fixed output and added a copy button

app.py

@@ -2,6 +2,7 @@
 import gradio as gr # For interface
 from sentence_transformers import SentenceTransformer # For embedding the text
 import torch # For gpu 
+import numpy as np 
 
 # Make the app device agnostic
 device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
@@ -16,13 +17,19 @@ def predict(input_text):
     # Calculate embeddings by calling model.encode(), specifying the device
     embeddings = model.encode(input_text, device=device)
     
-    return embeddings
+    # Set the print options to avoid truncation and use fixed-point notation
+    np.set_printoptions(threshold=np.inf, precision=8, suppress=True, floatmode='fixed')
+
+    # Convert the array to a string for display
+    embeddings_str = np.array2string(embeddings, separator=',')
+    
+    return embeddings_str
 
 # Gradio app interface
 gradio_app = gr.Interface(
     predict,
-    inputs="text", 
-    outputs=gr.Textbox(lines=30, placeholder="Scrollable array output"),
+    inputs=gr.Textbox(placeholder="Insert Text"), 
+    outputs=gr.Textbox(show_copy_button=True, placeholder='Vector of dimensions 1024'),
     title="Text to Vector Generator",
     description="Input a text and get its vector representation using an embedding model (mixedbread-ai/mxbai-embed-large-v1)."
 )