| 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", "") |
| |
| 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])} |
| } |
| |
| 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_results = self.get_token_predictions(text) |
| return [{ |
| "document": doc_result, |
| "sentences": sent_results, |
| "tokens": token_results |
| }] |
