adapt code

app.py

@@ -11,28 +11,18 @@ import base64
 hf_token = os.environ.get("HF_TOKEN_API_DEMO") # we get it from a secret env variable, such that it's private
 auth_headers = {"api_token": hf_token}
 
-def convert_mask_image_to_base64_string(mask_image):
-    buffer = io.BytesIO()
-    mask_image.save(buffer, format="PNG")  # You can choose the format (e.g., "JPEG", "PNG")
-    # Encode the buffer in base64
-    image_base64_string = base64.b64encode(buffer.getvalue()).decode('utf-8')
-    return f",{image_base64_string}" # for some reason the funciton which downloads image from base64 expects prefix of "," which is redundant in the url
 
 def download_image(url):
     response = requests.get(url)
     img_bytes = BytesIO(response.content)
     return Image.open(img_bytes).convert("RGB")
 
-def gen_fill_api_call(image_base64_file, mask_base64_file, prompt):
-
-    url = "http://engine.prod.bria-api.com/v1/gen_fill"
+def api_call(image_base64_file):
+    url = "https://engine.prod.bria-api.com/v1/tailored-gen/restyle_portrait"
     
     payload = {
-    "file": image_base64_file,
-    "mask_file": mask_base64_file,
-    "prompt": prompt,
-    "steps_num": 12,
-    "sync": True
+    "id_image_file": image_base64_file,
+    "tailored_model_id": 598,
     }
     response = requests.post(url, json=payload, headers=auth_headers)
     response = response.json()
@@ -44,12 +34,10 @@ def gen_fill_api_call(image_base64_file, mask_base64_file, prompt):
 def predict(dict, prompt):
 
     init_image = Image.fromarray(dict['background'][:, :, :3], 'RGB') #dict['background'].convert("RGB")#.resize((1024, 1024))
-    mask = Image.fromarray(dict['layers'][0][:,:,3], 'L') #dict['layers'].convert("RGB")#.resize((1024, 1024))
     
     image_base64_file = convert_mask_image_to_base64_string(init_image)
-    mask_base64_file = convert_mask_image_to_base64_string(mask)
     
-    gen_img = gen_fill_api_call(image_base64_file, mask_base64_file, prompt)
+    gen_img = api_call(image_base64_file, mask_base64_file, prompt)
     
     return gen_img
 
@@ -107,37 +95,24 @@ with image_blocks as demo:
         gr.Markdown("## BRIA Generative Fill API")
         gr.HTML('''
           <p style="margin-bottom: 10px; font-size: 94%">
-            This demo showcases the BRIA Generative Fill capability, which allows users to add and modify elements or objects from images, guided by a mask and a prompt.<br>
-            The pipeline comprises multiple components, including <a href="https://huggingface.co/briaai/BRIA-2.3" target="_blank">briaai/BRIA-2.3</a>, 
-            <a href="https://huggingface.co/briaai/BRIA-2.3-ControlNet-Generative-Fill" target="_blank">briaai/BRIA-2.3-ControlNet-Generative-Fill</a>, 
-            and <a href="https://huggingface.co/briaai/BRIA-2.3-FAST-LORA" target="_blank">briaai/BRIA-2.3-FAST-LORA</a>, all trained on licensed data.<br>
-            This ensures full legal liability coverage for copyright and privacy infringement.<br>
-            Notes:<br>
-            - High-resolution images may take longer to process.<br>
-            - For best results use blobby masks.<br>
-            - The Generative Fill ControlNet's weights are publicily available.<br><br>
+            This demo showcases the BRIA Tailored Portrait capability
           </p>
-          <p style="margin-bottom: 10px; font-size: 94%">
-          API Endpoint available on: <a href="https://fal.ai/models/fal-ai/bria/genfill" target="_blank">fal.ai</a><br>
-          ComfyUI node is available here: <a href="https://github.com/Bria-AI/ComfyUI-BRIA-API" target="_blank">ComfyUI Node</a>
-          </p> 
         ''')
 
     with gr.Row():
         with gr.Column():
-            image = gr.ImageEditor(sources=["upload"], layers=False, transforms=[], 
-                                   brush=gr.Brush(colors=["#000000"], color_mode="fixed"),
+            image = gr.ImageEditor(sources=["upload"], layers=False, transforms=[], color_mode="fixed"),
                                    )
-            prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here...")
+            # prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here...")
             with gr.Row(elem_id="prompt-container", equal_height=True):
                 with gr.Column():
-                    btn = gr.Button("Fill!", elem_id="run_button")
+                    btn = gr.Button("Restyle!", elem_id="run_button")
         
         with gr.Column():
             image_out = gr.Image(label="Output", elem_id="output-img")
 
     # Button click will trigger the inpainting function (now with prompt included)
-    btn.click(fn=predict, inputs=[image, prompt], outputs=[image_out], api_name='run')
+    btn.click(fn=predict, inputs=[image], outputs=[image_out], api_name='run')
 
     gr.HTML(
         """