fixed hidden states

app.py

@@ -24,8 +24,9 @@ index = faiss.IndexFlatL2(dimension)
 
 def embed_text(text):
     inputs = tokenizer(text, return_tensors="pt", truncation=True, padding="max_length", max_length=512)
-    outputs = model(**inputs)
-    return outputs.last_hidden_state.mean(dim=1).detach().numpy()
+    outputs = model(**inputs, output_hidden_states=True)  # Ensure hidden states are returned
+    hidden_state = outputs.hidden_states[-1]  # Get the last hidden state
+    return hidden_state.mean(dim=1).detach().numpy()  # Take the mean across the sequence
 
 # Example: Embed past conversation and store in FAISS
 past_conversation = "FDA approval for companion diagnostics requires careful documentation."