app.py

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()