up v5

app.py

@@ -1,25 +1,23 @@
-# import gradio as gr
-#
-# def greet(name):
-#     return "V5 Hello " + name + "!!"
-#
-# iface = gr.Interface(
-#     fn=greet,
-#     inputs="text",
-#     outputs="text",
-#     title="MB TEST 1",
-# )
-# iface.launch(share=True)
-
 import gradio as gr
 from models import make_inpainting
 import io
 from PIL import Image
 import numpy as np
 
-# from transformers import pipeline
-#
-# pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
+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
@@ -43,7 +41,7 @@ def predict(input_img1,input_img2):
 
 
     # predictions = pipeline(input_img1)
-    return input_img1
+    return result_image
 
 gradio_app = gr.Interface(
     predict,
@@ -54,6 +52,5 @@ gradio_app = gr.Interface(
     title="rem fur 1",
 )
 
-
 gradio_app.launch(share=True)