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import streamlit as st
from transformers import ViTImageProcessor, AutoModelForImageClassification
from PIL import Image
import requests
from io import BytesIO
# 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")
# URL input
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}")
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