diff --git a/deepface/DeepFace.py b/deepface/DeepFace.py
index f519669..23aed72 100644
--- a/deepface/DeepFace.py
+++ b/deepface/DeepFace.py
@@ -65,7 +65,7 @@ def build_model(model_name):
 
 	return model_obj[model_name]
 
-def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'opencv', align = True):
+def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'opencv', align = True, prog_bar = True):
 
 	"""
 	This function verifies an image pair is same person or different persons.
@@ -90,6 +90,8 @@ def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric =
 
 		detector_backend (string): set face detector backend as retinaface, mtcnn, opencv, ssd or dlib
 
+		prog_bar (boolean): enable/disable a progress bar
+
 	Returns:
 		Verify function returns a dictionary. If img1_path is a list of image pairs, then the function will return list of dictionary.
 
@@ -138,8 +140,7 @@ def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric =
 
 	#------------------------------
 
-	#calling deepface in a for loop causes lots of progress bars. this prevents it.
-	disable_option = False if len(img_list) > 1 else True
+	disable_option = (False if len(img_list) > 1 else True) or not prog_bar
 
 	pbar = tqdm(range(0,len(img_list)), desc='Verification', disable = disable_option)
 
@@ -279,7 +280,7 @@ def analyze(img_path, actions = ['emotion', 'age', 'gender', 'race'] , models =
 		enforce_detection (boolean): The function throws exception if a face could not be detected. Set this to True if you don't want to get exception. This might be convenient for low resolution images.
 
 		detector_backend (string): set face detector backend as retinaface, mtcnn, opencv, ssd or dlib.
-		
+
 		prog_bar (boolean): enable/disable a progress bar
 	Returns:
 		The function returns a dictionary. If img_path is a list, then it will return list of dictionary.
@@ -456,7 +457,7 @@ def analyze(img_path, actions = ['emotion', 'age', 'gender', 'race'] , models =
 
 		return resp_obj
 
-def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'opencv', align = True):
+def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'opencv', align = True, prog_bar = True):
 
 	"""
 	This function applies verification several times and find an identity in a database
@@ -478,6 +479,8 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
 
 		detector_backend (string): set face detector backend as retinaface, mtcnn, opencv, ssd or dlib
 
+		prog_bar (boolean): enable/disable a progress bar
+
 	Returns:
 		This function returns pandas data frame. If a list of images is passed to img_path, then it will return list of pandas data frame.
 	"""
@@ -552,7 +555,7 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
 
 			representations = []
 
-			pbar = tqdm(range(0,len(employees)), desc='Finding representations')
+			pbar = tqdm(range(0,len(employees)), desc='Finding representations', disable = prog_bar)
 
 			#for employee in employees:
 			for index in pbar:
@@ -597,7 +600,7 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
 
 		resp_obj = []
 
-		global_pbar = tqdm(range(0,len(img_paths)), desc='Analyzing')
+		global_pbar = tqdm(range(0, len(img_paths)), desc='Analyzing', disable = prog_bar)
 		for j in global_pbar:
 			img_path = img_paths[j]