Changes Made Keys p3

app.py

@@ -31,18 +31,18 @@ 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:
+    ref = db.reference('Students')
+    recognized_faces_info = []
+
+    for (top, right, bottom, left), face_encoding in zip(face_locations, 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)
@@ -52,43 +52,29 @@ def recognize_face(input_image):
 
             if student_info:
                 name = student_info['name']
-                results.append(student_info)
+                recognized_faces_info.append(student_info)
             else:
-                results.append({'name': 'Unknown'})
+                recognized_faces_info.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)
+        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(type="pil"),
-#     outputs=[gr.Image(type="pil"), gr.JSON(label="Student Information")],
-#     title="Face Recognition Attendance System",
-#     description="Upload an image to identify individuals."
-# )
+    return pil_img, recognized_faces_info
 
-# if __name__ == "__main__":
-#     iface.launch(debug=True,inline=False)
-# Create the Gradio interface
+# Gradio interface
 iface = gr.Interface(
     fn=recognize_face,
     inputs=gr.components.Image(source="webcam", type="pil", tool="editor"),
-    outputs=[gr.components.Image(type="pil"), gr.components.Label()],
+    outputs=[
+        gr.components.Image(type="pil"),
+        gr.components.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(share=True,debug=True,inline=False)
+    iface.launch(share=True)