petal_width']
        ]])
        
        # Validasi range nilai (opsional)
        if any(val < 0 for val in features[0]):
            return jsonify({'error': 'All feature values must be positive'}), 400
        
        # Prediksi
        prediction = model.predict(features)[0]
        prediction_proba = model.predict_proba(features)[0]
        
        # Konversi ke nama spesies
        species = target_names[prediction]
        confidence = f"{prediction_proba[prediction]:.2%}"
        
        # Tambahan info untuk debugging
        probabilities = {
            target_names[i]: f"{prob:.2%}" 
            for i, prob in enumerate(prediction_proba)
        }
        
        return jsonify({
            'species': species,
            'species_code': int(prediction),
            'confidence': confidence,
            'all_probabilities': probabilities,
            'input_features': {
                'sepal_length': data['sepal_length'],
                'sepal_width': data['sepal_width'],
                'petal_length': data['petal_length'],
                'petal_width': data['petal_width']
            }
        })
        
    except Exception as e:
        return jsonify({'error': str(e)}), 500

@app.route('/model-info', methods=['GET'])
def model_info():
    """Endpoint untuk mendapatkan informasi model"""
    try:
        # Dapatkan feature importance
        feature_importance = model.feature_importances_
        feature_info = {
            feature_names[i]: float(importance) 
            for i, importance in enumerate(feature_importance)
        }
        
        return jsonify({
            'model_type': 'Decision Tree Classifier',
            'features': list(feature_names),
            'target_classes': list(target_names),
            'feature_importance': feature_info,
            'tree_depth': model.get_depth(),
            'number_of_leaves': model.get_n_leaves(),
            'training_samples': len(iris.data)
        })
        
    except Exception as e:
        return jsonify({'error': str(e)}), 500

@app.route('/health', methods=['GET'])
def health():
    return jsonify({
        'status': 'OK', 
        'message': 'Iris Classification API is running',
        'model_loaded': model is not None
    }), 200

if __name__ == '__main__':
    app.run(debug=True, host='0.0.0.0', port=7860)