Update app.py

app.py

@@ -1,508 +1,11 @@
-# # import cv2
-# # from ultralytics import YOLO
-# # import time
-# # from datetime import datetime, timedelta
-# # import numpy
-# # import csv
-# #
-# # # Load YOLO model
-# # model = YOLO("D:\\live attendance\\best(attendance).pt")
-# #
-# # # Initialize webcam
-# # cap = cv2.VideoCapture(0)  # 0 is usually the default camera
-# #
-# # # Dictionary to store attendance records
-# # attendance_records = {}
-# #
-# # # CSV file to store attendance data
-# # csv_file = open(r"D:\\live attendance\\attendance_data.csv", "w", newline="")
-# # csv_writer = csv.writer(csv_file)
-# # csv_writer.writerow(["Name", "Time"])
-# #
-# #
-# # while True:
-# #     # Read a frame from the webcam
-# #     ret, frame = cap.read()
-# #     if not ret:
-# #         print("Failed to capture image")
-# #         break
-# #
-# #     # Detect objects
-# #     results = model(frame)
-# #
-# #     # Iterate through results
-# #     for result in results:
-# #         boxes = result.boxes
-# #         for box in boxes:  # Iterate through detected boxes
-# #             x1, y1, x2, y2 = box.xyxy[0].cpu().numpy().astype(int)  # Extract coordinates
-# #             class_id = int(box.cls[0])  # Get the class ID
-# #             confidence = box.conf[0]  # Get the confidence score
-# #
-# #             # Get the class name from YOLO class names
-# #             class_name = model.names[class_id]
-# #
-# #             # Draw rectangle around detected object
-# #             cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)  # Draw rectangle
-# #             cv2.putText(frame, f"{class_name}: {confidence:.2f}", (x1, y1 - 10),
-# #                         cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
-# #
-# #             # Check if attendance can be marked
-# #             current_time = datetime.now()
-# #             if class_name not in attendance_records:
-# #                 # Mark attendance for the first time
-# #                 attendance_records[class_name] = current_time
-# #                 print(f"Attendance marked for {class_name} at {current_time}")
-# #                 # Write to CSV
-# #                 csv_writer.writerow([class_name, current_time.strftime("%Y-%m-%d %H:%M:%S")])
-# #             else:
-# #                 last_attendance_time = attendance_records[class_name]
-# #                 # Check if 24 hours have passed
-# #                 if current_time - last_attendance_time >= timedelta(days=1):
-# #                     attendance_records[class_name] = current_time
-# #                     print(f"Attendance marked for {class_name} at {current_time}")
-# #
-# #                     # Write to CSV
-# #                     csv_writer.writerow([class_name, current_time.strftime("%Y-%m-%d %H:%M:%S")])
-# #                     # csv_file.flush()  # Ensure it's saved immediately
-# #
-# #     # Show the frame with detections
-# #     cv2.imshow("Detected Objects", frame)
-# #
-# #     # Break the loop on 'q' key press
-# #     if cv2.waitKey(1) & 0xFF == ord('q'):
-# #         break
-# #
-# # # Release the video capture object and close all OpenCV windows
-# # cap.release()
-# # cv2.destroyAllWindows()
-# #
-# # # import cv2
-# # # from ultralytics import YOLO
-# # # import time
-# # # from datetime import datetime, timedelta
-# # # import csv
-# # #
-# # # # Load YOLO model
-# # # model = YOLO("D:\\live attendance\\best(attendance).pt")
-# # #
-# # # # Initialize webcam
-# # # cap = cv2.VideoCapture(0)  # 0 is usually the default camera
-# # #
-# # # # Dictionary to store attendance records
-# # # attendance_records = {}
-# # #
-# # # # CSV file to store attendance data
-# # # # csv_file = open("D:\\live attendance\\attendance_data.csv", "w", newline="")
-# # # # csv_writer = csv.writer(csv_file)
-# # # # csv_writer.writerow(["Name", "Time"])
-# # #
-# # # with open('D:\\live attendance\\attendance_data.csv', 'w', newline='') as csv_file:
-# # #     writer = csv.writer(csv_file)
-# # #     writer.writerows(["Name", "Time"])
-# # #
-# # # while True:
-# # #     # Read a frame from the webcam
-# # #     ret, frame = cap.read()
-# # #     if not ret:
-# # #         print("Failed to capture image")
-# # #         break
-# # #
-# # #     # Detect objects
-# # #     results = model(frame)
-# # #
-# # #     # Iterate through results
-# # #     for result in results:
-# # #         boxes = result.boxes
-# # #         for box in boxes:  # Iterate through detected boxes
-# # #             x1, y1, x2, y2 = box.xyxy[0].cpu().numpy().astype(int)  # Extract coordinates
-# # #             class_id = int(box.cls[0])  # Get the class ID
-# # #             confidence = box.conf[0]  # Get the confidence score
-# # #
-# # #             # Get the class name from YOLO class names
-# # #             class_name = model.names[class_id]
-# # #
-# # #             # Draw rectangle around detected object
-# # #             cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)  # Draw rectangle
-# # #             cv2.putText(frame, f"{class_name}: {confidence:.2f}", (x1, y1 - 10),
-# # #                         cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
-# # #
-# # #             # Check if attendance can be marked
-# # #             current_time = datetime.now()
-# # #             if class_name not in attendance_records:
-# # #                 # Mark attendance for the first time
-# # #                 attendance_records[class_name] = current_time
-# # #                 csv_writer.writerow([class_name, current_time.strftime("%Y-%m-%d %H:%M:%S")])  # Save to CSV
-# # #                 print(f"Attendance marked for {class_name} at {current_time}")
-# # #             else:
-# # #                 last_attendance_time = attendance_records[class_name]
-# # #                 # Check if 24 hours have passed
-# # #                 if current_time - last_attendance_time >= timedelta(days=1):
-# # #                     attendance_records[class_name] = current_time
-# # #                     csv_writer.writerow([class_name, current_time.strftime("%Y-%m-%d %H:%M:%S")])  # Save to CSV
-# # #                     print(f"Attendance marked for {class_name} at {current_time}")
-# # #
-# # #     # Show the frame with detections
-# # #     cv2.imshow("Detected Objects", frame)
-# # #
-# # #     # Break the loop on 'q' key press
-# # #     if cv2.waitKey(1) & 0xFF == ord('q'):
-# # #         break
-# # #
-# # # # Release the video capture object and close all OpenCV windows
-# # # cap.release()
-# # # csv_file.close()  # Close the CSV file
-# # # cv2.destroyAllWindows()
-#
-# # import cv2
-# # from ultralytics import YOLO
-# # from datetime import datetime, timedelta
-# # import csv
-# #
-# # # Load YOLO model
-# # model = YOLO("D:\\live attendance\\best(attendance).pt")
-# #
-# # # Initialize webcam
-# # cap = cv2.VideoCapture(0)  # 0 is usually the default camera
-# #
-# # # Dictionary to store attendance records
-# # attendance_records = {}
-# #
-# # # CSV file to store attendance data
-# # csv_file_path = r"D:\\live attendance\\attendance_data.csv"
-# # with open(csv_file_path, "a", newline="") as csv_file:#think here
-# #     csv_writer = csv.writer(csv_file)
-# #
-# #
-# #     while True:
-# #         # Read a frame from the webcam
-# #         ret, frame = cap.read()
-# #         if not ret:
-# #             print("Failed to capture image")
-# #             break
-# #
-# #         # Detect objects
-# #         results = model(frame)
-# #
-# #         # Iterate through results
-# #         for result in results:
-# #             boxes = result.boxes
-# #             for box in boxes:  # Iterate through detected boxes
-# #                 x1, y1, x2, y2 = box.xyxy[0].cpu().numpy().astype(int)  # Extract coordinates
-# #                 class_id = int(box.cls[0])  # Get the class ID
-# #                 confidence = box.conf[0]  # Get the confidence score
-# #
-# #                 # Get the class name from YOLO class names
-# #                 class_name = model.names[class_id]
-# #
-# #                 # Draw rectangle around detected object
-# #                 cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)  # Draw rectangle
-# #                 cv2.putText(frame, f"{class_name}: {confidence:.2f}", (x1, y1 - 10),
-# #                             cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
-# #
-# #                 # Check if attendance can be marked
-# #                 current_time = datetime.now()
-# #                 if class_name not in attendance_records:
-# #                     # Mark attendance for the first time
-# #                     attendance_records[class_name] = current_time#think here
-# #                     print(f"Attendance marked for {class_name} at {current_time}")
-# #                     # Write to CSV
-# #                     csv_writer.writerow([class_name, current_time.strftime("%Y-%m-%d %H:%M:%S")])
-# #                 else:
-# #                     last_attendance_time = attendance_records[class_name]
-# #                     # Check if 24 hours have passed
-# #                     if current_time - last_attendance_time >= timedelta(days=1):
-# #                         attendance_records[class_name] = current_time
-# #                         print(f"Attendance marked for {class_name} at {current_time}")
-# #                         # Write to CSV
-# #                         csv_writer.writerow([class_name, current_time.strftime("%Y-%m-%d %H:%M:%S")])
-# #
-# #         # Show the frame with detections
-# #         cv2.imshow("Detected Objects", frame)
-# #
-# #         # Break the loop on 'q' key press
-# #         if cv2.waitKey(1) & 0xFF == ord('q'):
-# #             break
-# #
-# # # Release the video capture object and close all OpenCV windows
-# # cap.release()
-# # cv2.destroyAllWindows()
-#
-#
-# # import cv2
-# # import pandas as pd
-# # from ultralytics import YOLO
-# # from datetime import datetime, timedelta
-# #
-# # # Load the model
-# # model = YOLO("D:\\live attendance\\best(attendance).pt")
-# #
-# # # Open camera (0 for default camera, 1 for external camera)
-# # cap = cv2.VideoCapture(0)
-# #
-# # # Initialize lists to store class names and timestamps
-# # class_names_list = []
-# # time_list = []
-# #
-# # # Initialize a list to keep track of last attendance times
-# # last_attendance_times = []
-# #
-# # while True:
-# #     # Read a frame from the webcam
-# #     ret, frame = cap.read()
-# #     if not ret:
-# #         print("Failed to capture image")
-# #         break
-# #
-# #     # Detect objects
-# #     results = model(frame)
-# #
-# #     # Iterate through results
-# #     for result in results:
-# #         boxes = result.boxes
-# #         for box in boxes:  # Iterate through detected boxes
-# #             x1, y1, x2, y2 = box.xyxy[0].cpu().numpy().astype(int)  # Extract coordinates
-# #             class_id = int(box.cls[0])  # Get the class ID
-# #             confidence = box.conf[0]  # Get the confidence score
-# #
-# #             # Get the class name from YOLO class names
-# #             class_name = model.names[class_id]
-# #
-# #             # Draw rectangle around detected object
-# #             cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)  # Draw rectangle
-# #             cv2.putText(frame, f"{class_name}: {confidence:.2f}", (x1, y1 - 10),
-# #                         cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
-# #
-# #             # Get the current time
-# #             current_time = datetime.now()
-# #             current_time_str = current_time.strftime("%Y-%m-%d %H:%M:%S")
-# #
-# #             # Check if the class name is already in the list
-# #             if class_name not in class_names_list:
-# #                 # Mark attendance for the first time
-# #                 class_names_list.append(class_name)
-# #                 time_list.append(current_time_str)
-# #                 last_attendance_times.append( class_names_list+time_list)  # Store the time of attendance
-# #                 print(f"Attendance marked for {class_name} at {current_time_str}")
-# #             else:
-# #
-# #                 # Check if 24 hours have passed since last recorded attendance
-# #                 if current_time - last_attendance_times[class_name] >= timedelta(days=1):
-# #                     last_attendance_times[class_name] = current_time  # Update the last attendance time
-# #                     time_list[class_names_list.index(class_name)] = current_time_str  # Update the time in the list
-# #                     print(f"Attendance marked for {class_name} at {current_time_str}")
-# #
-# #     # Display output
-# #     cv2.imshow("Object Detection", frame)
-# #
-# #     # Exit on 'q' press
-# #     if cv2.waitKey(1) & 0xFF == ord('q'):
-# #         break
-# #
-# # # Release camera and close window
-# # cap.release()
-# # cv2.destroyAllWindows()
-# #
-# # # Save class names and timestamps to CSV
-# # df = pd.DataFrame({
-# #     "Class Name": class_names_list,
-# #     "Time": time_list
-# # })
-# # df.to_csv("D:\\live attendance\\attendance_data.csv", index=False)
-# #
-# # print("Detections saved ")
-#
-# # import cv2
-# # import pandas as pd
-# # from ultralytics import YOLO
-# # from datetime import datetime
-# #
-# # # Load the YOLO model
-# # model = YOLO("D:\\live attendance\\best(attendance).pt")
-# #
-# # # Open camera (0 for default camera, 1 for external camera)
-# # cap = cv2.VideoCapture(0)
-# #
-# # # Initialize lists to store class names and timestamps
-# # class_names_list = []
-# # time_list = []
-# #
-# # while True:
-# #     # Read a frame from the webcam
-# #     ret, frame = cap.read()
-# #     if not ret:
-# #         print("Failed to capture image")
-# #         break
-# #
-# #     # Detect objects
-# #     results = model(frame)
-# #
-# #     # Check if any results are detected
-# #     if results:  # Check if results are not empty
-# #         for result in results:
-# #             boxes = result.boxes
-# #             for box in boxes:  # Iterate through detected boxes
-# #                 x1, y1, x2, y2 = box.xyxy[0].cpu().numpy().astype(int)  # Extract coordinates
-# #                 class_id = int(box.cls[0])  # Get the class ID
-# #                 confidence = box.conf[0]  # Get the confidence score
-# #
-# #                 # Get the class name from YOLO class names
-# #                 class_name = model.names[class_id]
-# #
-# #                 # Draw rectangle around detected object
-# #                 cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)  # Draw rectangle
-# #                 cv2.putText(frame, f"{class_name}: {confidence:.2f}", (x1, y1 - 10),
-# #                             cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
-# #
-# #                 # Get the current time
-# #                 current_time = datetime.now()
-# #                 current_time_str = current_time.strftime("%Y-%m-%d %H:%M:%S")
-# #
-# #                 # Record attendance for every detected class
-# #                 if class_name not in class_names_list:
-# #                     class_names_list.append(class_name)
-# #                     time_list.append(current_time_str)
-# #                     print(f"Attendance marked for {class_name} at {current_time_str}")
-# #                 else:
-# #                     print(f"{class_name} already recorded.")
-# #
-# #     else:
-# #         print("No results detected.")
-# #
-# #     # Display output
-# #     cv2.imshow("Object Detection", frame)
-# #
-# #     # Exit on 'q' press
-# #     if cv2.waitKey(1) & 0xFF == ord('q'):
-# #         break
-# #
-# # # Release camera and close window
-# # cap.release()
-# # cv2.destroyAllWindows()
-# #
-# # # Check if lists are populated before saving
-# # print("Class Names:", class_names_list)
-# # print("Time List:", time_list)
-# #
-# # # Save class names and timestamps to CSV
-# # if class_names_list and time_list:  # Only save if there is data
-# #     df = pd.DataFrame({
-# #         "Class Name": class_names_list,
-# #         "Time": time_list
-# #     })
-# #     try:
-# #         df.to_csv("D:\\live attendance\\attendance_data.csv", index=False)
-# #         print("Detections saved to CSV.")
-# #     except Exception as e:
-# #         print(f"Error saving to CSV: {e}")
-# # else:
-# #     print("No attendance data to save.")
-#
-#
-# import cv2
-# import pandas as pd
-# from ultralytics import YOLO
-# from datetime import datetime, timedelta
-#
-# # Load the YOLO model
-# model = YOLO("D:\\live attendance\\best(attendance).pt")
-#
-# # model = YOLO("C:\\Users\\Dell\\Downloads\\best (2).pt")
-#
-# # Open camera (0 for default camera, 1 for external camera)
-# cap = cv2.VideoCapture(0)
-#
-# # Initialize lists to store class names and timestamps
-# class_names_list = []
-# time_list = []
-#
-# while True:
-#     # Read a frame from the webcam
-#     ret, frame = cap.read()
-#     if not ret:
-#         print("Failed to capture image")
-#         break
-#
-#     # Detect objects
-#     results = model(frame)
-#
-#     # Check if any results are detected
-#     if results:  # Check if results are not empty
-#         for result in results:
-#             boxes = result.boxes
-#             for box in boxes:  # Iterate through detected boxes
-#                 x1, y1, x2, y2 = box.xyxy[0].cpu().numpy().astype(int)  # Extract coordinates
-#                 class_id = int(box.cls[0])  # Get the class ID
-#                 confidence = box.conf[0]  # Get the confidence score
-#
-#                 # Get the class name from YOLO class names
-#                 class_name = model.names[class_id]
-#
-#                 # Draw rectangle around detected object
-#                 cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)  # Draw rectangle
-#                 cv2.putText(frame, f"{class_name}: {confidence:.2f}", (x1, y1 - 10),
-#                             cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
-#
-#                 # Get the current time
-#                 current_time = datetime.now()
-#                 current_time_str = current_time.strftime("%Y-%m-%d %H:%M:%S")
-#
-#                 # Check if the class has been recorded and if 24 hours have passed
-#                 if class_name in class_names_list:
-#                     index = class_names_list.index(class_name)
-#                     last_recorded_time = datetime.strptime(time_list[index], "%Y-%m-%d %H:%M:%S")
-#
-#                     # If less than 24 hours have passed since the last recording
-#                     if (current_time - last_recorded_time) < timedelta(hours=24):
-#                         print(f"{class_name} already recorded within the last 24 hours.")
-#                         continue
-#
-#                 # Record attendance for the class
-#                 class_names_list.append(class_name)
-#                 time_list.append(current_time_str)
-#                 print(f"Attendance marked for {class_name} at {current_time_str}")
-#
-#     else:
-#         print("No results detected.")
-#
-#     # Display output
-#     cv2.imshow("Object Detection", frame)
-#
-#     # Exit on 'q' press
-#     if cv2.waitKey(1) & 0xFF == ord('q'):
-#         break
-#
-# # Release camera and close window
-# cap.release()
-# cv2.destroyAllWindows()
-#
-# # Check if lists are populated before saving
-# print("Class Names:", class_names_list)
-# print("Time List:", time_list)
-#
-# # Save class names and timestamps to CSV
-# if class_names_list and time_list:  # Only save if there is data
-#     df = pd.DataFrame({
-#         "Class Name": class_names_list,
-#         "Time": time_list
-#     })
-#     try:
-#         df.to_csv("D:\\live attendance\\attendance_data.csv", index=False)
-#         print("Detections saved to CSV.")
-#     except Exception as e:
-#         print(f"Error saving to CSV: {e}")
-# else:
-#     print("No attendance data to save.")
-
-
+import os
 import cv2
 import pandas as pd
 import gradio as gr
 from ultralytics import YOLO
 from datetime import datetime, timedelta
-import os
 import numpy as np
 
-
 class AttendanceSystem:
     def __init__(self, model_path, csv_path):
         self.model_path = model_path
@@ -623,33 +126,21 @@ class AttendanceSystem:
         return True, "No records to clear"
 
 
-# Function to capture webcam input
-def capture_webcam():
-    cap = cv2.VideoCapture(0)
-    if not cap.isOpened():
-        return None
-    ret, frame = cap.read()
-    cap.release()
-    if ret:
-        return frame
-    return None
-
-
-# Function for Gradio interface
-def process_webcam(state):
+# Function for Gradio interface - process images from webcam
+def process_webcam_image(image, state):
     if state is None:
-        # Default paths - update these to match your system
-        model_path = "best(attendance).pt"
+        # Default paths - update these to match your Hugging Face deployment
+        model_path = "best(attendance).pt"  # Make sure to upload this model to your Space
         csv_path = "attendance_data.csv"
         state = AttendanceSystem(model_path, csv_path)
+        # Load model immediately
+        state.load_model()
 
-    # Capture frame from webcam
-    frame = capture_webcam()
-    if frame is None:
-        return None, "Failed to capture webcam frame", "", state
+    if image is None:
+        return None, "No image received", "", state
 
     # Process the frame
-    processed_frame, message, attendance_data, detected_names = state.process_frame(frame)
+    processed_frame, message, attendance_data, detected_names = state.process_frame(image)
 
     # Format attendance as HTML table for better display
     if attendance_data:
@@ -683,17 +174,18 @@ def change_model_path(model_path, csv_path, state):
     return message, state
 
 
-# Create Gradio interface - compatible with older Gradio versions
+# Create Gradio interface that works on Hugging Face
 with gr.Blocks(title="Attendance System") as app:
     gr.Markdown("# Automated Attendance System")
     gr.Markdown("This system uses YOLO to detect and record attendance of individuals.")
 
     with gr.Row():
         with gr.Column(scale=2):
-            # For older Gradio versions, use a button to capture webcam
-            webcam_button = gr.Button("Capture from Webcam")
+            # Use Gradio's webcam component which captures on the client side
+            input_image = gr.Image(source="webcam", label="Webcam Feed")
+            process_button = gr.Button("Process Image")
             output_image = gr.Image(label="Processed Feed")
-            status_text = gr.Textbox(label="Status", value="Click 'Capture from Webcam' to start")
+            status_text = gr.Textbox(label="Status", value="Capture an image and click 'Process Image'")
 
         with gr.Column(scale=1):
             # Attendance records and controls
@@ -710,15 +202,28 @@ with gr.Blocks(title="Attendance System") as app:
     state = gr.State(None)
 
     # Set up event handlers
-    webcam_button.click(process_webcam, inputs=[state],
-                        outputs=[output_image, status_text, attendance_display, state])
-
-    clear_button.click(clear_attendance_records, inputs=[state],
-                       outputs=[status_text, attendance_display, state])
-
-    update_paths_button.click(change_model_path, inputs=[model_path_input, csv_path_input, state],
-                              outputs=[status_text, state])
+    process_button.click(
+        process_webcam_image, 
+        inputs=[input_image, state],
+        outputs=[output_image, status_text, attendance_display, state]
+    )
+
+    clear_button.click(
+        clear_attendance_records, 
+        inputs=[state],
+        outputs=[status_text, attendance_display, state]
+    )
+
+    update_paths_button.click(
+        change_model_path, 
+        inputs=[model_path_input, csv_path_input, state],
+        outputs=[status_text, state]
+    )
+
+# Create a requirements.txt file for Hugging Face
+with open('requirements.txt', 'w') as f:
+    f.write('opencv-python-headless\npandas\ngradio\nultralytics')
 
 # Launch the app
 if __name__ == "__main__":
-    app.launch(share=True)  # Set share=False if you don't want to create a public link
+    app.launch()  # Remove share=True for Hugging Face deployment