app.py

import os
import torch
from PIL import Image
from torchvision import transforms
from transformers import AutoProcessor, FocalNetForImageClassification
import gradio as gr

# Path to the model
MODEL_PATH = "MichalMlodawski/nsfw-image-detection-large"

# Load the model and feature extractor
feature_extractor = AutoProcessor.from_pretrained(MODEL_PATH)
model = FocalNetForImageClassification.from_pretrained(MODEL_PATH)
model.eval()

# Image transformations
transform = transforms.Compose([
 transforms.Resize((512, 512)),
 transforms.ToTensor(),
 transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

# Mapping from model labels to NSFW categories
LABEL_TO_CATEGORY = {
 "LABEL_0": "Safe",
 "LABEL_1": "Questionable",
 "LABEL_2": "Unsafe"
}

def classify_image(image):
 if image is None:
 return "No image uploaded"

 # Convert to RGB (in case of PNG with alpha channel)
 image = Image.fromarray(image).convert("RGB")
 
 # Process image using feature_extractor
 inputs = feature_extractor(images=image, return_tensors="pt")
 
 # Prediction using the model
 with torch.no_grad():
 outputs = model(**inputs)
 probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
 confidence, predicted = torch.max(probabilities, 1)
 
 # Get the label from the model's configuration
 label = model.config.id2label[predicted.item()]
 category = LABEL_TO_CATEGORY.get(label, "Unknown")
 confidence_value = confidence.item() * 100

 # Prepare the result string
 emoji = {"Safe": "✅", "Questionable": "⚠️", "Unsafe": "🔞"}.get(category, "❓")
 confidence_bar = "🟩" * int(confidence_value // 10) + "⬜" * (10 - int(confidence_value // 10))
 
 #result = f"{emoji} NSFW Category: {category}\n"
 result = f"🏷️ Model Label: {label}\n"
 result += f"🎯 Confidence: {confidence_value:.2f}% {confidence_bar}"

 return result

# Define Gradio interface
iface = gr.Interface(
 fn=classify_image,
 inputs=gr.Image(type="numpy"),
 outputs=gr.Textbox(label="Classification Result"),
 title="🖼️ NSFW Image Classification 🔍",
 description="Upload an image to classify its safety level!",
 theme=gr.themes.Soft(primary_hue="purple"),
 allow_flagging="never"
)

if __name__ == "__main__":
 iface.launch()