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import torch |
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import streamlit as st |
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from transformers import RobertaTokenizer, RobertaForSequenceClassification |
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import re |
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import string |
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def tokenize_sentences(sentence): |
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encoded_dict = tokenizer.encode_plus( |
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sentence, |
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add_special_tokens=True, |
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max_length=128, |
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padding='max_length', |
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truncation=True, |
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return_attention_mask=True, |
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return_tensors='pt' |
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) |
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return torch.cat([encoded_dict['input_ids']], dim=0), torch.cat([encoded_dict['attention_mask']], dim=0) |
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def preprocess_query(query): |
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query = str(query).lower() |
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query = query.strip() |
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query=query.translate(str.maketrans("", "", string.punctuation)) |
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return query |
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def predict_aspects(sentence, threshold): |
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input_ids, attention_mask = tokenize_sentences(sentence) |
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with torch.no_grad(): |
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outputs = aspects_model(input_ids, attention_mask=attention_mask) |
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logits = outputs.logits |
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predicted_aspects = torch.sigmoid(logits).squeeze().tolist() |
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results = dict() |
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for label, prediction in zip(LABEL_COLUMNS_ASPECTS, predicted_aspects): |
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if prediction < threshold: |
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continue |
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precentage = round(float(prediction) * 100, 2) |
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results[label] = precentage |
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return results |
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BERT_MODEL_NAME_FOR_ASPECTS_CLASSIFICATION = 'roberta-large' |
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tokenizer = RobertaTokenizer.from_pretrained(BERT_MODEL_NAME_FOR_ASPECTS_CLASSIFICATION, do_lower_case=True) |
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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'] |
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aspects_model = RobertaForSequenceClassification.from_pretrained(BERT_MODEL_NAME_FOR_ASPECTS_CLASSIFICATION, num_labels=len(LABEL_COLUMNS_ASPECTS)) |
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aspects_model.load_state_dict(torch.load('./Aspects_Extraction_Model_updated.pth', map_location=torch.device('cpu')), strict=False) |
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aspects_model.eval() |
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st.title("Implicit and Explicit Aspect Extraction") |
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sentence = st.text_input("Enter a sentence:") |
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threshold = st.slider("Threshold", min_value=0.0, max_value=1.0, step=0.01, value=0.5) |
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if sentence: |
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processed_sentence = preprocess_query(sentence) |
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results = predict_aspects(processed_sentence, threshold) |
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if len(results) > 0: |
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st.write("Predicted Aspects:") |
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table_data = [["Category","Aspect", "Probability"]] |
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for aspect, percentage in results.items(): |
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aspect_parts = aspect.split("-") |
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table_data.append(aspect_parts + [f"{percentage}%"]) |
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st.table(table_data) |
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else: |
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st.write("No aspects above the threshold.") |
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