Update app.py

app.py

@@ -3,8 +3,32 @@ import torch
 
 from diffusers import AutoPipelineForInpainting, UNet2DConditionModel
 import diffusers
-from share_btn import community_icon_html, loading_icon_html, share_js
 
+import os
+from io import BytesIO
+import base64
+import re
+
+SECRET_TOKEN = os.getenv('SECRET_TOKEN', 'default_secret')
+
+# Regex pattern to match data URI scheme
+data_uri_pattern = re.compile(r'data:image/(png|jpeg|jpg|webp);base64,')
+
+def readb64(b64):
+    # Remove any data URI scheme prefix with regex
+    b64 = data_uri_pattern.sub("", b64)
+    # Decode and open the image with PIL
+    img = Image.open(BytesIO(base64.b64decode(b64)))
+    return img
+    
+# convert from PIL to base64
+def writeb64(image):
+    buffered = BytesIO()
+    image.save(buffered, format="PNG")
+    b64image = base64.b64encode(buffered.getvalue())
+    b64image_str = b64image.decode("utf-8")
+    return b64image_str
+    
 device = "cuda" if torch.cuda.is_available() else "cpu"
 pipe = AutoPipelineForInpainting.from_pretrained("diffusers/stable-diffusion-xl-1.0-inpainting-0.1", torch_dtype=torch.float16, variant="fp16").to(device)
 
@@ -16,7 +40,11 @@ def read_content(file_path: str) -> str:
 
     return content
 
-def predict(dict, prompt="", negative_prompt="", guidance_scale=7.5, steps=20, strength=1.0, scheduler="EulerDiscreteScheduler"):
+def predict(secret_token, input_image_b64, input_mask_b64, prompt="", negative_prompt="", guidance_scale=7.5, steps=20, strength=1.0, scheduler="EulerDiscreteScheduler"):
+    if secret_token != SECRET_TOKEN:
+        raise gr.Error(
+            f'Invalid secret token. Please fork the original space if you want to use it for yourself.')
+
     if negative_prompt == "":
         negative_prompt = None
     scheduler_class_name = scheduler.split("-")[0]
@@ -30,108 +58,37 @@ def predict(dict, prompt="", negative_prompt="", guidance_scale=7.5, steps=20, s
     scheduler = getattr(diffusers, scheduler_class_name)
     pipe.scheduler = scheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler", **add_kwargs)
     
-    init_image = dict["image"].convert("RGB").resize((1024, 1024))
-    mask = dict["mask"].convert("RGB").resize((1024, 1024))
+    init_image = readb64(input_image_b64).convert("RGB").resize((1024, 1024))
+    mask = readb64(input_mask_b64).convert("RGB").resize((1024, 1024))
     
     output = pipe(prompt = prompt, negative_prompt=negative_prompt, image=init_image, mask_image=mask, guidance_scale=guidance_scale, num_inference_steps=int(steps), strength=strength)
+
+    
+    return writeb64(output.images[0])
+
+
+
+inpainter = gr.Blocks()
+with inpainter as demo:
+    gr.HTML("""
+        <div style="z-index: 100; position: fixed; top: 0px; right: 0px; left: 0px; bottom: 0px; width: 100%; height: 100%; background: white; display: flex; align-items: center; justify-content: center; color: black;">
+        <div style="text-align: center; color: black;">
+        <p style="color: black;">This space is a REST API to programmatically inpaint an image.</p>
+        <p style="color: black;">Interested in using it? Please use the <a href="https://huggingface.co/spaces/diffusers/stable-diffusion-xl-inpainting" target="_blank">original space</a>, thank you!</p>
+        </div>
+        </div>""")
     
-    return output.images[0], gr.update(visible=True)
-
-
-css = '''
-.gradio-container{max-width: 1100px !important}
-#image_upload{min-height:400px}
-#image_upload [data-testid="image"], #image_upload [data-testid="image"] > div{min-height: 400px}
-#mask_radio .gr-form{background:transparent; border: none}
-#word_mask{margin-top: .75em !important}
-#word_mask textarea:disabled{opacity: 0.3}
-.footer {margin-bottom: 45px;margin-top: 35px;text-align: center;border-bottom: 1px solid #e5e5e5}
-.footer>p {font-size: .8rem; display: inline-block; padding: 0 10px;transform: translateY(10px);background: white}
-.dark .footer {border-color: #303030}
-.dark .footer>p {background: #0b0f19}
-.acknowledgments h4{margin: 1.25em 0 .25em 0;font-weight: bold;font-size: 115%}
-#image_upload .touch-none{display: flex}
-@keyframes spin {
-    from {
-        transform: rotate(0deg);
-    }
-    to {
-        transform: rotate(360deg);
-    }
-}
-#share-btn-container {padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; max-width: 13rem; margin-left: auto;}
-div#share-btn-container > div {flex-direction: row;background: black;align-items: center}
-#share-btn-container:hover {background-color: #060606}
-#share-btn {all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.5rem !important; padding-bottom: 0.5rem !important;right:0;}
-#share-btn * {all: unset}
-#share-btn-container div:nth-child(-n+2){width: auto !important;min-height: 0px !important;}
-#share-btn-container .wrap {display: none !important}
-#share-btn-container.hidden {display: none!important}
-#prompt input{width: calc(100% - 160px);border-top-right-radius: 0px;border-bottom-right-radius: 0px;}
-#run_button{position:absolute;margin-top: 11px;right: 0;margin-right: 0.8em;border-bottom-left-radius: 0px;
-    border-top-left-radius: 0px;}
-#prompt-container{margin-top:-18px;}
-#prompt-container .form{border-top-left-radius: 0;border-top-right-radius: 0}
-#image_upload{border-bottom-left-radius: 0px;border-bottom-right-radius: 0px}
-'''
-
-image_blocks = gr.Blocks(css=css, elem_id="total-container")
-with image_blocks as demo:
-    gr.HTML(read_content("header.html"))
-    with gr.Row():
-                with gr.Column():
-                    image = gr.Image(source='upload', tool='sketch', elem_id="image_upload", type="pil", label="Upload",height=400)
-                    with gr.Row(elem_id="prompt-container", mobile_collapse=False, equal_height=True):
-                        with gr.Row():
-                            prompt = gr.Textbox(placeholder="Your prompt (what you want in place of what is erased)", show_label=False, elem_id="prompt")
-                            btn = gr.Button("Inpaint!", elem_id="run_button")
-                    
-                    with gr.Accordion(label="Advanced Settings", open=False):
-                        with gr.Row(mobile_collapse=False, equal_height=True):
-                            guidance_scale = gr.Number(value=7.5, minimum=1.0, maximum=20.0, step=0.1, label="guidance_scale")
-                            steps = gr.Number(value=20, minimum=10, maximum=30, step=1, label="steps")
-                            strength = gr.Number(value=0.99, minimum=0.01, maximum=1.0, step=0.01, label="strength")
-                            negative_prompt = gr.Textbox(label="negative_prompt", placeholder="Your negative prompt", info="what you don't want to see in the image")
-                        with gr.Row(mobile_collapse=False, equal_height=True):
-                            schedulers = ["DEISMultistepScheduler", "HeunDiscreteScheduler", "EulerDiscreteScheduler", "DPMSolverMultistepScheduler", "DPMSolverMultistepScheduler-Karras", "DPMSolverMultistepScheduler-Karras-SDE"]
-                            scheduler = gr.Dropdown(label="Schedulers", choices=schedulers, value="EulerDiscreteScheduler")
-                        
-                with gr.Column():
-                    image_out = gr.Image(label="Output", elem_id="output-img", height=400)
-                    with gr.Group(elem_id="share-btn-container", visible=False) as share_btn_container:
-                        community_icon = gr.HTML(community_icon_html)
-                        loading_icon = gr.HTML(loading_icon_html)
-                        share_button = gr.Button("Share to community", elem_id="share-btn",visible=True)
-            
-
-    btn.click(fn=predict, inputs=[image, prompt, negative_prompt, guidance_scale, steps, strength, scheduler], outputs=[image_out, share_btn_container], api_name='run')
-    prompt.submit(fn=predict, inputs=[image, prompt, negative_prompt, guidance_scale, steps, strength, scheduler], outputs=[image_out, share_btn_container])
-    share_button.click(None, [], [], _js=share_js)
-
-    gr.Examples(
-                examples=[
-                    ["./imgs/aaa (8).png"],
-                    ["./imgs/download (1).jpeg"],
-                    ["./imgs/0_oE0mLhfhtS_3Nfm2.png"],
-                    ["./imgs/02_HubertyBlog-1-1024x1024.jpg"],
-                    ["./imgs/jdn_jacques_de_nuce-1024x1024.jpg"],
-                    ["./imgs/c4ca473acde04280d44128ad8ee09e8a.jpg"],
-                    ["./imgs/canam-electric-motorcycles-scaled.jpg"],
-                    ["./imgs/e8717ce80b394d1b9a610d04a1decd3a.jpeg"],
-                    ["./imgs/Nature___Mountains_Big_Mountain_018453_31.jpg"],
-                    ["./imgs/Multible-sharing-room_ccexpress-2-1024x1024.jpeg"],
-                ],
-                fn=predict,
-                inputs=[image],
-                cache_examples=False,
-    )
-    gr.HTML(
-        """
-            <div class="footer">
-                <p>Model by <a href="https://huggingface.co/diffusers" style="text-decoration: underline;" target="_blank">Diffusers</a> - Gradio Demo by 🤗 Hugging Face
-                </p>
-            </div>
-        """
-    )
-
-image_blocks.queue(max_size=25,api_open=False).launch(show_api=False)
+    secret_token = gr.Textbox()
+    input_image_b64 = gr.Textbox()
+    input_mask_b64 = gr.Textbox()
+    prompt = gr.Textbox()
+    guidance_scale = gr.Number(value=7.5, minimum=1.0, maximum=20.0, step=0.1, label="guidance_scale")
+    steps = gr.Number(value=20, minimum=10, maximum=30, step=1, label="steps")
+    strength = gr.Number(value=0.99, minimum=0.01, maximum=1.0, step=0.01, label="strength")
+    negative_prompt = gr.Textbox(label="negative_prompt", placeholder="Your negative prompt", info="what you don't want to see in the image")
+    schedulers = ["DEISMultistepScheduler", "HeunDiscreteScheduler", "EulerDiscreteScheduler", "DPMSolverMultistepScheduler", "DPMSolverMultistepScheduler-Karras", "DPMSolverMultistepScheduler-Karras-SDE"]
+    scheduler = gr.Dropdown(label="Schedulers", choices=schedulers, value="EulerDiscreteScheduler")             
+    output_image_b64 = gr.Textbox()
+    btn.click(fn=predict, inputs=[secret_token, input_image_b64, input_mask_b64, prompt, negative_prompt, guidance_scale, steps, strength, scheduler], outputs=output_image_b64)
+
+inpainter.queue(max_size=25).launch()