app.py

import os
os.system("pip install torch transformers gradio matplotlib")


import torch
import gradio as gr
import numpy as np
import matplotlib.pyplot as plt
from transformers import AutoTokenizer, AutoModelForSequenceClassification

torch.set_num_threads(torch.get_num_threads())

# Load the trained model and tokenizer from Hugging Face Hub
model_path = "HyperX-Sentience/RogueBERT-Toxicity-85K"
model = AutoModelForSequenceClassification.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)

# Move the model to CUDA if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# Define toxicity labels
labels = ["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"]

def predict_toxicity(comment):
    """Predicts the toxicity levels of a given comment."""
    inputs = tokenizer(comment, truncation=True, padding="max_length", max_length=128, return_tensors="pt")
    inputs = {key: val.to(device) for key, val in inputs.items()}
    
    with torch.no_grad():
        outputs = model(**inputs)
        probabilities = torch.sigmoid(outputs.logits).cpu().numpy()[0]
    
    return {labels[i]: float(probabilities[i]) for i in range(len(labels))}

import pandas as pd

def format_toxicity_data(comment):
    """Formats the toxicity scores for a modern bar graph."""
    scores = predict_toxicity(comment)
    df = pd.DataFrame(list(scores.items()), columns=["Category", "Score"])
    return df


# Gradio interface
demo = gr.Interface(
    fn=format_toxicity_data,
    inputs=gr.Textbox(label="Enter a comment:"),
    outputs=gr.BarPlot(
        x="Category",
        y="Score",
        title="Toxicity Analysis",
        y_lim=[0, 1],
        color="blue",
        label="Toxicity Scores",
        interactive=False
    ),
    title="Toxicity Detection with RogueBERT",
    description="Enter a comment to analyze its toxicity levels. The results will be displayed as a modern bar chart."
)

demo.launch()