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import streamlit as st |
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import torch |
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from transformers import AutoTokenizer, AutoModel |
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from sentence_transformers import util |
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class SentenceSimiliarity(): |
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def __init__(self, sentence1, sentence2): |
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self.sentence1 = sentence1 |
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self.sentence2 = sentence2 |
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self.model_name = "bert-base-uncased" |
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self.tokenizer = AutoTokenizer.from_pretrained(self.model_name) |
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self.model = AutoModel.from_pretrained(self.model_name) |
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def tokenize(self): |
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tokenized1 = self.tokenizer( |
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self.sentence1, |
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return_tensors='pt', |
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padding=True, |
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truncation=True |
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) |
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tokenized2 = self.tokenizer( |
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self.sentence2, |
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return_tensors='pt', |
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padding=True, |
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truncation=True |
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) |
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return tokenized1, tokenized2 |
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def get_embeddings(self): |
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tokenized1, tokenized2 = self.tokenize() |
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with torch.no_grad(): |
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embeddings1 = self.model(**tokenized1).last_hidden_state.mean(dim=1) |
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embeddings2 = self.model(**tokenized2).last_hidden_state.mean(dim=1) |
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return embeddings1, embeddings2 |
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def get_similarity_scores(self): |
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embeddings1, embeddings2 = self.get_embeddings() |
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scores = util.cos_sim(embeddings1, embeddings2) |
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return scores |
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def results(self): |
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scores = self.get_similarity_scores() |
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statement = f"The sentence has {scores.item() * 100:.2f}% similarity" |
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return statement |
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class UI(): |
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def __init__(self): |
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st.title("Sentence Similiarity Checker") |
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st.caption("You can use this for checking similarity between resume and job description") |
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def get(self): |
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self.sentence1 = st.text_area( |
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label="Sentence 1", |
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help="This is a parent text the next text will be compared with this text" |
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) |
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self.sentence2 = st.text_area( |
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label="Sentence 2", |
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help="This is a child text" |
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) |
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self.button = st.button( |
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label="Check", |
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help='Check Sentence Similarity' |
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) |
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def result(self): |
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self.get() |
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ss = SentenceSimiliarity(self.sentence1, self.sentence2) |
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if self.button: |
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st.text(ss.results()) |
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ui = UI() |
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ui.result() |
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