# 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 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 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)
        
#         if self.button:
#             st.text(ss.results())
#         # print(ss.results())

# ui = UI()
# ui.result()