handler.py

import pickle
import numpy as np
from collections import Counter
import os

class EndpointHandler:
    def __init__(self, path=""):
        # path is the directory where the model files are located
        with open(os.path.join(path, "lang_id_model.pkl"), "rb") as f:
            self.model_data = pickle.load(f)
        
        self.W = self.model_data["W"]
        self.feature_map = self.model_data["feature_map"]
        self.ngram_length = self.model_data["ngram_length"]
        self.lang_list = self.model_data["lang_list"]

    def __call__(self, data):
        """
        data: A dictionary containing the 'inputs' key from the UI
        """
        inputs = data.pop("inputs", data)
        
        # 1. Vectorize text (Standard N-gram logic)
        ngrams = ["".join(s) for s in (zip(*[inputs[i:] for i in range(self.ngram_length)]))]
        counts = Counter(ngrams)
        
        x = np.zeros(len(self.feature_map))
        for ngram, count in counts.items():
            if ngram in self.feature_map:
                x[self.feature_map[ngram]] = count
        
        # 2. Add Bias and Compute Scores
        x_aug = np.insert(x, 0, 1)
        z = self.W.dot(x_aug)
        
        # 3. Numerical Stable Softmax
        probs = np.exp(z - np.max(z)) / np.exp(z - np.max(z)).sum()
        
        # 4. Return formatted list for the HF Widget
        return [{"label": self.lang_list[i], "score": float(probs[i])} 
                for i in range(len(self.lang_list))]