Update app.py

app.py

@@ -1,6 +1,5 @@
 import gradio as gr
 import torch
-
 from transformers import AutoModel, AutoTokenizer
 from sklearn.metrics.pairwise import cosine_similarity
 
@@ -10,8 +9,6 @@ model = AutoModel.from_pretrained(model_name)
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
 def generate_sentence_embedding(sentence, language):
-
-    # Set adapter to specified language
     if "de" in language:
         model.set_default_language("de_CH")
     if "fr" in language:
@@ -20,15 +17,9 @@ def generate_sentence_embedding(sentence, language):
         model.set_default_language("it_CH")
     if "rm" in language:
         model.set_default_language("rm_CH")
-
-    # Tokenize input sentence
     inputs = tokenizer(sentence, padding=True, truncation=True, return_tensors="pt", max_length=512)
-
-    # Take tokenized input and pass it through the model
     with torch.no_grad():
         outputs = model(**inputs)
-
-    # Extract sentence embeddings via mean pooling
     token_embeddings = outputs.last_hidden_state
     attention_mask = inputs['attention_mask'].unsqueeze(-1).expand(token_embeddings.size()).float()
     sum_embeddings = torch.sum(token_embeddings * attention_mask, 1)
@@ -37,16 +28,13 @@ def generate_sentence_embedding(sentence, language):
     return embedding
 
 def calculate_cosine_similarities(source_sentence, source_language, target_sentence_1, target_language_1, target_sentence_2, target_language_2, target_sentence_3, target_language_3):
-
     source_embedding = generate_sentence_embedding(source_sentence, source_language)
     target_embedding_1 = generate_sentence_embedding(target_sentence_1, target_language_1)
     target_embedding_2 = generate_sentence_embedding(target_sentence_2, target_language_2)
     target_embedding_3 = generate_sentence_embedding(target_sentence_3, target_language_3)
-
     cosine_score_1 = cosine_similarity(source_embedding, target_embedding_1)
     cosine_score_2 = cosine_similarity(source_embedding, target_embedding_2)
     cosine_score_3 = cosine_similarity(source_embedding, target_embedding_3)
-
     cosine_scores = {
         target_sentence_1: cosine_score_1[0][0],
         target_sentence_2: cosine_score_2[0][0],
@@ -63,18 +51,23 @@ def main():
     demo = gr.Interface(
         fn=calculate_cosine_similarities,
         inputs=[
-            gr.Textbox(lines=1, placeholder="Der Zug fährt um 9 Uhr in Zürich ab.", label="source sentence"),
-            gr.Dropdown(["de", "fr", "it", "rm"], value="de", label="language"),
-            gr.Textbox(lines=1, placeholder="Le train arrive à Lausanne à 11 heures.", label="target sentence 1"),
-            gr.Dropdown(["de", "fr", "it", "rm"], value="fr", label="language"),
-            gr.Textbox(lines=1, placeholder="Alla stazione di Lugano ci sono diversi binari.", label="target sentence 2"),
-            gr.Dropdown(["de", "fr", "it", "rm"], value="it", label="language"),
-            gr.Textbox(lines=1, placeholder="A Cuera van biars trens ellas muntognas.", label="target sentence 3"),
-            gr.Dropdown(["de", "fr", "it", "rm"], value="rm", label="language")
+            gr.Textbox(lines=1, placeholder="Enter source sentence", label="Source Sentence"),
+            gr.Dropdown(["de", "fr", "it", "rm"], label="Source Language"),
+            gr.Textbox(lines=1, placeholder="Enter target sentence 1", label="Target Sentence 1"),
+            gr.Dropdown(["de", "fr", "it", "rm"], label="Target Language 1"),
+            gr.Textbox(lines=1, placeholder="Enter target sentence 2", label="Target Sentence 2"),
+            gr.Dropdown(["de", "fr", "it", "rm"], label="Target Language 2"),
+            gr.Textbox(lines=1, placeholder="Enter target sentence 3", label="Target Sentence 3"),
+            gr.Dropdown(["de", "fr", "it", "rm"], label="Target Language 3")
             ],
-        outputs= gr.Textbox(label="Cosine similarity scores", type="text", lines=3)
+        outputs= gr.Textbox(label="Cosine Similarity Scores", type="text", lines=3),
+        title="Sentence Similarity Calculator",
+        description="Enter a source sentence and up to three target sentences to calculate their cosine similarity.",
+        examples=[
+            ["Der Zug fährt um 9 Uhr in Zürich ab.", "de", "Le train arrive à Lausanne à 11 heures.", "fr", "Alla stazione di Lugano ci sono diversi binari.", "it", "A Cuera van biars trens ellas muntognas.", "rm"]
+        ]
     )
     demo.launch(share=True)
 
 if __name__ == "__main__":
-    main()
+    main()