import numpy as np
import spacy
from transformers import pipeline, BertTokenizer, BertForSequenceClassification
from scipy.spatial.distance import cosine
from nltk.corpus import stopwords, wordnet as wn
import tensorflow_hub as hub
import tensorflow as tf
from nltk.tokenize import word_tokenize
import nltk
from itertools import combinations, product
import logging
import os

# Load fine-tuned BERT model and tokenizer
model_path = 'fine_tuned_bert'
tokenizer = BertTokenizer.from_pretrained(model_path)
model = BertForSequenceClassification.from_pretrained(model_path)
classifier = pipeline('text-classification', model=model, tokenizer=tokenizer, return_all_scores=True)

def softmax(logits):
    e_logits = np.exp(logits - np.max(logits))
    return e_logits / e_logits.sum()

sentence = "This pic is tough"
original_inputs = tokenizer(sentence, return_tensors='pt')
original_logits = model(**original_inputs).logits.detach().numpy()
original_softmax = softmax(original_logits)
original_confidence = np.max(original_softmax)
original_prediction = np.argmax(original_softmax)

print("Original prediction:", original_prediction)
print("Original confidence:", original_confidence)
print("Original logits:", original_logits)