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app.py

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