api.py

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)