initial template of tabs with api call to int

app.py

@@ -23,33 +23,64 @@ def download_image(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):
+def lifestyle_shot_by_text_api_call(image_base64_file, prompt):
 
-    url = "http://engine.prod.bria-api.com/v1/gen_fill"
+    # url = "http://engine.prod.bria-api.com/v1/product/lifestyle_shot_by_text" # TODO: use this line!
+    url = "http://engine.int.bria-api.com/v1/product/lifestyle_shot_by_text" # TODO: use line above!
     
     payload = {
     "file": image_base64_file,
-    "mask_file": mask_base64_file,
-    "prompt": prompt,
-    "steps_num": 12,
-    "sync": True
+    "scene_description": prompt,
+    "num_results": 1,
+    "sync": True,
+    "original_quality": True,
+    "optimize_description": True,
     }
     response = requests.post(url, json=payload, headers=auth_headers)
     response = response.json()
-    res_image = download_image(response["urls"][0])
+    res_image = download_image(response['result'][0][0])
     
     return res_image
 
 
-def predict(dict, prompt):
+def predict_ref_by_text(input_image, 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))
+    # 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(input_image)
+    
+    gen_img = lifestyle_shot_by_text_api_call(image_base64_file, prompt)
+    
+    return gen_img
+
+
+def lifestyle_shot_by_image_api_call(image_base64_file, ref_image_base64_file):
+
+    # url = "http://engine.prod.bria-api.com/v1/product/lifestyle_shot_by_image" # TODO: use this line!
+    url = "http://engine.int.bria-api.com/v1/product/lifestyle_shot_by_image" # TODO: use line above!
+    
+    payload = {
+    "file": image_base64_file,
+    "ref_image_file": ref_image_base64_file,
+    "num_results": 1,
+    "sync": True,
+    "original_quality": True,
+    "optimize_description": True,
+    }
+    response = requests.post(url, json=payload, headers=auth_headers)
+    response = response.json()
+    res_image = download_image(response['result'][0][0])
+    
+    return res_image
+
+
+def predict_ref_by_image(init_image, ref_image):
     
     image_base64_file = convert_mask_image_to_base64_string(init_image)
-    mask_base64_file = convert_mask_image_to_base64_string(mask)
+    ref_base64_file = convert_mask_image_to_base64_string(ref_image)
     
-    gen_img = gen_fill_api_call(image_base64_file, mask_base64_file, prompt)
+    gen_img = lifestyle_shot_by_image_api_call(image_base64_file, ref_base64_file)
     
     return gen_img
 
@@ -103,37 +134,56 @@ div#share-btn-container > div {flex-direction: row;background: black;align-items
 
 image_blocks = gr.Blocks(css=css, elem_id="total-container")
 with image_blocks as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown("## BRIA Generative Fill API")
-        gr.HTML('''
+    # with gr.Column(elem_id="col-container"):
+    gr.Markdown("## BRIA Replace Background By Reference 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>
+            This demo showcases the BRIA Replace Background By Reference capability, which allows users to replace background for an image, guided by either a textual prompt or a reference image which is used for inpiring the generated background.<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>
+            <a href="https://huggingface.co/briaai/RMBG-2.0" target="_blank">briaai/RMBG-2.0</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>
+            - For best results in reference by image: make sure the foreground in the image is already located in the wanted position and scale, relative to the elements in the reference image.<br>
           </p>
         ''')
-
-    with gr.Row():
-        with gr.Column():
-            image = gr.ImageEditor(sources=["upload"], layers=False, transforms=[], 
-                                   brush=gr.Brush(colors=["#000000"], color_mode="fixed"),
-                                   )
-            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")
-        
-        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')
+    with gr.Tab(elem_id="By prompt"):
+
+        with gr.Row():
+            with gr.Column():
+                image = gr.ImageEditor(sources=["upload"], layers=False, transforms=[], 
+                                    brush=gr.Brush(colors=["#000000"], color_mode="fixed"),
+                                    )
+                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("Replace Background!", 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_ref_by_text, inputs=[image, prompt], outputs=[image_out], api_name='run')
+    
+    with gr.Tab(elem_id="By image reference"):
+
+        with gr.Row():
+            with gr.Column():
+                image = gr.ImageEditor(sources=["upload"], layers=False, transforms=[], 
+                                    brush=gr.Brush(colors=["#000000"], color_mode="fixed"),
+                                    )
+                ref_image = gr.ImageEditor(sources=["upload"], layers=False, transforms=[], 
+                                    brush=gr.Brush(colors=["#000000"], color_mode="fixed"),
+                                    )
+                with gr.Row(elem_id="prompt-container", equal_height=True):
+                    with gr.Column():
+                        btn = gr.Button("Replace Background!", 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_ref_by_image, inputs=[image, ref_image], outputs=[image_out], api_name='run')
 
     gr.HTML(
         """