Update app.py

app.py

@@ -1,5 +1,6 @@
 import gradio as gr
 from transformers import AutoModel, AutoTokenizer
+import numpy as np
 
 # Load a small CPU model for text to vector processing
 model_name = "sentence-transformers/all-MiniLM-L6-v2"
@@ -7,15 +8,11 @@ model = AutoModel.from_pretrained(model_name)
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
 def text_to_vector(text):
-    """
-    Converts text to a vector representation using a pre-trained model.
-    """
     inputs = tokenizer(text, return_tensors="pt")
     outputs = model(**inputs)
     vector = outputs.pooler_output.detach().numpy()[0]
-    # Convert the vector to a string representation for display
-    vector_str = ", ".join([str(x) for x in vector])
-    return vector_str
+    # Convert to a string representation for display
+    return ", ".join(map(str, vector))
 
 demo = gr.Interface(
     fn=text_to_vector,