app.py

import os
import torch
import gradio as gr
from transformers import pipeline

# Load the models
Abuse_detector = pipeline("text-classification", model="Hate-speech-CNERG/english-abusive-MuRIL")
Hate_Speech_detector = pipeline("text-classification", model="cardiffnlp/twitter-roberta-base-hate-latest")
Profanity_detector = pipeline("text-classification", model="tarekziade/pardonmyai")

# Function to classify based on the user's choice
def classify_text(text, detection_type):
    if detection_type == "Abuse":
        result = Abuse_detector(text)
        if result[0]['label'] == 'LABEL_0':
            result[0]['label'] = "Not-abusive"
        elif result[0]['label'] == 'LABEL_1':
            result[0]['label'] = "Abusive"
    elif detection_type == "Hate Speech":
        result = Hate_Speech_detector(text)

    elif detection_type == "Profanity":
        result = Profanity_detector(text)

    return result[0]

# Gradio interface
interface = gr.Interface(
    fn=classify_text,
    inputs=[gr.Textbox(label="Enter text"), gr.Radio(["Abuse", "Hate Speech", "Profanity"], label="Detection Type")],
    outputs="json",
    title="Abuse, Hate Speech, and Profanity Detection",
    description="Enter text and select the type of detection you want."
)

# Launch the Gradio app
if __name__ == "__main__":
    interface.launch(share=True)