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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)
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