Changes Made Keys p3

app.py

@@ -7,7 +7,6 @@ import pickle
 import firebase_admin
 from firebase_admin import credentials
 from firebase_admin import db
-from datetime import datetime
 from firebase_admin import storage
 
 # Initialize Firebase
@@ -32,53 +31,51 @@ def recognize_face(input_image):
     # Convert PIL Image to numpy array
     img = np.array(input_image)
     img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
-
     # Detect faces and encode
     face_locations = face_recognition.face_locations(img)
     face_encodings = face_recognition.face_encodings(img, face_locations)
-
     # Initialize the database reference
     ref = db.reference('Students')
-    recognized_faces_info = []
-
-    for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
+    
+    # Recognize faces and fetch data from the database
+    results = []
+    for face_encoding in face_encodings:
         matches = face_recognition.compare_faces(encodeListKnown, face_encoding)
         name = "Unknown"
+        student_info = {}
 
         face_distances = face_recognition.face_distance(encodeListKnown, face_encoding)
         best_match_index = np.argmin(face_distances)
         if matches[best_match_index]:
             student_id = studentsIds[best_match_index]
-            student_ref = ref.child(student_id)
-            student_info = student_ref.get()
+            student_info = ref.child(student_id).get()
 
             if student_info:
                 name = student_info['name']
-                # Increment total_attendance
-                student_info['total_attendance'] += 1
-                # Update last attendance time
-                student_info['last_attendance_time'] = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-                # Write back to the database
-                student_ref.update(student_info)
-                recognized_faces_info.append(student_info)
+                results.append(student_info)
             else:
-                recognized_faces_info.append({'name': 'Unknown'})
+                results.append({'name': 'Unknown'})
         
         # Draw rectangles around the faces
-        cv2.rectangle(img, (left, top), (right, bottom), (0, 0, 255), 2)
-        cv2.putText(img, name, (left + 6, bottom - 6), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1)
+        for (top, right, bottom, left), name in zip(face_locations, [student_info.get('name', 'Unknown') for student_info in results]):
+            cv2.rectangle(img, (left, top), (right, bottom), (0, 0, 255), 2)
+            cv2.putText(img, name, (left + 6, bottom - 6), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1)
 
     # Convert back to PIL Image
     pil_img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
-    return pil_img, recognized_faces_info
-
+    return pil_img, results
+# Define a function to handle webcam images
+def process_webcam_image(image):
+    # Convert the base64 image to a format that can be processed
+    # Process the image through the face recognition function
+    return recognize_face(image)
 # Gradio interface
 iface = gr.Interface(
     fn=recognize_face,
-    inputs=gr.components.Image(source="webcam", type="pil", tool="editor"),
+    inputs=gr.Image(tool="editor", source="webcam"),
     outputs=[
-        gr.components.Image(type="pil"),
-        gr.components.JSON(label="Student Information")
+        gr.Image(),
+        gr.JSON(label="Student Information")
     ],
     title="Real-time Face Recognition Attendance System",
     description="Activate your webcam and take a photo to check attendance."

app1.py

@@ -1,85 +0,0 @@
-import gradio as gr
-import face_recognition
-import cv2
-import numpy as np
-from PIL import Image
-import pickle
-import firebase_admin
-from firebase_admin import credentials
-from firebase_admin import db
-from firebase_admin import storage
-
-# Initialize Firebase
-cred = credentials.Certificate("serviceAccountKey.json")  # Update with your credentials path
-firebase_app = firebase_admin.initialize_app(cred, {
-    'databaseURL': 'https://faceantendancerealtime-default-rtdb.firebaseio.com/',
-    'storageBucket': 'faceantendancerealtime.appspot.com'
-})
-bucket = storage.bucket()
-
-# Function to download face encodings from Firebase Storage
-def download_encodings():
-    blob = bucket.blob('EncodeFile.p')
-    blob.download_to_filename('EncodeFile.p')
-    with open('EncodeFile.p', 'rb') as file:
-        return pickle.load(file)
-
-encodeListKnownWithIds = download_encodings()
-encodeListKnown, studentsIds = encodeListKnownWithIds
-
-def recognize_face(input_image):
-    # Convert PIL Image to numpy array
-    img = np.array(input_image)
-    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
-    # Detect faces and encode
-    face_locations = face_recognition.face_locations(img)
-    face_encodings = face_recognition.face_encodings(img, face_locations)
-    # Initialize the database reference
-    ref = db.reference('Students')
-    
-    # Recognize faces and fetch data from the database
-    results = []
-    for face_encoding in face_encodings:
-        matches = face_recognition.compare_faces(encodeListKnown, face_encoding)
-        name = "Unknown"
-        student_info = {}
-
-        face_distances = face_recognition.face_distance(encodeListKnown, face_encoding)
-        best_match_index = np.argmin(face_distances)
-        if matches[best_match_index]:
-            student_id = studentsIds[best_match_index]
-            student_info = ref.child(student_id).get()
-
-            if student_info:
-                name = student_info['name']
-                results.append(student_info)
-            else:
-                results.append({'name': 'Unknown'})
-        
-        # Draw rectangles around the faces
-        for (top, right, bottom, left), name in zip(face_locations, [student_info.get('name', 'Unknown') for student_info in results]):
-            cv2.rectangle(img, (left, top), (right, bottom), (0, 0, 255), 2)
-            cv2.putText(img, name, (left + 6, bottom - 6), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1)
-
-    # Convert back to PIL Image
-    pil_img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
-    return pil_img, results
-# Define a function to handle webcam images
-def process_webcam_image(image):
-    # Convert the base64 image to a format that can be processed
-    # Process the image through the face recognition function
-    return recognize_face(image)
-# Gradio interface
-iface = gr.Interface(
-    fn=recognize_face,
-    inputs=gr.Image(tool="editor", source="webcam"),
-    outputs=[
-        gr.Image(),
-        gr.JSON(label="Student Information")
-    ],
-    title="Real-time Face Recognition Attendance System",
-    description="Activate your webcam and take a photo to check attendance."
-)
-
-if __name__ == "__main__":
-    iface.launch()