Update app.py

app.py

@@ -1,5 +1,3 @@
-import os
-from sklearn.linear_model import LinearRegression  # <-- Add this line
 import random
 import os
 import shutil
@@ -14,6 +12,7 @@ from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
 from reportlab.lib import colors
 from datetime import datetime
 import joblib
+from sklearn.linear_model import LinearRegression
 
 # Initialize the face mesh model
 mp_face_mesh = mp.solutions.face_mesh
@@ -207,22 +206,22 @@ def analyze_face(input_data):
     if isinstance(input_data, str):  # Video input (file path in Replit)
         cap = cv2.VideoCapture(input_data)
         if not cap.isOpened():
-            return "<div style='color:red;'>⚠️ Error: Could not open video.</div>", None
+            return None, None  # Return None for both parts if error occurs
         ret, frame = cap.read()
         cap.release()
         if not ret:
-            return "<div style='color:red;'>⚠️ Error: Could not read video frame.</div>", None
+            return None, None  # Return None for both parts if error occurs
     else:  # Image input
         frame = input_data
         if frame is None:
-            return "<div style='color:red;'>⚠️ Error: No image provided.</div>", None
+            return None, None  # Return None for both parts if error occurs
 
     # Resize image to reduce processing time
     frame = cv2.resize(frame, (640, 480))  # Adjust resolution for Replit
     frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
     result = face_mesh.process(frame_rgb)
     if not result.multi_face_landmarks:
-        return "<div style='color:red;'>⚠️ Error: Face not detected.</div>", None
+        return None, None  # Return None for both parts if no face is detected
     landmarks = result.multi_face_landmarks[
         0].landmark  # Fixed: Use integer index
     features = extract_features(frame_rgb, landmarks)