import random
import numpy as np
import torch
import logging
from transformers import pipeline
from autocorrect import Speller
# Load a pre-trained SBERT model

# Set seeds for reproducibility of zero-shot classification
def set_seed(seed):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False

set_seed(1)


# Load a pre-trained model and tokenizer
classifier = pipeline("zero-shot-classification", model="roberta-large-mnli")
spell = Speller()

# Classify item as food or non-food
def classify_as_item_or_category(item):
    cleaned_item = item.strip().lower()
    spell_fix_item = spell(cleaned_item)
    result = classifier(spell_fix_item, candidate_labels=["single food item", "food category"])
    label = result["labels"][0]
    score = result["scores"][0]

    # logging.info(f"Item: {item}, Label: {label}, Score: {score}")
    return label, score