handler.py

from typing import Dict, Any
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import re

class EndpointHandler():
    def __init__(self, path=""):
        self.tokenizer = AutoTokenizer.from_pretrained(path)
        self.model = AutoModelForSequenceClassification.from_pretrained(path)
        self.model.eval()
        self.id2label = {0: "Human", 1: "Mixed", 2: "AI"}
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.model.to(self.device)

    def split_into_sentences(self, text: str):
        sentences = re.split(r'(?<=[.!?])\s+', text)
        return [s.strip() for s in sentences if s.strip()]

    def get_token_predictions(self, text: str):
        tokens = self.tokenizer.tokenize(text)
        token_predictions = []
        for i in range(len(tokens)):
            start = max(0, i - 10)
            end = min(len(tokens), i + 10)
            context = self.tokenizer.convert_tokens_to_string(tokens[start:end])
            inputs = self.tokenizer(context, return_tensors="pt", truncation=True, max_length=512)
            inputs = {k: v.to(self.device) for k, v in inputs.items()}
            with torch.no_grad():
                outputs = self.model(**inputs)
                probs = torch.softmax(outputs.logits, dim=1)
                ai_prob = probs[0][2].item()
            token = tokens[i].replace("Ġ", " ").replace("▁", " ").replace("Ċ", " ").strip()
            if token:
                token_predictions.append({"token": token, "ai_prob": ai_prob})
        return token_predictions

    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
        text = data.get("inputs", "")
        # Document level
        inputs = self.tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
        inputs = {k: v.to(self.device) for k, v in inputs.items()}
        with torch.no_grad():
            outputs = self.model(**inputs)
            probs = torch.softmax(outputs.logits, dim=1)
            pred = torch.argmax(probs, dim=1).item()
            doc_result = {
                "prediction": self.id2label[pred],
                "confidence": probs[0][pred].item(),
                "probabilities": {self.id2label[i]: float(p) for i, p in enumerate(probs[0])}
            }
        # Sentence level
        sentences = self.split_into_sentences(text)
        sent_results = []
        for sent in sentences:
            inputs = self.tokenizer(sent, return_tensors="pt", truncation=True, max_length=512)
            inputs = {k: v.to(self.device) for k, v in inputs.items()}
            with torch.no_grad():
                outputs = self.model(**inputs)
                probs = torch.softmax(outputs.logits, dim=1)
                pred = torch.argmax(probs, dim=1).item()
            sent_results.append({
                "sentence": sent,
                "prediction": self.id2label[pred],
                "confidence": probs[0][pred].item(),
                "probabilities": {self.id2label[i]: float(p) for i, p in enumerate(probs[0])}
            })
        # Token level
        token_results = self.get_token_predictions(text)
        return [{
            "document": doc_result,
            "sentences": sent_results,
            "tokens": token_results
        }]