Delete app.py

app.py

@@ -1,48 +0,0 @@
-import gradio as gr
-import requests 
-from io import BytesIO
-from PIL import Image
-import base64
-
-canvas_html = "<face-canvas id='canvas-root' style='display:flex;max-width: 500px;margin: 0 auto;'></face-canvas>"
-load_js = """
-async () => {
-  const url = "https://huggingface.co/datasets/radames/gradio-components/raw/main/face-canvas.js"
-  fetch(url)
-    .then(res => res.text())
-    .then(text => {
-      const script = document.createElement('script');
-      script.type = "module"
-      script.src = URL.createObjectURL(new Blob([text], { type: 'application/javascript' }));
-      document.head.appendChild(script);
-    });
-}
-"""
-get_js_image = """
-async (canvasData) => {
-  const canvasEl = document.getElementById("canvas-root");
-  const data = canvasEl? canvasEl._data : null;
-  return data
-}
-"""
-
-def predict(canvas_data):
-  base64_img = canvas_data['image']
-  image_data = base64.b64decode(base64_img.split(',')[1])
-  image = Image.open(BytesIO(image_data))
-  return image
-
-blocks = gr.Blocks()
-with blocks:
-  canvas_data = gr.JSON(value={}, visible=False)
-  with gr.Row():
-    with gr.Column(visible=True) as box_el:
-        canvas = gr.HTML(canvas_html,elem_id="canvas_html")
-    with gr.Column(visible=True) as box_el:
-        image_out = gr.Image()
-
-  btn = gr.Button("Run")
-  btn.click(fn=predict, inputs=[canvas_data], outputs=[image_out], _js=get_js_image)
-  blocks.load(None, None, None, _js=load_js)
-
-blocks.launch(debug=True, inline=True)