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import gradio as gr
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
# Load the model and tokenizer from Hugging Face Hub
model_name = "vai0511/ai-content-classifier"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define function for classification
def classify_text(text):
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
predicted_class = torch.argmax(logits, dim=1).item()
labels = {0: "Human-Written", 1: "AI-Generated", 2: "Paraphrased"}
return labels[predicted_class]
# Gradio Interface
iface = gr.Interface(
fn=classify_text,
inputs=gr.Textbox(lines=5, placeholder="Enter your text here..."),
outputs="text",
title="AI-Driven Content Source Identification",
description="Detect whether the given text is human-written, AI-generated, or paraphrased."
)
iface.launch()
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