Update app.py

app.py

@@ -6,6 +6,8 @@ import numpy as np
 import os
 import sqlite3
 from datetime import datetime
+from huggingface_hub import Repository
+import pandas as pd
 
 # Establish connection to SQLite database
 conn = sqlite3.connect('attendance.db')
@@ -44,6 +46,43 @@ def add_attendance(name):
     c.execute("INSERT INTO attendance (name, time) VALUES (?, ?)", (username, current_datetime))
     conn.commit()
 
+def addAttendance(name):
+    directory_name = "my_directory"
+
+    # Check if the repository exists, if not, create it
+    if not Repository.exists(directory_name):
+        repo = Repository.create(directory_name)
+        print(f"Repository '{directory_name}' created successfully!")
+    else:
+        repo = Repository()
+    
+    # Create the directory
+    repo.create_directory(directory_name)
+    
+    # Define recognized results (example)
+    recognized_results = [
+        {"Name": "{name}", "Status": "Present"},
+        {"Name": "Alice", "Status": "Absent"},
+        {"Name": "Bob", "Status": "Present"}
+    ]
+    
+    # Convert recognized results to DataFrame
+    df = pd.DataFrame(recognized_results)
+    
+    # Define the path to save the Excel file
+    excel_file_path = "recognized_results.xlsx"
+    
+    # Save DataFrame to Excel file
+    df.to_excel(excel_file_path, index=False)
+    
+    # Define the destination path within the directory
+    destination_path = os.path.join(directory_name, excel_file_path)
+    
+    # Move the Excel file to the destination path
+    os.system(f"mv {excel_file_path} {destination_path}")
+    
+    print(f"Excel file '{excel_file_path}' added to directory '{directory_name}' successfully!")
+
 if file_name is not None:
     col1, col2 = st.columns(2)
 
@@ -84,7 +123,7 @@ if file_name is not None:
             cv2.putText(image, name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
             
             # Store attendance in SQLite database
-            add_attendance(name)
+            addAttendance(name)
             
         # Display the image with recognized faces
         st.image(image, use_column_width=True, output_format="PNG")