Update app.py

app.py

@@ -1,96 +1,96 @@
-import streamlit as st
-import torch
-from transformers import AutoTokenizer, AutoModel
-from sentence_transformers import util
-class SentenceSimiliarity():
+# import streamlit as st
+# import torch
+# from transformers import AutoTokenizer, AutoModel
+# from sentence_transformers import util
+# class SentenceSimiliarity():
 
-    def __init__(self, model_name, sentence1, sentence2):
-        self.sentence1 = sentence1
-        self.sentence2 = sentence2
-        self.model_name = model_name
-        self.model = AutoModel.from_pretrained(self.model_name)
-        self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
+#     def __init__(self, model_name, sentence1, sentence2):
+#         self.sentence1 = sentence1
+#         self.sentence2 = sentence2
+#         self.model_name = model_name
+#         self.model = AutoModel.from_pretrained(self.model_name)
+#         self.tokenizer = AutoTokenizer.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 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_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 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
+#     def results(self):
+#         scores = self.get_similarity_scores()
+#         statement = f"The sentence has {scores.item() * 100:.2f}% similarity"
+#         return statement
     
 
-class UI():
+# 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 __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 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 model_selection(self):
-        available_models = [
-            "distilbert-base-uncased",
-            "bert-base-uncased",
-            "sentence-transformers/all-MiniLM-L6-v2",
-            # "sentence-transformers/all-mpnet-base-v2",
-            # "intfloat/multilingual-e5-base",
-            # "togethercomputer/m2-bert-80M-32k-retrieval",
-            # "togethercomputer/m2-bert-80M-8k-retrieval",
-            # "togethercomputer/m2-bert-80M-2k-retrieval",
-        ]
-        model_name = st.sidebar.selectbox(
-            label="Select Your Models",
-            options=available_models,
-        )
-        return model_name
+#     def model_selection(self):
+#         available_models = [
+#             "distilbert-base-uncased",
+#             "bert-base-uncased",
+#             "sentence-transformers/all-MiniLM-L6-v2",
+#             # "sentence-transformers/all-mpnet-base-v2",
+#             # "intfloat/multilingual-e5-base",
+#             # "togethercomputer/m2-bert-80M-32k-retrieval",
+#             # "togethercomputer/m2-bert-80M-8k-retrieval",
+#             # "togethercomputer/m2-bert-80M-2k-retrieval",
+#         ]
+#         model_name = st.sidebar.selectbox(
+#             label="Select Your Models",
+#             options=available_models,
+#         )
+#         return model_name
         
 
-    def result(self):
-        self.get()
-        model_name = self.model_selection()
-        ss = SentenceSimiliarity(model_name, self.sentence1, self.sentence2)
+#     def result(self):
+#         self.get()
+#         model_name = self.model_selection()
+#         ss = SentenceSimiliarity(model_name, self.sentence1, self.sentence2)
         
-        if self.button:
-            st.text(ss.results())
-        # print(ss.results())
+#         if self.button:
+#             st.text(ss.results())
+#         # print(ss.results())
 
-ui = UI()
-ui.result()
+# ui = UI()
+# ui.result()