chore: version5

app.py

@@ -158,7 +158,7 @@ def key_gen_fn(user_symptoms: List[str]) -> Dict:
     with evaluation_key_path.open("wb") as f:
         f.write(serialized_evaluation_keys)
 
-    serialized_evaluation_keys_shorten_hex = serialized_evaluation_keys.hex()[:INPUT_BROWSER_LIMIT]   
+    serialized_evaluation_keys_shorten_hex = serialized_evaluation_keys.hex()[:INPUT_BROWSER_LIMIT]
 
     return {
         error_box2: gr.update(visible=False),
@@ -331,8 +331,6 @@ def get_output_fn(user_id: str, user_symptoms: np.ndarray) -> Dict:
             )
         }
 
-
-
     data = {
         "user_id": user_id,
     }
@@ -363,7 +361,7 @@ def decrypt_fn(user_id: str, user_symptoms: np.ndarray) -> Dict:
     Args:
         user_id (int): The current user's ID
         user_symptoms (numpy.ndarray): The user symptoms
-        
+
     Returns:
         Decrypted output
     """
@@ -408,7 +406,6 @@ def decrypt_fn(user_id: str, user_symptoms: np.ndarray) -> Dict:
     }
 
 
-
 def clear_all_btn():
     """Clear all the box outputs."""
 
@@ -450,7 +447,7 @@ CSS = """
 if __name__ == "__main__":
 
     print("Starting demo ...")
-    
+
     clean_directory()
 
     (X_train, X_test), (y_train, y_test) = load_data()
@@ -510,7 +507,7 @@ if __name__ == "__main__":
                 error_box1 = gr.Textbox(label="Error", visible=False)
 
                 # Default disease, picked from the dataframe
-                # disease_box = gr.Dropdown(list(sorted(set(df_test["prognosis"]))), 
+                # disease_box = gr.Dropdown(list(sorted(set(df_test["prognosis"]))),
                 # label="Disease:")
                 # disease_box.change(
                 #     fn=fill_in_fn,
@@ -533,7 +530,7 @@ if __name__ == "__main__":
                     inputs=[*check_boxes],
                     outputs=[user_vect_box1, error_box1],
                 )
-            
+
             with gr.TabItem("2. Data Encryption") as encryption_tab:
                 gr.Markdown("<span style='color:orange'>Client Side</span>")
                 gr.Markdown("## Step 2: Generate the keys")
@@ -659,7 +656,6 @@ if __name__ == "__main__":
                     outputs=[srv_resp_retrieve_data_box, error_box6],
                 )
 
-                
                 gr.Markdown("## Step 7: Decrypt the output")
 
                 decrypt_target_btn = gr.Button("Decrypt the output")

server.py

@@ -32,11 +32,12 @@ def send_input(
     """Send the inputs to the server."""
 
     print("\nSend the data to the server ............\n")
-    # Retrieve the encrypted input and the evaluation key paths
+    
+    # Receive the Client's files (Evaluation key + Encrypted symptoms)
     evaluation_key_path = SERVER_DIR / f"{user_id}_valuation_key"
-    encrypted_input_path = SERVER_DIR / f"{user_id}_encrypted_symptoms"
+    encrypted_input_path = SERVER_DIR / f"{user_id}_encrypted_input"
 
-    # # Write the files using the above paths
+    # Save the files using the above paths
     with encrypted_input_path.open("wb") as encrypted_input, evaluation_key_path.open(
         "wb"
     ) as evaluation_key:
@@ -52,9 +53,9 @@ def run_fhe(
 
     print("\nRun in FHE in the server ............\n")
     evaluation_key_path = SERVER_DIR / f"{user_id}_valuation_key"
-    encrypted_input_path = SERVER_DIR / f"{user_id}_encrypted_symptoms"
+    encrypted_input_path = SERVER_DIR / f"{user_id}_encrypted_input"
 
-    # Read the files using the above paths
+    # Read the files (Evaluation key + Encrypted symptoms) using the above paths
     with encrypted_input_path.open("rb") as encrypted_output_file, evaluation_key_path.open(
         "rb"
     ) as evaluation_key_file:
@@ -82,10 +83,11 @@ def run_fhe(
 
 @app.post("/get_output")
 def get_output(user_id: str = Form()):
-    """Retrieve the encrypted output."""
+    """Retrieve the encrypted output from the server."""
 
     print("\nGet the output from the server ............\n")
-    # Retrieve the encrypted output path
+    
+    # Path where the encrypted output is saved
     encrypted_output_path = SERVER_DIR / f"{user_id}_encrypted_output"
 
     # Read the file using the above path
@@ -94,4 +96,5 @@ def get_output(user_id: str = Form()):
 
     time.sleep(1)
 
+    # Send the encrypted output
     return Response(encrypted_output)

utils.py

@@ -118,7 +118,7 @@ def load_data() -> Tuple[pandas.DataFrame, pandas.DataFrame, numpy.ndarray]:
 
 def load_model(X_train: pandas.DataFrame, y_train: numpy.ndarray):
     """
-    Load a pretrained serialized model
+    Load a pre-trained serialized model
 
     Args:
         X_train (pandas.DataFrame): Training set