app.py

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

# Install required packages
# !pip install torch transformers gradio matplotlib

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

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

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

# Toxicity category labels
labels = ["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"]

# Function to predict toxicity
def predict_toxicity(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)
        logits = outputs.logits
        probabilities = torch.sigmoid(logits).cpu().numpy()[0]
    
    toxicity_scores = {label: float(probabilities[i]) for i, label in enumerate(labels)}
    return toxicity_scores

# Function to create a bar chart
def plot_toxicity(comment):
    toxicity_scores = predict_toxicity(comment)
    categories = list(toxicity_scores.keys())
    scores = list(toxicity_scores.values())
    
    plt.figure(figsize=(12, 7), dpi=300, facecolor='black')
    ax = plt.gca()
    ax.set_facecolor('black')
    bars = plt.bar(categories, scores, color='#20B2AA', edgecolor='white', width=0.5)  # Sea green
    
    plt.xticks(color='white', fontsize=14, rotation=25, ha='right')
    plt.yticks(color='white', fontsize=14)
    plt.title("Toxicity Score Analysis", color='white', fontsize=16)
    plt.ylim(0, 1.1)
    
    for bar in bars:
        yval = bar.get_height()
        plt.text(bar.get_x() + bar.get_width()/2, yval + 0.03, f'{yval:.2f}', ha='center', color='white', fontsize=12, fontweight='bold')
    
    plt.tight_layout(pad=2)
    plt.savefig("toxicity_chart.png", facecolor='black', bbox_inches='tight')
    plt.close()
    
    return "toxicity_chart.png"

# Gradio UI
demo = gr.Interface(
    fn=plot_toxicity,
    inputs=gr.Textbox(label="Enter a comment"),
    outputs=gr.Image(type="filepath", label="Toxicity Analysis"),
    title="Toxicity Detector",
    description="Enter a comment to analyze its toxicity scores across different categories.",
)

# Launch the Gradio app
if __name__ == "__main__":
    demo.launch()