File size: 3,427 Bytes
2362d45 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 |
import torch
import nltk
import streamlit as st
from transformers import RobertaTokenizer, RobertaForSequenceClassification
nltk.download('punkt')
def tokenize_sentences(sentence):
encoded_dict = tokenizer.encode_plus(
sentence,
add_special_tokens=True,
max_length=128,
padding='max_length',
truncation=True,
return_attention_mask=True,
return_tensors='pt'
)
return torch.cat([encoded_dict['input_ids']], dim=0), torch.cat([encoded_dict['attention_mask']], dim=0)
def remove_stop_words(sentence):
words = nltk.word_tokenize(sentence)
custom_words = ['recommend', 'having', 'Hello', 'best', 'restaurant', 'top', 'want', 'need', 'well', 'most', 'should', 'be', 'good', 'also']
stop_words.update(custom_words)
words_without_stopwords = [word for word in words if word.lower() not in stop_words]
sentence_without_stopwords = ' '.join(words_without_stopwords)
return sentence_without_stopwords
def preprocess_query(query):
query = str(query).lower()
query = query.strip()
query = remove_stop_words(query)
return query
def predict_aspects(sentence, threshold):
input_ids, attention_mask = tokenize_sentences(sentence)
with torch.no_grad():
outputs = aspects_model(input_ids, attention_mask=attention_mask)
logits = outputs.logits
predicted_aspects = torch.sigmoid(logits).squeeze().tolist()
results = dict()
for label, prediction in zip(LABEL_COLUMNS_ASPECTS, predicted_aspects):
if prediction < threshold:
continue
precentage = round(float(prediction) * 100, 2)
results[label] = precentage
return results
# Load tokenizer and model
BERT_MODEL_NAME_FOR_ASPECTS_CLASSIFICATION = 'roberta-large'
tokenizer = RobertaTokenizer.from_pretrained(BERT_MODEL_NAME_FOR_ASPECTS_CLASSIFICATION, do_lower_case=True)
LABEL_COLUMNS_ASPECTS = ['FOOD-CUISINE', 'FOOD-DEALS', 'FOOD-DIET_OPTION', 'FOOD-EXPERIENCE', 'FOOD-FLAVOR', 'FOOD-GENERAL', 'FOOD-INGREDIENT', 'FOOD-KITCHEN', 'FOOD-MEAL', 'FOOD-MENU', 'FOOD-PORTION', 'FOOD-PRESENTATION', 'FOOD-PRICE', 'FOOD-QUALITY', 'FOOD-RECOMMENDATION', 'FOOD-TASTE', 'GENERAL-GENERAL', 'RESTAURANT-ATMOSPHERE', 'RESTAURANT-BUILDING', 'RESTAURANT-DECORATION', 'RESTAURANT-EXPERIENCE', 'RESTAURANT-FEATURES', 'RESTAURANT-GENERAL', 'RESTAURANT-HYGIENE', 'RESTAURANT-KITCHEN', 'RESTAURANT-LOCATION', 'RESTAURANT-OPTIONS', 'RESTAURANT-RECOMMENDATION', 'RESTAURANT-SEATING_PLAN', 'RESTAURANT-VIEW', 'SERVICE-BEHAVIOUR', 'SERVICE-EXPERIENCE', 'SERVICE-GENERAL', 'SERVICE-WAIT_TIME']
aspects_model = RobertaForSequenceClassification.from_pretrained(BERT_MODEL_NAME_FOR_ASPECTS_CLASSIFICATION, num_labels=len(LABEL_COLUMNS_ASPECTS))
aspects_model.load_state_dict(torch.load('./Aspects_Extraction_Model_updated.pth', map_location=torch.device('cpu')))
aspects_model.eval()
# Streamlit App
st.title("Implicit and Explicit Aspect Extraction")
sentence = st.text_input("Enter a sentence:")
threshold = st.slider("Threshold", min_value=0.0, max_value=1.0, step=0.01, value=0.5)
if sentence:
processed_sentence = preprocess_query(sentence)
results = predict_aspects(processed_sentence, threshold)
if len(results)>0:
st.write("Predicted Aspects:")
for aspect, percentage in results.items():
st.write(f"- {aspect}: {percentage}%")
else:
st.write("No aspects above the threshold.")
|