Update app.py

app.py

@@ -1,20 +1,54 @@
+import os
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
 from gradio_client import Client, handle_file
+from PIL import Image
+
+def predict(imgs, garm_img):
+  print(imgs, garm_img)
+  client = Client("life4cut/ff-v1", hf_token=HF_TOKEN_FF)
+  result = client.predict(
+      #dict={"background":handle_file('https://s3.ap-northeast-2.amazonaws.com/life4cut.app/images/temp/111.png'),"layers":[],"composite":None},
+      #garm_img=handle_file('https://s3.ap-northeast-2.amazonaws.com/life4cut.app/images/temp/00_style5.png'),
+      dict={"background":handle_file(imgs),"layers":[],"composite":None},
+      garm_img=handle_file(garm_img),
+      garment_des="Hello!!",
+      is_checked=True,
+      is_checked_crop=False,
+      denoise_steps=30,
+      seed=42,
+      api_name="/tryon"
+  )
+  #print(result)
+  return result[0], result[1]
+#print(result[1])
+
+# View the image
+#Image.open(result[0])
+#Image.open(result)
+#example_path = os.path.join(os.path.dirname(__file__), 'example')
+#garm_list = os.listdir(os.path.join(example_path,"cloth"))
+#garm_list_path = [os.path.join(example_path,"cloth",garm) for garm in garm_list]
 
-client = Client("life4cut/ff-v1")
-result = client.predict(
-		dict={"background":handle_file('https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png'),"layers":[],"composite":None},
-		garm_img=handle_file('https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png'),
-		garment_des="Hello!!",
-		is_checked=True,
-		is_checked_crop=False,
-		denoise_steps=30,
-		seed=42,
-		api_name="/tryon"
-)
-print(result)
+image_blocks = gr.Blocks().queue()
+with image_blocks as demo:
+    gr.Markdown("## fashion filter")
+    with gr.Row():
+      with gr.Column():
+          #imgs = gr.ImageEditor(sources='upload', type="pil", label='Human. Mask with pen or use auto-masking', interactive=True)
+          imgs = gr.Image(sources='upload', type="filepath", label='Human. Mask with pen or use auto-masking')
+          with gr.Row():
+              is_checked = gr.Checkbox(label="Yes", info="Use auto-generated mask (Takes 5 seconds)",value=True)
+          with gr.Row():
+              is_checked_crop = gr.Checkbox(label="Yes", info="Use auto-crop & resizing",value=False)
+      with gr.Column():
+          garm_img = gr.Image(label="Garment", sources='upload', type="filepath")
+      with gr.Column():
+          # image_out = gr.Image(label="Output", elem_id="output-img", height=400)
+          masked_img = gr.Image(label="Masked image output", elem_id="masked-img",show_share_button=False)
+      with gr.Column():
+          # image_out = gr.Image(label="Output", elem_id="output-img", height=400)
+          image_out = gr.Image(label="Output", elem_id="output-img",show_share_button=False) 
+    with gr.Column():
+        try_button = gr.Button(value="predict")
+    try_button.click(fn=predict, inputs=[imgs, garm_img], outputs=[image_out,masked_img])        
+image_blocks.launch()