Changes Made Keys p3

app.py

@@ -6,14 +6,14 @@ 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_admin.initialize_app(cred,{
-    'databaseURL': 'https://faceantendancerealtime-default-rtdb.firebaseio.com/', #From Real time database link
-    'storageBucket': 'faceantendancerealtime.appspot.com' #from storage link (except https://)
-
+firebase_app = firebase_admin.initialize_app(cred, {
+    'databaseURL': 'https://faceantendancerealtime-default-rtdb.firebaseio.com/',
+    'storageBucket': 'faceantendancerealtime.appspot.com'
 })
 bucket = storage.bucket()
 
@@ -31,36 +31,48 @@ 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)
-
-    # Recognize faces
-    for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
+    # 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]:
-            name = studentsIds[best_match_index]
+            student_id = studentsIds[best_match_index]
+            student_info = ref.child(student_id).get()
 
-        # Draw rectangle and label
-        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)
+            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
-    return Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
+    pil_img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
+    return pil_img, results
 
 # Gradio interface
 iface = gr.Interface(
     fn=recognize_face,
     inputs=gr.Image(type="pil"),
-    outputs=gr.Image(type="pil"),
+    outputs=[gr.Image(type="pil"), gr.JSON(label="Student Information")],
     title="Face Recognition Attendance System",
     description="Upload an image to identify individuals."
 )
 
 if __name__ == "__main__":
-    iface.launch(inline=False, share=True)
+    iface.launch(debug=True,inline=False)