Update app.py

app.py

@@ -4,11 +4,11 @@ import torch
 import gradio as gr
 os.system("pip install gradio==2.5.3")
 
-os.system("pip install facexlib")
+#os.system("pip install facexlib")
 
-from facexlib.utils.face_restoration_helper import FaceRestoreHelper
+#from facexlib.utils.face_restoration_helper import FaceRestoreHelper
 #os.system("pip install autocrop")
-#os.system("pip install dlib")
+os.system("pip install dlib")
 #from autocrop import Cropper
 import torch
 torch.backends.cudnn.benchmark = True
@@ -34,19 +34,19 @@ os.makedirs('style_images', exist_ok=True)
 os.makedirs('style_images_aligned', exist_ok=True)
 os.makedirs('models', exist_ok=True)
 
-#os.system("wget http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2")
-#os.system("bzip2 -dk shape_predictor_68_face_landmarks.dat.bz2")
-#os.system("mv shape_predictor_68_face_landmarks.dat models/dlibshape_predictor_68_face_landmarks.dat")
+os.system("wget http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2")
+os.system("bzip2 -dk shape_predictor_68_face_landmarks.dat.bz2")
+os.system("mv shape_predictor_68_face_landmarks.dat models/dlibshape_predictor_68_face_landmarks.dat")
 
 #cropper = Cropper(face_percent=80)
 
-face_helper = FaceRestoreHelper(
-        upscale_factor=0,
-        face_size=512,
-        crop_ratio=(1, 1),
-        det_model='retinaface_resnet50',
-        save_ext='png',
-        device='cpu')
+#face_helper = FaceRestoreHelper(
+        #upscale_factor=0,
+        #face_size=512,
+        #crop_ratio=(1, 1),
+        #det_model='retinaface_resnet50',
+        #save_ext='png',
+        #device='cpu')
 
 device = 'cpu' 
 
@@ -97,8 +97,8 @@ generatorjinx.load_state_dict(ckptjinx["g"], strict=False)
 
 
 def inference(img, model):    
-    face_helper.clean_all()
-    #aligned_face = align_face(img)
+    #face_helper.clean_all()
+    aligned_face = align_face(img)
     #cropped_array = cropper.crop(img[:,:,::-1])
     
     #if cropped_array.any():
@@ -106,12 +106,12 @@ def inference(img, model):
     #else:
         #aligned_face = Image.fromarray(img[:,:,::-1])
     
-    face_helper.read_image(img)
-    face_helper.get_face_landmarks_5(only_center_face=False, eye_dist_threshold=10)
-    face_helper.align_warp_face(save_cropped_path="/home/user/app/")
-    pilimg = Image.open("/home/user/app/_02.png")
+    #face_helper.read_image(img)
+    #face_helper.get_face_landmarks_5(only_center_face=False, eye_dist_threshold=10)
+    #face_helper.align_warp_face(save_cropped_path="/home/user/app/")
+    #pilimg = Image.open("/home/user/app/_02.png")
     
-    my_w = e4e_projection(pilimg, "test.pt", device).unsqueeze(0)
+    my_w = e4e_projection(aligned_face, "test.pt", device).unsqueeze(0)
     if model == 'JoJo':
         with torch.no_grad():
             my_sample = generatorjojo(my_w, input_is_latent=True)