more_pompt

app.py

@@ -495,31 +495,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
         gr.Markdown(f"# {MODEL_TITLE}\n{MODEL_DESCRIPTION}")
 
     with gr.Tabs():
-        # --- TAB 1: Mask-to-Image ---
-        with gr.Tab("Mask-to-Image", id="mask2img"):
-            gr.Markdown("""
-            ### Instructions
-            1.  Upload a single-channel segmentation mask (`.tif` file), or select one from the examples gallery below.
-            2.  Enter the corresponding 'Cell Type' (e.g., 'CoNSS', 'HeLa') to create the prompt.
-            3.  Select how many sample images you want to generate.
-            4.  Adjust 'Inference Steps' and 'Seed' as needed.
-            5.  Click 'Generate Training Samples' to start the process.
-            6.  The 'Generated Samples' will appear in the main gallery, with the 'Input Mask' shown below for reference.
-            """) # Content hidden for brevity
-            with gr.Row(variant="panel"):
-                with gr.Column(scale=1, min_width=350):
-                    m2i_input_file = gr.File(label="Upload Segmentation Mask (.tif)", file_types=['.tif', '.tiff'])
-                    m2i_cell_type_input = gr.Textbox(label="Cell Type (for prompt)", placeholder="e.g., CoNSS, HeLa, MCF-7")
-                    m2i_num_images_slider = gr.Slider(minimum=1, maximum=10, step=1, value=5, label="Number of Images to Generate")
-                    m2i_steps_slider = gr.Slider(minimum=5, maximum=50, step=1, value=10, label="Inference Steps")
-                    m2i_seed_input = gr.Number(label="Seed", value=42)
-                    m2i_generate_button = gr.Button("Generate Training Samples", variant="primary")
-                with gr.Column(scale=2):
-                    m2i_output_gallery = gr.Gallery(label="Generated Samples", columns=5, object_fit="contain", height="auto")
-                    m2i_input_display = gr.Image(label="Input Mask", type="pil", interactive=False)
-            m2i_gallery = gr.Gallery(value=m2i_gallery_examples, label="Input Examples (Click an image to use it as input)", columns=6, object_fit="contain", height="auto")
-
-        # --- TAB 2: Text-to-Image ---
+        # --- TAB 1: Text-to-Image ---
         with gr.Tab("Text-to-Image Generation", id="txt2img"):
             gr.Markdown("""
             ### Instructions
@@ -539,7 +515,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
                     t2i_generated_output = gr.Image(label="Generated Image", type="pil", interactive=False)
             t2i_gallery = gr.Gallery(value=t2i_gallery_examples, label="Examples (Click an image to use its prompt)", columns=6, object_fit="contain", height="auto")
 
-        # --- TAB 3: Super-Resolution ---
+        # --- TAB 2: Super-Resolution ---
         with gr.Tab("Super-Resolution", id="super_res"):
             gr.Markdown("""
             ### Instructions
@@ -565,7 +541,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
                         sr_output_image = gr.Image(label="Super-Resolved Image", type="pil", interactive=False)
             sr_gallery = gr.Gallery(value=sr_gallery_examples, label="Input Examples (Click an image to use it as input)", columns=6, object_fit="contain", height="auto")
 
-        # --- TAB 4: Denoising ---
+        # --- TAB 3: Denoising ---
         with gr.Tab("Denoising", id="denoising"):
             gr.Markdown("""
             ### Instructions
@@ -589,6 +565,30 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
                         dn_output_image = gr.Image(label="Denoised Image", type="pil", interactive=False)
             dn_gallery = gr.Gallery(value=dn_gallery_examples, label="Input Examples (Click an image to use it as input)", columns=6, object_fit="contain", height="auto")
 
+        # --- TAB 4: Mask-to-Image ---
+        with gr.Tab("Mask-to-Image", id="mask2img"):
+            gr.Markdown("""
+            ### Instructions
+            1.  Upload a single-channel segmentation mask (`.tif` file), or select one from the examples gallery below.
+            2.  Enter the corresponding 'Cell Type' (e.g., 'CoNSS', 'HeLa') to create the prompt.
+            3.  Select how many sample images you want to generate.
+            4.  Adjust 'Inference Steps' and 'Seed' as needed.
+            5.  Click 'Generate Training Samples' to start the process.
+            6.  The 'Generated Samples' will appear in the main gallery, with the 'Input Mask' shown below for reference.
+            """) # Content hidden for brevity
+            with gr.Row(variant="panel"):
+                with gr.Column(scale=1, min_width=350):
+                    m2i_input_file = gr.File(label="Upload Segmentation Mask (.tif)", file_types=['.tif', '.tiff'])
+                    m2i_cell_type_input = gr.Textbox(label="Cell Type (for prompt)", placeholder="e.g., CoNSS, HeLa, MCF-7")
+                    m2i_num_images_slider = gr.Slider(minimum=1, maximum=10, step=1, value=5, label="Number of Images to Generate")
+                    m2i_steps_slider = gr.Slider(minimum=5, maximum=50, step=1, value=10, label="Inference Steps")
+                    m2i_seed_input = gr.Number(label="Seed", value=42)
+                    m2i_generate_button = gr.Button("Generate Training Samples", variant="primary")
+                with gr.Column(scale=2):
+                    m2i_output_gallery = gr.Gallery(label="Generated Samples", columns=5, object_fit="contain", height="auto")
+                    m2i_input_display = gr.Image(label="Input Mask", type="pil", interactive=False)
+            m2i_gallery = gr.Gallery(value=m2i_gallery_examples, label="Input Examples (Click an image to use it as input)", columns=6, object_fit="contain", height="auto")
+
         # --- TAB 5: Cell Segmentation ---
         with gr.Tab("Cell Segmentation", id="segmentation"):
             gr.Markdown("""