Update app.py

app.py

@@ -1,7 +1,6 @@
 import gradio as gr
 from transformers import AutoModel, AutoTokenizer
 import numpy as np
-import json
 
 # Load a small CPU model for text to vector processing
 model_name = "Supabase/gte-small"
@@ -18,20 +17,17 @@ def text_to_vector(texts_json):
 
     inputs = tokenizer(texts, return_tensors="pt", padding=True, truncation=True)
     outputs = model(**inputs)
-    vectors = outputs.pooler_output.detach().numpy()
-
-    # Convert to PostgreSQL-friendly array format
-    postgres_array = "{" + ",".join(["{" + ",".join(map(str, v)) + "}" for v in vectors]) + "}"
+    vectors = outputs.pooler_output.detach().numpy()  # NumPy array
     
-    return postgres_array
-
+    # Flatten the array and return as a 1D array of floats 
+    return vectors.reshape(-1).tolist() 
 
 demo = gr.Interface(
     fn=text_to_vector,
     inputs=gr.Textbox(label="Enter JSON array", placeholder="Enter an array of sentences as a JSON string"),
-    outputs=gr.Textbox(label="Text Vectors (PostgreSQL Array)", lines=10),
+    outputs=gr.Textbox(label="Text Vectors (flattened float array)", lines=10),
     title="Batch Text to Vector",
-    description="This demo converts an array of sentences to vectors and returns them as a PostgreSQL-friendly array."
+    description="This demo converts an array of sentences to vectors and returns them as a flattened float array."
 )
 
 demo.launch()