up v9

app.py

@@ -1,23 +1,9 @@
 import gradio as gr
-from models import make_inpainting
 import io
 from PIL import Image
 import numpy as np
-
-from PIL import Image
-from typing import Union
-import random
-import numpy as np
-import os
-import time
-
 from models import make_image_controlnet, make_inpainting
-from segmentation import segment_image
-from config import HEIGHT, WIDTH, POS_PROMPT, NEG_PROMPT, COLOR_MAPPING, map_colors, map_colors_rgb
-from palette import COLOR_MAPPING_CATEGORY
 from preprocessing import preprocess_seg_mask, get_image, get_mask
-from explanation import make_inpainting_explanation, make_regeneration_explanation, make_segmentation_explanation
-
 
 def image_to_byte_array(image: Image) -> bytes:
     # BytesIO is a fake file stored in memory
@@ -29,21 +15,19 @@ def image_to_byte_array(image: Image) -> bytes:
     return imgByteArr
 
 def predict(input_img1,input_img2):
-
-    # image = Image.open(requests.get("https://applydesignblobs-chh5aahjdzh0cnew.z01.azurefd.net/spaceimages/org_sqr_7fee0869-3187-4363-b5fb-5233e943649d.png", stream=True).raw)
-    # mask = Image.open(requests.get("https://applydesign.blob.core.windows.net/spaceimages/mask_e85b1585-8.png", stream=True).raw)
+    print("predict")
 
     canvas_mask = np.array(input_img2)
     mask = get_mask(canvas_mask)
 
+    print(input_img1, mask)
+
     result_image = make_inpainting(positive_prompt='test1',
-                                   image=Image.fromarray(input_img1),
+                                   image=input_img1,
                                    mask_image=mask,
                                    negative_prompt="xxx",
                                    )
 
-
-    # predictions = pipeline(input_img1)
     return result_image
 
 gradio_app = gr.Interface(