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import os |
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os.system("pip install torch transformers gradio matplotlib") |
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import torch |
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import gradio as gr |
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import numpy as np |
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import matplotlib.pyplot as plt |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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torch.set_num_threads(torch.get_num_threads()) |
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model_path = "HyperX-Sentience/RogueBERT-Toxicity-85K" |
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model = AutoModelForSequenceClassification.from_pretrained(model_path) |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model.to(device) |
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labels = ["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"] |
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def predict_toxicity(comment): |
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"""Predicts the toxicity levels of a given comment.""" |
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inputs = tokenizer(comment, truncation=True, padding="max_length", max_length=128, return_tensors="pt") |
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inputs = {key: val.to(device) for key, val in inputs.items()} |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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probabilities = torch.sigmoid(outputs.logits).cpu().numpy()[0] |
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return {labels[i]: float(probabilities[i]) for i in range(len(labels))} |
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def visualize_toxicity(comment): |
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"""Generates a bar chart showing toxicity levels.""" |
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scores = predict_toxicity(comment) |
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plt.figure(figsize=(6, 4)) |
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plt.bar(scores.keys(), scores.values(), color=['blue', 'red', 'green', 'purple', 'orange', 'brown']) |
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plt.ylim(0, 1) |
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plt.ylabel("Toxicity Score") |
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plt.title("Toxicity Analysis") |
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plt.xticks(rotation=45) |
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plt.grid(axis='y', linestyle='--', alpha=0.7) |
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plt.savefig("toxicity_plot.png") |
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plt.close() |
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return "toxicity_plot.png" |
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demo = gr.Interface( |
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fn=visualize_toxicity, |
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inputs=gr.Textbox(label="Enter a comment:"), |
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outputs=gr.Image(type="file", label="Toxicity Scores"), |
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title="Toxicity Detection with RogueBERT", |
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description="Enter a comment to analyze its toxicity levels. The results will be displayed as a bar chart." |
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) |
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demo.launch() |
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