import gradio as gr
from transformers import ViTImageProcessor, AutoModelForImageClassification
from PIL import Image
import io
import requests
from flask import Flask, request, jsonify

# Load the model and processor
processor = ViTImageProcessor.from_pretrained('AdamCodd/vit-base-nsfw-detector')
model = AutoModelForImageClassification.from_pretrained('AdamCodd/vit-base-nsfw-detector')

# Define prediction function
def predict_image(image):
    try:
        # Process the image and make prediction
        inputs = processor(images=image, return_tensors="pt")
        outputs = model(**inputs)
        logits = outputs.logits

        # Get predicted class
        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)

# Create Gradio interface
iface = gr.Interface(
    fn=predict_image,
    inputs=gr.Image(type="pil", label="Upload Image"),
    outputs=gr.Textbox(label="Predicted Class"),
    title="NSFW Image Classifier"
)

# Launch the Gradio interface
iface.launch()

# Flask app for API endpoint
app = Flask(__name__)

@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:
        # Load image from the uploaded file
        image = Image.open(file.stream)
        
        # Process the image and make prediction
        inputs = processor(images=image, return_tensors="pt")
        outputs = model(**inputs)
        logits = outputs.logits

        # Get predicted class
        predicted_class_idx = logits.argmax(-1).item()
        predicted_label = model.config.id2label[predicted_class_idx]
        
        return jsonify({'predicted_class': predicted_label})
    except Exception as e:
        return jsonify({'error': str(e)}), 500

# Run Flask app
if __name__ == '__main__':
    app.run(port=5000)