Update app.py

app.py

@@ -15,7 +15,7 @@ from PIL import Image, ImageOps
 import numpy as np
 import imageio
 os.mkdir("data")
-# os.rename("best.ckpt", "models/best.ckpt")
+os.rename("best.ckpt", "models/best.ckpt")
 os.mkdir("dataout")
 
 # Load CLIPSeg model
@@ -42,10 +42,12 @@ def process_image(image, prompt):
     # Perform inpainting with LAMA
     # input_dict = {"image": image, "mask": mask_image}
     # imageio.imwrite("./data/data_mask.png", input_dict["mask"])
-    # os.system('python predict.py model.path=/home/user/app/ indir=/home/user/app/data/ outdir=/home/user/app/dataout/ device=cpu')
+    imageio.imwrite("./data/data_mask.png", mask_image)
+    os.system('python predict.py model.path=/home/user/app/ indir=/home/user/app/data/ outdir=/home/user/app/dataout/ device=cpu')
     # # inpainted_image = lama_model.inference(data=input_dict)["data"][0]
+    inpainted_image = "./data/data_mask.png"
     
-    inpainted_image = Image.new('RGB', (image.shape[1], image.shape[0]), (0, 0, 0))
+    # inpainted_image = Image.new('RGB', (image.shape[1], image.shape[0]), (0, 0, 0))
     # inpainted_image = cv2.cvtColor(inpainted_image, cv2.COLOR_BGR2RGB)
     # inpainted_image = Image.fromarray(inpainted_image)
 
@@ -53,7 +55,7 @@ def process_image(image, prompt):
 
 interface = gr.Interface(fn=process_image, 
                      inputs=[gr.Image(type="pil"), gr.Textbox(label="Please describe what you want to identify")],
-                     outputs=[gr.Image(type="pil"), gr.Image(type="pil")],
+                     outputs=[gr.Image(type="pil"), gr.Image(type="file",label="output")],
                      title="Interactive demo: zero-shot image segmentation with CLIPSeg and inpainting with LAMA",
                      description="Demo for using CLIPSeg and LAMA to perform zero- and one-shot image segmentation and inpainting. To use it, simply upload an image and add a text to mask (identify in the image), or use one of the examples below and click 'submit'. Results will show up in a few seconds.")