Update app.py

app.py

@@ -1,25 +1,18 @@
 import gradio as gr
-import numpy as np
 from sentence_transformers import SentenceTransformer
 
 # Load the pre-trained model
 model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
 
 def get_embeddings(sentences):
-    # Split sentences by new line
-    # sentences_list = [s.strip() for s in sentences.split('\n') if s.strip()]
-    # Get embeddings for the input sentences
     embeddings = model.encode(sentences, convert_to_tensor=True)
-    # Convert to 2D NumPy array
-    # embeddings_array = np.array(embeddings)
-    embeddings_array=embeddings.tolist()
-    return embeddings_array
+    return embeddings.tolist()
 
 # Define the Gradio interface
 interface = gr.Interface(
     fn=get_embeddings,  # Function to call
     inputs=gr.Textbox(lines=2, placeholder="Enter sentences here, one per line"),  # Input component
-    outputs=gr.DataFrame(),
+    outputs=gr.JSON(),
     title="Sentence Embeddings",  # Interface title
     description="Enter sentences to get their embeddings."  # Description
 )