src/application/text/ai_classification.py

from typing import (
    Dict,
    List,
    Tuple,
)

import torch
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
)

from src.application.config import AI_TEXT_CLASSIFICATION_MODEL


def load_model_and_tokenizer(
    model_path: str = AI_TEXT_CLASSIFICATION_MODEL,
) -> Tuple[AutoTokenizer, AutoModelForSequenceClassification]:
    """
    Loads the trained model and tokenizer from the specified path.

    Args:
        model_path: path of directory containing the saved model and tokenizer.

    Returns:
        A tuple containing the loaded tokenizer and model.
    """
    tokenizer = AutoTokenizer.from_pretrained(model_path)
    model = AutoModelForSequenceClassification.from_pretrained(model_path)
    model.eval()
    return tokenizer, model


def predict(
    texts: List[str],
    model: AutoModelForSequenceClassification,
    tokenizer: AutoTokenizer,
) -> List[Dict[str, str]]:
    """
    Classify on input texts into gpt-4o or gpt-4o-mini.

    Args:
        texts: A list of input text strings to be classified.
        model: The loaded model for sequence classification.
        tokenizer: The loaded tokenizer.

    Returns:
        A list of dictionaries, where each dictionary contains the input text,
        the predicted label, and the confidence score.
    """
    label_map = {0: "GPT-4o", 1: "GPT-4o mini"}
    inputs = tokenizer(
        texts,
        padding="max_length",
        truncation=True,
        return_tensors="pt",
    )
    with torch.no_grad():
        outputs = model(**inputs)
    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
    confidence, predictions = torch.max(probabilities, dim=-1)

    results = []
    for text, pred, conf in zip(
        texts,
        predictions.tolist(),
        confidence.tolist(),
    ):
        results.append(
            {"input": text, "prediction": label_map[pred], "confidence": conf},
        )

    return results


if __name__ == "__main__":
    text = """The resignation brings a long political chapter to an end.
Trudeau has been in office since 2015, when he brought the Liberals back
to power from the political wilderness.
"""

    tokenizer, model = load_model_and_tokenizer("ductuan024/gpts-detector")
    predictions = predict(text, model, tokenizer)

    print(predictions[0]["prediction"])
    print(predictions[0]["confidence"])