app.py

import torch
import gradio as gr
from transformers import pipeline

# Load the model
moderator = pipeline("text-classification", model="KoalaAI/Text-Moderation")

# Visual styling logic
def moderate_text(input_text):
    result = moderator(input_text)
    label = result[0]['label']
    score = round(result[0]['score'] * 100, 2)

    # Set color and emoji
    if label == "toxic":
        color = "#FF4C4C"       # Bright red
        emoji = "😡"
        message = "⚠️ Toxic content detected"
    elif label == "not-toxic":
        color = "#4CAF50"       # Green
        emoji = "😊"
        message = "✅ Content is safe"
    else:
        color = "#FFD700"       # Gold for unsure
        emoji = "😐"
        message = "⚠️ Uncertain classification"

    # HTML-formatted response
    html_output = f"""
    <div style='padding:1em;border-radius:10px;background-color:{color};color:white;font-weight:bold;font-size:16px'>
        {emoji} {message} <br>
        Confidence Score: {score}%
    </div>
    """
    return html_output

# Gradio interface
#demo = gr.Interface(fn=moderate_text, inputs="text", outputs="text", title="AISA - Text Moderation", description="Enter your message in **English or Tamil** to check if it's safe or toxic. :)")
demo = gr.Interface(
    fn=moderate_text,
    inputs="text",
    outputs=gr.HTML(),
    title="AISA - Text Moderation",
    description="Enter your message in **English or Tamil** to check if it's safe or toxic. 😊"
)

demo.launch()