Update app.py

app.py

@@ -1,10 +1,13 @@
+import torch
 import streamlit as st
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-model = AutoModelForSequenceClassification.from_pretrained("sentence-transformers/all-distilroberta-v1")
+# load the pre-trained and fine-tuned model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained('nlptown/bert-base-multilingual-uncased-sentiment')
+model = AutoModelForSequenceClassification.from_pretrained('nlptown/bert-base-multilingual-uncased-sentiment')
 
 # set the app title
-st.title("Sentence Similarity Checker")
+st.title("Brazilian Portuguese Sentence Similarity Checker")
 
 # get the input sentences from the user
 sentence1 = st.text_input("Enter the first sentence:")
@@ -12,12 +15,16 @@ sentence2 = st.text_input("Enter the second sentence:")
 
 # check if both sentences are not empty
 if sentence1 and sentence2:
-    # encode the sentences into embeddings
-    embeddings1 = model.encode(sentence1, convert_to_tensor=True)
-    embeddings2 = model.encode(sentence2, convert_to_tensor=True)
+    # tokenize the sentences and get their IDs
+    input_ids = tokenizer.encode(sentence1, sentence2, truncation=True, padding=True, return_tensors='pt')
     
-    # calculate the cosine similarity between the embeddings
-    similarity_score = float(embeddings1 @ embeddings2.T)
+    # pass the IDs through the model to get the logits
+    with torch.no_grad():
+        logits = model(input_ids)[0]
     
-    # display the similarity score to the user
-    st.write("Similarity score:", similarity_score)
+    # apply softmax to the logits to get the predicted probabilities
+    probs = torch.softmax(logits, dim=1).squeeze().tolist()
+    
+    # display the predicted probabilities to the user
+    st.write("Probability that the sentences are similar:", probs[1])
+    st.write("Probability that the sentences are dissimilar:", probs[0])