Update app.py

app.py

@@ -1,96 +1,135 @@
-# import streamlit as st
-# import torch
+import os
+import requests
+import streamlit as st
+import torch
 # from transformers import AutoTokenizer, AutoModel
 # from sentence_transformers import util
-# class SentenceSimiliarity():
+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.KEY = os.getenv("HF_KEY")
+        self.headers = {"Authorization": f"Bearer {self.KEY}"}
+        self.sentence1 = sentence1
+        self.sentence2 = sentence2
+        self.api_url = f"https://api-inference.huggingface.co/models/{model_name}"
+        
+        # self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
+        # self.model = AutoModel.from_pretrained(self.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",
+            "sentence-transformers/distiluse-base-multilingual-cased-v2",
+            "intfloat/e5-small",
+            "intfloat/e5-base",
+            "intfloat/e5-large-v2",
+            "intfloat/multilingual-e5-base",
+            # "togethercomputer/m2-bert-80M-32k-retrieval",
+            # "togethercomputer/m2-bert-80M-8k-retrieval",
+            # "togethercomputer/m2-bert-80M-2k-retrieval",
+        ]
+        self.model_name = st.sidebar.selectbox(
+            label="Select Your Models",
+            options=available_models,
+        )
+        
 
-#     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 query(self, payload):
+        response = requests.post(self.api_url, headers=self.headers, json=payload)
+        return response.json()
+
+    def results(self):
+        scores = self.query({
+            "inputs": {
+                "source_sentence": self.sentence1,
+                "sentences": [
+                    self.sentence2,
+                ]
+            },
+        })
+        # scores = self.get_similarity_scores()
+        statement = f"The sentence has {scores[0] * 100:.2f}% similarity"
+        # statement = scores
+        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):
+        available_models = [
+            # "distilbert-base-uncased",
+            # "bert-base-uncased",
+            "sentence-transformers/all-MiniLM-L6-v2",
+            "sentence-transformers/all-mpnet-base-v2",
+            "sentence-transformers/distiluse-base-multilingual-cased-v2",
+            "intfloat/e5-small",
+            "intfloat/e5-base",
+            "intfloat/e5-large-v2",
+            "intfloat/multilingual-e5-base",
+            # "togethercomputer/m2-bert-80M-32k-retrieval",
+            # "togethercomputer/m2-bert-80M-8k-retrieval",
+            # "togethercomputer/m2-bert-80M-2k-retrieval",
+        ]
+        self.model_name = st.sidebar.selectbox(
+            label="Select Your Models",
+            options=available_models,
+        )
 
-#     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
-        
+        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()
-#         model_name = self.model_selection()
-#         ss = SentenceSimiliarity(model_name, self.sentence1, self.sentence2)
+    def result(self):
+        self.get()
+        ss = SentenceSimiliarity(self.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()