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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
# 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 for UI and API endpoint
st.title("NSFW Image Classifier")
# URL input for UI
image_url_ui = st.text_input("Enter Image URL", placeholder="Enter image URL here")
# API endpoint for classification (POST request)
@st.experimental_singleton # Ensure a single instance for performance
def api_endpoint():
if request.method == 'POST':
data = request.json
if 'image_url' in data:
try:
image_url = data['image_url']
# Load image from URL
response = requests.get(image_url)
image = Image.open(BytesIO(response.content))
# Predict and return result as JSON
prediction = predict_image(image)
return json.dumps({'predicted_class': prediction})
except Exception as e:
return json.dumps({'error': str(e)}), 500 # Internal Server Error
else:
return json.dumps({'error': 'Missing "image_url" in request body'}), 400 # Bad Request
else:
return json.dumps({'error': 'Only POST requests are allowed'}), 405 # Method Not Allowed
st.experimental_next_router(api_endpoint) # Register the API endpoint
if image_url_ui:
try:
# Load image from UI input (if URL is provided)
response = requests.get(image_url_ui)
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 (for UI)
prediction = predict_image(image)
st.write(f"Predicted Class: {prediction}")
except Exception as e:
st.write(f"Error: {e}")
# Display API endpoint information
space_url = st.session_state.get('huggingface_space_url') # Assuming it's available
if space_url:
api_endpoint_url = f"{space_url}/api/classify" # Construct the URL based on Space URL
st.write(f"You can also use this API endpoint to classify images:")
st.write(f"```curl")
st.write(f"curl -X POST -H 'Content-Type: application/json' -d '{{ \"image_url\": \"https://example.jpg\" }}' {api_endpoint_url}")
st.write(f"```")
st.write(f"This will return the predicted class in JSON format.") |