Update app.py

app.py

@@ -8,6 +8,7 @@ from numpy.linalg import norm
 import matplotlib.pyplot as plt
 import os
 import base64
+
 st.set_page_config(layout="wide")
 # Function to load the Random Forest model
 @st.cache_resource
@@ -25,15 +26,6 @@ model = load_model()
 if model is None:
     st.stop()
 
-# Initialize MediaPipe Hands
-@st.cache_resource
-def load_mediapipe_model():
-    mp_hands = mp.solutions.hands
-    return mp_hands.Hands(static_image_mode=True, max_num_hands=1, min_detection_confidence=0.5)
-
-hands = load_mediapipe_model()
-mp_drawing = mp.solutions.drawing_utils
-
 # Function to normalize landmarks
 def normalize_landmarks(landmarks):
     center = np.mean(landmarks, axis=0)
@@ -55,19 +47,21 @@ def calculate_angles(landmarks):
 
 # Function to process image and predict alphabet
 def process_and_predict(image):
-    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    results = hands.process(image_rgb)
-    
-    if results.multi_hand_landmarks:
-        landmarks = np.array([[lm.x, lm.y] for lm in results.multi_hand_landmarks[0].landmark])
-        landmarks_normalized = normalize_landmarks(landmarks)
-        angles = calculate_angles(landmarks_normalized)
-        
-        angle_columns = [f'angle_{i}' for i in range(len(angles))]
-        angles_df = pd.DataFrame([angles], columns=angle_columns)
+    mp_hands = mp.solutions.hands
+    with mp_hands.Hands(static_image_mode=True, max_num_hands=1, min_detection_confidence=0.5) as hands:
+        image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        results = hands.process(image_rgb)
         
-        probabilities = model.predict_proba(angles_df)[0]
-        return probabilities, landmarks
+        if results.multi_hand_landmarks:
+            landmarks = np.array([[lm.x, lm.y] for lm in results.multi_hand_landmarks[0].landmark])
+            landmarks_normalized = normalize_landmarks(landmarks)
+            angles = calculate_angles(landmarks_normalized)
+            
+            angle_columns = [f'angle_{i}' for i in range(len(angles))]
+            angles_df = pd.DataFrame([angles], columns=angle_columns)
+            
+            probabilities = model.predict_proba(angles_df)[0]
+            return probabilities, landmarks
     
     return None, None
 
@@ -75,7 +69,8 @@ def process_and_predict(image):
 def plot_hand_landmarks(landmarks, title):
     fig, ax = plt.subplots(figsize=(10, 10))
     ax.scatter(landmarks[:, 0], landmarks[:, 1], c='blue', s=50)
-    for connection in mp.solutions.hands.HAND_CONNECTIONS:
+    mp_hands = mp.solutions.hands
+    for connection in mp_hands.HAND_CONNECTIONS:
         start_idx = connection[0]
         end_idx = connection[1]
         ax.plot([landmarks[start_idx, 0], landmarks[end_idx, 0]],
@@ -162,8 +157,4 @@ with tab2:
                     except Exception as e:
                         st.error(f"An error occurred while processing image for {alphabet}: {str(e)}")
                 else:
-                    st.error(f"Image not found for {alphabet}")
-
-# Don't close hands here, as it might be None
-if hands is not None:
-    hands.close()
+                    st.error(f"Image not found for {alphabet}")