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from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
class EndpointHandler:
def __init__(self, path=""):
# Load model and tokenizer from the repo path
self.tokenizer = AutoTokenizer.from_pretrained(path)
self.model = AutoModelForSequenceClassification.from_pretrained(path)
self.model.eval()
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.model.to(self.device)
def __call__(self, data):
"""
This method is called when the endpoint receives a request.
Expected input: { "inputs": "some string" } or { "inputs": ["a", "b", ...] }
"""
inputs = data.get("inputs", None)
if inputs is None:
return {"error": "No input provided"}
if isinstance(inputs, str):
inputs = [inputs]
results = []
for text in inputs:
encoded = self.tokenizer(
text,
return_tensors="pt",
truncation=True,
padding="max_length",
max_length=4096,
)
encoded = {k: v.to(self.device) for k, v in encoded.items()}
with torch.no_grad():
outputs = self.model(**encoded)
raw_score = outputs.logits.squeeze().item()
clipped_score = min(max(raw_score, 0.0), 1.0)
results.append({"score": round(clipped_score, 4)})
return results
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