app.py

import pandas as pd
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Load the pre-trained BERT model and tokenizer
model_name = "bert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=6)

# Load the data
data = pd.read_csv("toxic_comments.csv")

# Define the function to preprocess the text
def preprocess(text):
    inputs = tokenizer(text, padding=True, truncation=True, max_length=128, return_tensors="pt")
    return inputs["input_ids"], inputs["attention_mask"]

# Define the function to classify a text input
def classify(text):
    input_ids, attention_mask = preprocess(text)
    with torch.no_grad():
        logits = model(input_ids, attention_mask=attention_mask).logits
    preds = torch.sigmoid(logits).squeeze().tolist()
    return {labels[i]: preds[i] for i in range(len(labels))}

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

# Classify the comments and print the results
for i, row in data.iterrows():
    text = row["comment_text"]
    preds = classify(text)
    print("Comment: ", text)
    print("Predictions: ", preds)
    print("Labels: ", row[labels].to_dict())