Create app.py

app.py

@@ -0,0 +1,72 @@
+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.")