app.py

from flask import Flask, request, jsonify
from flask_cors import CORS
import tensorflow as tf
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from PIL import Image
import numpy as np
import os
import uuid

app = Flask(__name__)
# Di file Flask Anda
# Ganti CORS(app) dengan ini:
origins = [
    "https://5173-idx-skripsigit-1741671480633.cluster-3g4scxt2njdd6uovkqyfcabgo6.cloudworkstations.dev",
    "http://localhost:5173", # Tambahkan ini untuk development lokal jika perlu
]
app.config['UPLOAD_FOLDER'] = 'static/uploads'
CORS(app, resources={r"/api/*": {
    "origins": origins,
    "methods": ["GET", "POST"],
    "allow_headers": ["Content-Type", "Authorization"]
    }})
os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
# Path to model
MODEL_PATH = 'model/model_mobilenetv2.keras'

# Load model
try:
    model = tf.keras.models.load_model(MODEL_PATH)
except Exception as e:
    print(f"Error loading model: {e}")
    model = None

# Image dimensions (adjust based on your model)
IMG_SIZE = (256, 256)
print(tf.__version__)
@app.route('/api', methods=['GET', 'POST'])
def predict():
    if request.method == 'GET':
        return jsonify({'message': 'Hello from Flask!'})

    if model is None:
        return jsonify({'error': 'Model not loaded'}), 500

    if 'image' not in request.files:
        return jsonify({'error': 'No image provided'}), 400

    if request.method == 'POST':
            file = request.files['image']
            if not file.filename:
                return jsonify({'error': 'No file selected'}), 400
            # Membuat nama file yang aman dan unik
            filename = f"{uuid.uuid4().hex}_{file.filename}"
            filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
            file.save(filepath)

            img = Image.open(filepath).convert('RGB')
            img = load_img(filepath, target_size=IMG_SIZE)
            img_array = img_to_array(img)
            img_array = np.expand_dims(img_array, axis=0) / 255.0
            # img = img.resize(IMG_SIZE)
            # img_array = np.array(img) / 255.0
            # img_array = np.expand_dims(img_array, axis=0)

            predictions = model.predict(img_array)
            predicted_class = np.argmax(predictions[0]).tolist()
            probabilities = predictions[0].tolist()

            class_labels = ['Buah Busuk', 'Buah Sehat', 'Daun Bercak Coklat', 'Daun Bercak Hitam', 'Daun Bercak Merah', 'Daun Bercak Putih', 'Daun Berlubang', 'Daun Normal']  # Replace with your labels
            result = {
                'predicted_class': class_labels[predicted_class],
                'probabilities': probabilities,
                'class_index': predicted_class
            }

            return jsonify(result)
        # except Exception as e:
        #     return jsonify({'error': str(e)}), 500

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8080, debug=True)