add streamlit

app.py

@@ -1,22 +1,30 @@
+import streamlit as st
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
+# load the pre-trained model and tokenizer
 tokenizer = AutoTokenizer.from_pretrained("distilbert-base-multilingual-cased")
 model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-multilingual-cased")
 
-# example sentences
-sentence1 = "O Brasil é o maior país da América do Sul"
-sentence2 = "A Argentina é o segundo maior país da América do Sul"
-
-# tokenize the sentences
-inputs = tokenizer(sentence1, sentence2, padding=True, truncation=True, max_length=250, return_tensors="pt")
-
-# get the output logits for the sentence pair classification task
-outputs = model(**inputs).logits
-
-# calculate the softmax probabilities for the two classes (similar or dissimilar)
-probs = outputs.softmax(dim=1)
-
-# the probability of the sentences being similar is the second element of the output array
-similarity_score = probs[0][1].item()
-
-print("Similarity score:", similarity_score)
+# define the Streamlit app
+def app():
+    # set the app title
+    st.title("Sentence Similarity Checker")
+    
+    # get the input sentences from the user
+    sentence1 = st.text_input("Enter the first sentence:")
+    sentence2 = st.text_input("Enter the second sentence:")
+    
+    # check if both sentences are not empty
+    if sentence1 and sentence2:
+        # tokenize the sentences and get the output logits for the sentence pair classification task
+        inputs = tokenizer(sentence1, sentence2, padding=True, truncation=True, max_length=250, return_tensors="pt")
+        outputs = model(**inputs).logits
+        
+        # calculate the softmax probabilities for the two classes (similar or dissimilar)
+        probs = outputs.softmax(dim=1)
+        
+        # the probability of the sentences being similar is the second element of the output array
+        similarity_score = probs[0][1].item()
+        
+        # display the similarity score to the user
+        st.write("Similarity score:", similarity_score)