np array

app.py

@@ -2,6 +2,8 @@ import gradio as gr
 import io
 from PIL import Image
 import numpy as np
+
+from config import WIDTH, HEIGHT
 from models import make_image_controlnet, make_inpainting
 from preprocessing import preprocess_seg_mask, get_image, get_mask
 
@@ -14,12 +16,15 @@ def image_to_byte_array(image: Image) -> bytes:
     imgByteArr = imgByteArr.getvalue()
     return imgByteArr
 
-def predict(input_img1,input_img2):
+def predict(input_img1,
+            input_img2):
 
     print("predict")
+    input_img1 = Image.fromarray(input_img1)
+    input_img2 = Image.fromarray(input_img2)
 
-    input_img1 = input_img1.resize((512, 512))
-    input_img2 = input_img2.resize((512, 512))
+    input_img1 = input_img1.resize((WIDTH, HEIGHT))
+    input_img2 = input_img2.resize((WIDTH, WIDTH))
 
     canvas_mask = np.array(input_img2)
     mask = get_mask(canvas_mask)
@@ -36,8 +41,8 @@ def predict(input_img1,input_img2):
 
 gradio_app = gr.Interface(
     predict,
-    inputs=[gr.Image(label="img", sources=['upload', 'webcam'], type="pil"),
-            gr.Image(label="mask", sources=['upload', 'webcam'], type="pil")
+    inputs=[gr.Image(label="img", sources=['upload', 'webcam'], type="numpy"),
+            gr.Image(label="mask", sources=['upload', 'webcam'], type="numpy")
             ],
     outputs= gr.Image(label="resp"),
     title="rem fur 1",