Update app.py

app.py

@@ -43,46 +43,38 @@ except Exception as e:
 # Database Functions
 def initialize_database():
     """ Initializes the SQLite database by creating the students table if it doesn't exist. """
-    conn = sqlite3.connect(DATABASE)
-    cursor = conn.cursor()
-    cursor.execute("""
-        CREATE TABLE IF NOT EXISTS students (
-            id INTEGER PRIMARY KEY AUTOINCREMENT,
-            name TEXT NOT NULL,
-            roll_no TEXT NOT NULL UNIQUE,
-            image_path TEXT NOT NULL,
-            timestamp DATETIME DEFAULT CURRENT_TIMESTAMP
-        )
-    """)
-    conn.commit()
-    conn.close()
+    with sqlite3.connect(DATABASE) as conn:
+        cursor = conn.cursor()
+        cursor.execute("""
+            CREATE TABLE IF NOT EXISTS students (
+                id INTEGER PRIMARY KEY AUTOINCREMENT,
+                name TEXT NOT NULL,
+                roll_no TEXT NOT NULL UNIQUE,
+                image_path TEXT NOT NULL,
+                timestamp DATETIME DEFAULT CURRENT_TIMESTAMP
+            )
+        """)
+        conn.commit()
 
 def save_to_database(name, roll_no, image_path):
     """ Saves the student's data to the database. """
-    conn = sqlite3.connect(DATABASE)
-    cursor = conn.cursor()
-    try:
-        cursor.execute("""
-            INSERT INTO students (name, roll_no, image_path)
-            VALUES (?, ?, ?)
-        """, (name, roll_no, image_path))
-        conn.commit()
-        st.success("Data saved successfully!")
-    except sqlite3.IntegrityError:
-        st.error("Roll number already exists!")
-    finally:
-        conn.close()
+    with sqlite3.connect(DATABASE) as conn:
+        cursor = conn.cursor()
+        try:
+            cursor.execute("""
+                INSERT INTO students (name, roll_no, image_path)
+                VALUES (?, ?, ?)
+            """, (name, roll_no, image_path))
+            conn.commit()
+            st.success("Data saved successfully!")
+        except sqlite3.IntegrityError:
+            st.error("Roll number already exists!")
 
 def save_image_to_hugging_face(image, name, roll_no):
     """ Saves the image locally to the KNOWN_FACES_DIR and uploads it to Hugging Face. """
-    # Ensure the directory exists
-    if not os.path.exists(KNOWN_FACES_DIR):
-        os.makedirs(KNOWN_FACES_DIR)
-
-    # Construct the local file path
     filename = f"{name}_{roll_no}.jpg"
     local_path = os.path.join(KNOWN_FACES_DIR, filename)
-    
+
     try:
         # Convert image to RGB if necessary
         if image.mode != "RGB":
@@ -90,7 +82,7 @@ def save_image_to_hugging_face(image, name, roll_no):
         
         # Save the image to the known_faces directory
         image.save(local_path)
-        
+
         # Upload the saved file to Hugging Face
         api.upload_file(
             path_or_fileobj=local_path,
@@ -106,7 +98,6 @@ def save_image_to_hugging_face(image, name, roll_no):
     return local_path
 
 
-
 # Initialize the database when the app starts
 initialize_database()
 
@@ -148,11 +139,10 @@ if st.button("Register"):
 
 # Display registered student data
 if st.checkbox("Show registered students"):
-    conn = sqlite3.connect(DATABASE)
-    cursor = conn.cursor()
-    cursor.execute("SELECT name, roll_no, image_path, timestamp FROM students")
-    rows = cursor.fetchall()
-    conn.close()
+    with sqlite3.connect(DATABASE) as conn:
+        cursor = conn.cursor()
+        cursor.execute("SELECT name, roll_no, image_path, timestamp FROM students")
+        rows = cursor.fetchall()
     
     st.write("### Registered Students")
     for row in rows:
@@ -166,6 +156,7 @@ def detect_faces_and_emotions(image):
     face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
     faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.3, minNeighbors=5)
     
+    emotion_labels = []
     for (x, y, w, h) in faces:
         face = gray_image[y:y+h, x:x+w]
         resized_face = cv2.resize(face, (48, 48))  # Resize face to 48x48
@@ -176,8 +167,9 @@ def detect_faces_and_emotions(image):
         # Predict the emotion
         emotion_prediction = emotion_model.predict(reshaped_face)
         emotion_label = np.argmax(emotion_prediction)
-        return EMOTION_LABELS[emotion_label]
-    return None
+        emotion_labels.append(EMOTION_LABELS[emotion_label])
+    
+    return emotion_labels if emotion_labels else None
 
 # UI for Emotion Detection
 if st.sidebar.selectbox("Menu", ["Register Student", "Face Recognition and Emotion Detection", "View Attendance"]) == "Face Recognition and Emotion Detection":
@@ -189,9 +181,9 @@ if st.sidebar.selectbox("Menu", ["Register Student", "Face Recognition and Emoti
         if uploaded_file:
             img = Image.open(uploaded_file)
             img_array = np.array(img)
-            emotion_label = detect_faces_and_emotions(img_array)
-            if emotion_label:
-                st.success(f"Emotion Detected: {emotion_label}")
+            emotion_labels = detect_faces_and_emotions(img_array)
+            if emotion_labels:
+                st.success(f"Emotions Detected: {', '.join(emotion_labels)}")
             else:
                 st.warning("No face detected.")
 
@@ -201,8 +193,8 @@ if st.sidebar.selectbox("Menu", ["Register Student", "Face Recognition and Emoti
         if camera_image:
             img = Image.open(camera_image)
             img_array = np.array(img)
-            emotion_label = detect_faces_and_emotions(img_array)
-            if emotion_label:
-                st.success(f"Emotion Detected: {emotion_label}")
+            emotion_labels = detect_faces_and_emotions(img_array)
+            if emotion_labels:
+                st.success(f"Emotions Detected: {', '.join(emotion_labels)}")
             else:
                 st.warning("No face detected.")