from flask import Flask, request, jsonify
from transformers import ViTImageProcessor, AutoModelForImageClassification
from PIL import Image
import requests
import torch

# Inisialisasi Flask app
app = Flask(__name__)

# Inisialisasi model dan processor
processor = ViTImageProcessor.from_pretrained('AdamCodd/vit-base-nsfw-detector')
model = AutoModelForImageClassification.from_pretrained('AdamCodd/vit-base-nsfw-detector')

# Fungsi untuk memproses gambar dan membuat prediksi
def predict_image(url):
    try:
        # Mengambil gambar dari URL
        image = Image.open(requests.get(url, stream=True).raw)
        
        # Memproses gambar dan membuat prediksi
        inputs = processor(images=image, return_tensors="pt")
        outputs = model(**inputs)
        logits = outputs.logits

        # Mengambil prediksi kelas
        predicted_class_idx = logits.argmax(-1).item()
        predicted_label = model.config.id2label[predicted_class_idx]
        
        return predicted_label
    except Exception as e:
        return str(e)

# Route untuk menerima permintaan POST dengan URL gambar
@app.route('/predict', methods=['POST'])
def predict():
    if request.method == 'POST':
        data = request.get_json()
        if 'image_url' not in data:
            return jsonify({'error': 'URL gambar tidak ditemukan dalam request'}), 400
        
        image_url = data['image_url']
        prediction = predict_image(image_url)
        return jsonify({'predicted_class': prediction})

# Menjalankan Flask app
if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000, debug=True)