Update app.py

app.py

@@ -1,18 +1,21 @@
 import gradio as gr
 from sentence_transformers import SentenceTransformer
 
-# Load the embedding model
-model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+# ✅ Load a model that outputs 1024-dim vectors
+model = SentenceTransformer('intfloat/e5-large')
 
 def embed_text(text):
-    embedding = model.encode(text).tolist()
+    # Optionally prepend "passage: " if using e5 models
+    processed_text = "passage: " + text.strip()
+    embedding = model.encode(processed_text).tolist()
     return embedding
 
+# Gradio interface
 iface = gr.Interface(
     fn=embed_text,
     inputs=gr.Textbox(lines=5, label="Enter patient text"),
     outputs="json",
-    title="Clinical Text Embedding API"
+    title="Clinical Text Embedding API (1024-dim)"
 )
 
 iface.launch()