Update app.py

app.py

@@ -4,7 +4,7 @@ import numpy as np
 import cv2
 from PIL import Image
 import io
-import base64
+import os
 
 # Try to import FaceNet with error handling
 try:
@@ -68,15 +68,14 @@ class FaceRecognitionSystem:
         try:
             # Convert image to PIL format
             if isinstance(image, str):
+                # File path
                 pil_image = Image.open(image).convert('RGB')
             elif isinstance(image, np.ndarray):
+                # numpy array
                 pil_image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
             else:
-                # Handle file uploads
-                if hasattr(image, 'read'):
-                    pil_image = Image.open(io.BytesIO(image.read())).convert('RGB')
-                else:
-                    return None, "Unsupported image format"
+                # Handle Gradio file object
+                pil_image = Image.open(image).convert('RGB')
             
             # Detect faces and extract embeddings
             faces = self.mtcnn(pil_image)
@@ -183,26 +182,30 @@ class FaceRecognitionSystem:
             else:
                 result_message = f"⚠️ {name} already marked today (ID: {student_id})"
             
-            # Draw recognition info on image
-            if isinstance(image, np.ndarray):
+            # Convert image for display
+            if isinstance(image, str):
+                display_image = cv2.imread(image)
+            elif isinstance(image, np.ndarray):
                 display_image = image.copy()
             else:
-                display_image = np.array(image)
+                # Handle Gradio file object
+                display_image = np.array(Image.open(image))
                 if len(display_image.shape) == 3 and display_image.shape[2] == 3:
                     display_image = cv2.cvtColor(display_image, cv2.COLOR_RGB2BGR)
             
             # Add text to image
-            cv2.putText(display_image, f"Recognized: {name}", (10, 30), 
-                       cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
-            cv2.putText(display_image, f"ID: {student_id}", (10, 60), 
-                       cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
-            cv2.putText(display_image, f"Class: {class_name}", (10, 90), 
-                       cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
+            if display_image is not None:
+                cv2.putText(display_image, f"Recognized: {name}", (10, 30), 
+                           cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
+                cv2.putText(display_image, f"ID: {student_id}", (10, 60), 
+                           cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
+                cv2.putText(display_image, f"Class: {class_name}", (10, 90), 
+                           cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
+                
+                display_image = cv2.cvtColor(display_image, cv2.COLOR_BGR2RGB)
+                return display_image, result_message
             
-            display_image = cv2.cvtColor(display_image, cv2.COLOR_BGR2RGB)
-            return display_image, result_message
-        else:
-            return image, "❌ No matching student found"
+        return image, "❌ No matching student found"
     
     def get_all_students(self):
         """Get all registered students"""
@@ -273,31 +276,26 @@ with gr.Blocks(title="Face Recognition Attendance System") as demo:
             with gr.Column():
                 name_input = gr.Textbox(
                     label="Full Name", 
-                    placeholder="Enter student's full name",
-                    info="This name will be used for attendance records"
+                    placeholder="Enter student's full name"
                 )
                 student_id_input = gr.Textbox(
                     label="Student ID", 
-                    placeholder="Enter unique student ID",
-                    info="Must be unique for each student"
+                    placeholder="Enter unique student ID"
                 )
                 class_input = gr.Textbox(
                     label="Class", 
-                    placeholder="Enter class name",
-                    info="e.g., Computer Science, Mathematics, etc."
+                    placeholder="Enter class name"
                 )
                 image_input = gr.Image(
                     label="Upload Face Image",
-                    type="filepath",
-                    info="Upload a clear frontal face photo for registration"
+                    type="filepath"
                 )
-                register_btn = gr.Button("🚀 Register Student with Face", variant="primary")
+                register_btn = gr.Button("Register Student with Face", variant="primary")
             
             with gr.Column():
                 register_output = gr.Textbox(
                     label="Registration Status", 
-                    lines=5,
-                    info="Status of student registration and face embedding extraction"
+                    lines=5
                 )
         
         register_btn.click(
@@ -312,20 +310,17 @@ with gr.Blocks(title="Face Recognition Attendance System") as demo:
             with gr.Column():
                 attendance_image = gr.Image(
                     label="Upload Photo for Attendance",
-                    type="filepath",
-                    info="Upload a photo containing faces to mark attendance automatically"
+                    type="filepath"
                 )
-                recognize_btn = gr.Button("🎯 Recognize Faces & Mark Attendance", variant="primary")
+                recognize_btn = gr.Button("Recognize Faces & Mark Attendance", variant="primary")
             
             with gr.Column():
                 processed_image = gr.Image(
-                    label="Processed Image",
-                    info="Image with recognition results"
+                    label="Processed Image"
                 )
                 recognition_output = gr.Textbox(
                     label="Recognition Results",
-                    lines=4,
-                    info="Attendance marking status and recognition details"
+                    lines=4
                 )
         
         recognize_btn.click(
@@ -339,7 +334,7 @@ with gr.Blocks(title="Face Recognition Attendance System") as demo:
         with gr.Row():
             with gr.Column():
                 gr.Markdown("#### Registered Students")
-                students_btn = gr.Button("👥 View All Students", variant="secondary")
+                students_btn = gr.Button("View All Students", variant="secondary")
                 students_output = gr.Markdown()
             
             with gr.Column():
@@ -347,9 +342,9 @@ with gr.Blocks(title="Face Recognition Attendance System") as demo:
                 time_range = gr.Radio(
                     choices=["Today", "All Time"],
                     value="Today",
-                    label="Select Time Range for Attendance Records"
+                    label="Select Time Range"
                 )
-                attendance_btn = gr.Button("📈 View Attendance Records", variant="secondary")
+                attendance_btn = gr.Button("View Attendance Records", variant="secondary")
                 attendance_output = gr.Markdown()
         
         students_btn.click(view_students_interface, outputs=students_output)
@@ -365,58 +360,21 @@ with gr.Blocks(title="Face Recognition Attendance System") as demo:
         
         This system uses state-of-the-art facial recognition technology to automate attendance marking.
         
-        ### 🚀 Features:
+        ### Features:
         - **Face Registration**: Register students with their facial data
         - **Automatic Recognition**: Recognize students from photos and mark attendance
         - **Attendance Management**: View and manage attendance records
-        - **SQLite Database**: Secure storage of student data and face embeddings
-        
-        ### 🔧 Technology Stack:
-        - **FaceNet**: For face detection and recognition
-        - **MTCNN**: Multi-task Cascaded CNN for face detection
-        - **Gradio**: For user-friendly web interface
-        - **SQLite**: For data persistence
-        
-        ### 📝 How to Use:
         
-        1. **Register Students**:
-           - Go to 'Register Student' tab
-           - Enter student details (Name, ID, Class)
-           - Upload a clear frontal face photo
-           - Click 'Register Student with Face'
+        ### How to Use:
+        1. **Register Students**: Go to 'Register Student' tab, enter details and upload face photo
+        2. **Mark Attendance**: Go to 'Mark Attendance' tab, upload photo with faces
+        3. **View Records**: Check registered students and attendance records
         
-        2. **Mark Attendance**:
-           - Go to 'Mark Attendance' tab
-           - Upload a photo containing student faces
-           - System automatically recognizes faces and marks attendance
-           - View results in the processed image and status message
-        
-        3. **View Records**:
-           - View all registered students
-           - Check attendance records (today or all time)
-        
-        ### 💡 Tips for Best Results:
-        - Use clear, well-lit face images for registration
-        - Ensure faces are clearly visible in attendance photos
-        - Frontal faces work better than profiles
-        - Good lighting improves recognition accuracy
-        
-        ### 🔒 Privacy & Security:
-        - Face embeddings are stored as numerical vectors
-        - No raw face images are stored in the database
-        - All data is processed locally
+        ### Technology:
+        - FaceNet for face recognition
+        - Gradio for web interface
+        - SQLite for data storage
         """)
-        
-        # System status
-        status_msg = "✅ **System Status: OPERATIONAL**" if FACE_NET_AVAILABLE else "⚠️ **System Status: LIMITED FUNCTIONALITY**"
-        gr.Markdown(f"\n### System Status\n{status_msg}")
-        
-        if not FACE_NET_AVAILABLE:
-            gr.Markdown("""
-            ⚠️ **Note**: Face recognition features are currently unavailable. 
-            The system is running in limited functionality mode. 
-            Basic student registration and attendance tracking are still available.
-            """)
 
 if __name__ == "__main__":
-    demo.launch(show_error=True)
+    demo.launch()