Model/Flask.py

from flask import Flask, request, jsonify
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.image import img_to_array
import numpy as np
from PIL import Image
import io
import logging
import json
import sys

# === CONFIGURATION ===
MODEL_PATH = "model_resnet152v2.keras"  # Absolute Docker path
CLASS_INDICES_PATH = "class_indices.json"
IMAGE_SIZE = 224

# === Set up logging ===
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

app = Flask(__name__)

# === LOAD MODEL ===
try:
    model = load_model(MODEL_PATH)
    logger.info("✅ Model loaded successfully.")
except Exception as e:
    logger.error(f"❌ Failed to load model: {e}")
    sys.exit(1)

# === LOAD CLASS LABELS ===
try:
    with open(CLASS_INDICES_PATH, "r", encoding="utf-8") as f:
        class_indices = json.load(f)
    idx_to_label = {int(v): k for k, v in class_indices.items()}
except Exception as e:
    logger.error(f"❌ Failed to load class indices: {e}")
    sys.exit(1)

def predict_image_bytes(img_bytes):
    try:
        img = Image.open(io.BytesIO(img_bytes)).convert("RGB")
        img = img.resize((IMAGE_SIZE, IMAGE_SIZE))
        img_array = img_to_array(img) / 255.0
        img_array = np.expand_dims(img_array, axis=0)

        prediction = model.predict(img_array)
        top_index = np.argmax(prediction[0])
        label = idx_to_label[top_index]
        return label.replace('_', ' ').lower()  # Return lowercase to match Spring's expectation
    except Exception as e:
        logger.error(f"Prediction error: {e}")
        return "error"

@app.route('/predict', methods=['POST'])
def predict():
    if 'file' not in request.files:
        return jsonify({'error': 'No file part'}), 400
    
    file = request.files['file']
    if file.filename == '':
        return jsonify({'error': 'No selected file'}), 400

    try:
        img_bytes = file.read()
        prediction = predict_image_bytes(img_bytes)
        logger.info(f"Prediction result: {prediction}")
        return prediction
    except Exception as e:
        logger.error(f"Error processing image: {e}")
        return jsonify({'error': str(e)}), 500

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5003)