model.py

from typing import List, Dict
import numpy as np
import torch
from transformers import BertForSequenceClassification, BertTokenizer
from sklearn.preprocessing import LabelEncoder
from huggingface_hub import hf_hub_download

class CustomBertClassifier:
    def __init__(self):
        # Load model and tokenizer
        self.model = BertForSequenceClassification.from_pretrained(".")
        self.tokenizer = BertTokenizer.from_pretrained(".")
        self.model.eval()

        # Load label classes
        label_path = hf_hub_download(repo_id="JaySenpai/bert-model", filename="label_classes.npy")
        self.le = LabelEncoder()
        self.le.classes_ = np.load(label_path, allow_pickle=True)

    def __call__(self, inputs: str) -> List[Dict]:
        # Tokenize input
        inputs = self.tokenizer(inputs, return_tensors="pt", truncation=True, padding=True)
        with torch.no_grad():
            outputs = self.model(**inputs)
        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
        probs = probs[0].tolist()

        # Map to labels
        results = []
        for i, prob in enumerate(probs):
            results.append({
                "label": self.le.classes_[i],
                "score": round(prob, 4)
            })
        # Sort by score descending
        results = sorted(results, key=lambda x: x["score"], reverse=True)
        return results