svm_predict.py

import nltk
from nltk import word_tokenize
from nltk import pos_tag
import joblib
from train import feature_vector, pos_tags

model = joblib.load('SVM_NEI_model.pkl')
nltk.download('averaged_perceptron_tagger_eng')
nltk.download('punkt_tab')

def predict(sentence):
    tokens = word_tokenize(sentence)
    sent_pos_tags = pos_tag(tokens)
    predictions = []
    l = len(tokens)
    for idx, word in enumerate(tokens):
        # prev_tag = -1 if idx==0 else sent_pos_tags[idx-1][1]
        # next_tag = -1 if idx==len(tokens)-1 else sent_pos_tags[idx+1][1]
        # current_tag = sent_pos_tags[idx][1]
        # prev_idx = pos_tags.index(prev_tag) if prev_tag in pos_tags else -1
        # next_idx = pos_tags.index(next_tag) if next_tag in pos_tags else -1
        # current_idx = pos_tags.index(current_tag) if current_tag in pos_tags else -1
        # vec = feature_vector(word, prev_idx, next_idx, current_idx)
        vec = feature_vector(word, idx/l, sent_pos_tags[idx][1])
        y_pred = model.predict([vec])
        predictions.append(round(y_pred[0]))
    return tokens, predictions