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# Create a Gradio demo for the Multi-Head model
import gradio as gr
import torch
import torch.nn as nn
from transformers import AutoModelForImageClassification, AutoImageProcessor
from huggingface_hub import hf_hub_download
# ========================================
# MODEL DEFINITION
# ========================================
class MultiHeadContentModerator(nn.Module):
def __init__(self, base_model_name="Falconsai/nsfw_image_detection", num_violence_labels=2):
super().__init__()
original_model = AutoModelForImageClassification.from_pretrained(base_model_name)
hidden_size = original_model.config.hidden_size
self.vit = original_model.vit
self.nsfw_classifier = original_model.classifier
self.violence_classifier = nn.Linear(hidden_size, num_violence_labels)
# Falconsai uses: {0: 'normal', 1: 'nsfw'}
self.nsfw_id2label = {0: 'normal', 1: 'nsfw'}
self.violence_id2label = {0: 'safe', 1: 'violence'}
def forward(self, pixel_values, task='both'):
outputs = self.vit(pixel_values=pixel_values)
pooled_output = outputs.last_hidden_state[:, 0]
if task == 'both':
return {
'nsfw': self.nsfw_classifier(pooled_output),
'violence': self.violence_classifier(pooled_output)
}
elif task == 'nsfw':
return self.nsfw_classifier(pooled_output)
elif task == 'violence':
return self.violence_classifier(pooled_output)
# ========================================
# LOAD MODEL
# ========================================
MODEL_ID = "Ali7880/multihead-content-moderator" # Change this!
# Download model files
checkpoint_path = hf_hub_download(MODEL_ID, "multihead_model.pt")
checkpoint = torch.load(checkpoint_path, map_location='cpu', weights_only=False)
# Create and load model
model = MultiHeadContentModerator(
base_model_name=checkpoint['base_model'],
num_violence_labels=checkpoint['num_violence_labels']
)
model.load_state_dict(checkpoint['model_state_dict'])
model.violence_id2label = checkpoint['violence_id2label']
model.nsfw_id2label = checkpoint['nsfw_id2label']
model.eval()
processor = AutoImageProcessor.from_pretrained(MODEL_ID)
# ========================================
# INFERENCE FUNCTION
# ========================================
def moderate_image(image):
if image is None:
return None, None, "Please upload an image"
# Preprocess
inputs = processor(images=image, return_tensors="pt")
# Predict
with torch.no_grad():
outputs = model(inputs['pixel_values'], task='both')
nsfw_probs = torch.softmax(outputs['nsfw'], dim=-1).numpy()[0]
violence_probs = torch.softmax(outputs['violence'], dim=-1).numpy()[0]
# Format results
nsfw_results = {model.nsfw_id2label[i]: float(p) for i, p in enumerate(nsfw_probs)}
violence_results = {model.violence_id2label[i]: float(p) for i, p in enumerate(violence_probs)}
# Falconsai: {0: 'normal', 1: 'nsfw'}, Violence: {0: 'safe', 1: 'violence'}
is_nsfw = nsfw_probs.argmax() == 1 # 1 = nsfw
is_violent = violence_probs.argmax() == 1 # 1 = violence
flags = []
if is_nsfw:
flags.append(f"NSFW ({nsfw_results.get('nsfw', 0):.0%})")
if is_violent:
flags.append(f"Violence ({violence_results.get('violence', 0):.0%})")
if flags:
verdict = "❌ UNSAFE - " + ", ".join(flags)
else:
verdict = f"✅ SAFE (Normal: {nsfw_results.get('normal', 0):.0%}, Safe: {violence_results.get('safe', 0):.0%})"
return nsfw_results, violence_results, verdict
# ========================================
# GRADIO INTERFACE
# ========================================
demo = gr.Interface(
fn=moderate_image,
inputs=gr.Image(type="pil", label="Upload Image"),
outputs=[
gr.Label(label="NSFW Detection", num_top_classes=2),
gr.Label(label="Violence Detection", num_top_classes=2),
gr.Textbox(label="Overall Verdict")
],
title="🛡️ Multi-Head Content Moderator",
description="Upload an image to check for NSFW and Violence content simultaneously."
)
if __name__ == "__main__":
demo.launch() |