from flask import Flask, request, jsonify
import os
from datetime import datetime
import numpy as np
from PIL import Image
from tensorflow.keras.models import load_model

app = Flask(__name__)

UPLOAD_FOLDER = os.path.join(os.path.dirname(__file__), 'uploads')
os.makedirs(UPLOAD_FOLDER, exist_ok=True)

MODEL_PATH = os.path.join(os.path.dirname(__file__), 'model_padi.h5')

CLASS_NAMES = [
    'Healthy',
    'Blast',
    'Blight',
    'Tungro'
]


# Load model
model = None

try:
    model = load_model(MODEL_PATH)

    print("Model berhasil dimuat")
    print("Input Shape :", model.input_shape)
    print("Output Shape:", model.output_shape)
    print("Model Type  :", type(model))

except Exception as e:
    print("Gagal load model:", e)


# Preprocessing gambar
def preprocess_image(path):

    image = Image.open(path).convert('RGB')

    image = image.resize((224, 224))

    image_array = np.array(image).astype('float32') / 255.0

    image_array = np.expand_dims(image_array, axis=0)

    return image_array


@app.route('/prediksi', methods=['POST'])
def prediksi():

    if 'gambar' not in request.files:
        return jsonify({
            'error': 'Tidak ada gambar'
        }), 400

    file = request.files['gambar']

    if file.filename == '':
        return jsonify({
            'error': 'File kosong'
        }), 400

    # Simpan gambar
    filename = datetime.now().strftime('%Y%m%d%H%M%S_') + file.filename

    filepath = os.path.join(UPLOAD_FOLDER, filename)

    file.save(filepath)

    print("Gambar diterima:", filename)

    if model is None:
        return jsonify({
            'error': 'Model gagal dimuat'
        }), 500

    try:

        # Preprocessing
        image_array = preprocess_image(filepath)

        # Prediksi
        predictions = model.predict(image_array)

        print("\n===== HASIL PREDIKSI =====")

        for i, nama in enumerate(CLASS_NAMES):
            print(f"{nama}: {predictions[0][i] * 100:.2f}%")

        predicted_index = int(np.argmax(predictions[0]))

        label = CLASS_NAMES[predicted_index]

        confidence_display = round(
            float(predictions[0][predicted_index]) * 100,
            2
        )

        print(f"HASIL AKHIR: {label}")
        print(f"CONFIDENCE: {confidence_display}%")

        # Semua probabilitas
        all_predictions = {}

        for i, nama in enumerate(CLASS_NAMES):

            all_predictions[nama] = round(
                float(predictions[0][i]) * 100,
                2
            )

        # Deskripsi penyakit
        deskripsi = {
            'Blast': {
                'deskripsi': 'Penyakit jamur yang menyebabkan bercak pada daun.',
                'solusi': 'Gunakan fungisida dan varietas tahan penyakit.'
            },

            'Blight': {
                'deskripsi': 'Hawar daun akibat bakteri.',
                'solusi': 'Gunakan benih sehat dan kurangi kelembapan.'
            },

            'Healthy': {
                'deskripsi': 'Daun padi sehat tanpa gejala penyakit.',
                'solusi': 'Pertahankan perawatan tanaman.'
            },

            'Tungro': {
                'deskripsi': 'Penyakit virus yang ditularkan wereng.',
                'solusi': 'Kendalikan wereng dan gunakan varietas tahan.'
            }
        }

        return jsonify({

            'penyakit': label,

            'confidence': confidence_display,

            'all_predictions': all_predictions,

            'deskripsi': deskripsi[label]['deskripsi'],

            'solusi': deskripsi[label]['solusi']

        })

    except Exception as e:

        print("ERROR:", e)

        return jsonify({
            'error': str(e)
        }), 500


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