Update app.py

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 from requests import head
 from transformer_vectorizer import TransformerVectorizer
 from sklearn.feature_extraction.text import TfidfVectorizer
-
+import numpy as np 
 from concrete.ml.deployment import FHEModelClient
 import numpy
 import os
@@ -110,11 +110,11 @@ def encode_quantize_encrypt(text, user_id):
         encodings =vectorizer.fit_transform([text]).toarray()
         if encodings.shape[1] < 1736:
             # 在后面填充零
-            padding = np.zeros((1, 1736 - dense_matrix.shape[1]))
-            encodings = np.hstack((dense_matrix, padding))
+            padding = np.zeros((1, 1736 - encodings.shape[1]))
+            encodings = np.hstack((encodings, padding))
         elif encodings.shape[1] > 1736:
             # 截取前1736列
-            encodings = dense_matrix[:, :1736]
+            encodings = encodings[:, :1736]
     else:
         fhe_api = FHEModelClient(FHE_MODEL_PATH, f".fhe_keys/{user_id}")
         encodings = transformer_vectorizer.transform([text])