Update app.py

app.py

@@ -1,40 +1,43 @@
-import gradio as gr
-
 from transformers.utils import logging
 logging.set_verbosity_error()
-from sentence_transformers import SentenceTransformer
 
-def compare(name):
-    return model.encode(name, convert_to_tensor=True)
+from sentence_transformers import SentenceTransformer
 
 model = SentenceTransformer("all-MiniLM-L6-v2")
-sentences1 = ['The cat sits outside',
-              'A man is playing guitar',
-              'The movies are awesome']
-embeddings1 = model.encode(sentences1, convert_to_tensor=True)
-sentences2 = ['The dog plays in the garden',
-              'A woman watches TV',
-              'The new movie is so great']
-embeddings2 = model.encode(sentences2, 
-                           convert_to_tensor=True)
-print(embeddings2)
 
+import gradio as gr
 from sentence_transformers import util
-cosine_scores = util.cos_sim(embeddings1,embeddings2)
 
-print(cosine_scores)
 
-demo = gr.Interface(fn=compare, inputs="text", outputs="text")
-demo.launch()
+def compare_sentences(sentence1, sentence2):
+    embeddings1 = model.encode([sentence1], convert_to_tensor=True)
+    embeddings2 = model.encode([sentence2], convert_to_tensor=True)
+    output = util.cos_sim(embeddings1, embeddings2)
+    return float(output[0][0])
+
+sentence1 = input("Enter the first sentence: ")
+sentence2 = input("Enter the second sentence: ")
 
+similarity_score = compare_sentences(sentence1, sentence2)
 
-iface.launch()
+print(similarity_score)
 
+
+***
 for i in range(len(sentences1)):
     print("{} \t\t {} \t\t Score: {:.4f}".format(sentences1[i],
                                                  sentences2[i],
-                                                 cosine_scores[i][i]))
+                                                 cosine_scores[i][I]))
+
+
+#added
+demo = gr.Interface(fn=compare, inputs="text", outputs="text")
+demo.launch()
+
+
+def compare(name):
+    return model.encode(name, convert_to_tensor=True)
 
-    
+iface.launch()