Delete app.py

app.py

@@ -1,93 +0,0 @@
-import torch
-import streamlit as st
-from transformers import RobertaTokenizer, RobertaForSequenceClassification
-import re
-import string
- 
-
-
-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 preprocess_query(query):
-    query = str(query).lower()
-    query = query.strip()
-    query=query.translate(str.maketrans("", "", string.punctuation))
-    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-base'
-tokenizer = RobertaTokenizer.from_pretrained(BERT_MODEL_NAME_FOR_ASPECTS_CLASSIFICATION, do_lower_case=True)
-
-# Define the aspect labels
-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'
-]
-
-# Load the model with the specified number of labels
-aspects_model = RobertaForSequenceClassification.from_pretrained(
-    BERT_MODEL_NAME_FOR_ASPECTS_CLASSIFICATION, 
-    num_labels=len(LABEL_COLUMNS_ASPECTS)
-)
-
-# Load the state dictionary
-aspects_model.load_state_dict(torch.load('./Restaurant_Reviews (1).tsv',map_location=torch.device('cpu')),strict=False)
-
-# Set the model to evaluation mode
-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:")
-        table_data = [["Category","Aspect", "Probability"]]
-        for aspect, percentage in results.items():
-            aspect_parts = aspect.split("-")
-            table_data.append(aspect_parts + [f"{percentage}%"])
-        st.table(table_data)
-    else:
-        st.write("No aspects above the threshold.")
-