Add Mask generation Model

app.py

@@ -24,7 +24,7 @@ models = {
 
 def load_mask_model():
     if models["mask"] is None:
-        print("🔄 Loading Mask Model...")
+        print("Loading Mask Model...")
         model = DiffusionModelUNet(
             spatial_dims=2,
             in_channels=4,
@@ -97,7 +97,7 @@ def synthesize_image(mask_input, source_type, model_choice):
     # A. Handle Input Source
     if source_type == "Upload Mask":
         if mask_input is None:
-            return None, "⚠️ Please upload a mask first."
+            return None, "Please upload a mask first."
         # Expecting RGB upload, need to convert to integer map?
         # Or if your conditional models take RGB, pass raw.
         # For safety, let's assume we convert upload to numpy.
@@ -111,7 +111,7 @@ def synthesize_image(mask_input, source_type, model_choice):
     else:
         # Input comes from the "Generate Mask" step (State variable)
         if mask_input is None:
-             return None, "⚠️ Please generate a mask first."
+             return None, "Please generate a mask first."
         mask_idx = mask_input
 
     # B. Run Conditional Inference
@@ -147,7 +147,7 @@ with gr.Blocks(title="Cardiac MRI Synthesis") as demo:
             
             # Tab 1: Generate
             with gr.Group(visible=True) as group_gen:
-                btn_gen_mask = gr.Button("🎲 Generate Random Mask", variant="primary")
+                btn_gen_mask = gr.Button("Generate Random Mask", variant="primary")
                 out_gen_mask = gr.Image(label="Generated Mask", type="numpy", interactive=False)
                 state_mask = gr.State() # Stores the raw integer mask (0-3) hidden from view
 

models/mask_diffusion.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a0482056875d7646c42da7219300c8e9bd57ad5f8051b3426c0caf1adf9b85e7
+size 252760728

requirements.txt

@@ -8,4 +8,5 @@ pillow
 tqdm
 gradio
 scipy
-safetensors
+safetensors
+huggingface_hub