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import streamlit as st
from transformers import ViTImageProcessor, AutoModelForImageClassification
from PIL import Image
import requests
from io import BytesIO
import json
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)
# Streamlit app
st.title("NSFW Image Classifier")
# Display API usage instructions
st.write("You can use this app with the API endpoint below. Send a POST request with the image URL to get classification.")
st.write("Example URL to use with curl:")
st.code("curl -X POST https://huggingface.co/spaces/yeftakun/nsfw_api2/api/classify -H 'Content-Type: application/json' -d '{\"image_url\": \"https://example.jpg\"}'")
# URL input for UI
image_url = st.text_input("Enter Image URL", placeholder="Enter image URL here")
if image_url:
try:
# Load image from URL
response = requests.get(image_url)
image = Image.open(BytesIO(response.content))
st.image(image, caption='Image from URL', use_column_width=True)
st.write("")
st.write("Classifying...")
# Predict and display result
prediction = predict_image(image)
st.write(f"Predicted Class: {prediction}")
except Exception as e:
st.write(f"Error: {e}")
# API Endpoint using Flask
app = Flask(__name__)
@app.route('/api/classify', methods=['POST'])
def classify():
data = request.json
image_url = data.get('image_url')
if not image_url:
return jsonify({"error": "Image URL is required"}), 400
try:
# Load image from URL
response = requests.get(image_url)
image = Image.open(BytesIO(response.content))
# Predict image
prediction = predict_image(image)
return jsonify({"predicted_class": prediction})
except Exception as e:
return jsonify({"error": str(e)}), 500
if __name__ == '__main__':
app.run(port=5000)
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