update app

app.py

@@ -7,37 +7,32 @@ import numpy as np
 # --- Documentation Strings ---
 
 USAGE_GUIDELINES = """
-## 1. Quick Start Guide: HemaScan Pro
+## 1. Quick Start Guide: HemaScan Pro (Binary Mask)
 
-HemaScan Pro uses an advanced semantic segmentation AI model to detect structural regions in microscopic blood smear images.
+HemaScan Pro generates a high-contrast black & white segmentation mask.
 
-1. **Upload Image** – Select a JPG or PNG blood smear image.
-2. **Try Samples** – Click on example images for quick testing.
-3. **Run Analysis** – Press the "Run Segmentation" button.
-4. **View Results** – A high-visibility color segmentation mask will appear.
+1. Upload a blood smear image (JPG/PNG).
+2. Click "Run Segmentation".
+3. View the generated binary mask.
 """
 
 INPUT_EXPLANATION = """
 ## 2. Expected Inputs
 
-| Input Field | Description | Format |
-|-------------|------------|--------|
-| Upload Image | Microscopic blood smear image | JPG / PNG |
+| Field | Requirement |
+|-------|------------|
+| Upload Image | JPG / PNG blood smear image |
 
-✔ Image is automatically resized to 512×512 for processing.
+✔ Automatically resized to 512×512.
 """
 
 OUTPUT_EXPLANATION = """
-## 3. Expected Outputs (Color Segmentation Mask)
+## 3. Output Description (Black & White Mask)
 
-The output is a **color-coded segmentation mask**:
-
-• Each detected object category is assigned a distinct color.  
-• Enhances boundary clarity between cells and background.  
-• Mask is enlarged by **400% (4×)** for improved visibility.  
-
-### Example Testing
-Click any example image below to automatically run segmentation.
+• Background = White  
+• Detected Regions = Black  
+• Enlarged by 400% (4×) for clarity  
+• Clean binary medical-style visualization
 """
 
 # --------------------
@@ -49,15 +44,6 @@ model = SegformerForSemanticSegmentation.from_pretrained(
 )
 model.eval()
 
-# Create vibrant color palette
-def create_color_palette(num_classes=150):
-    np.random.seed(42)
-    palette = np.random.randint(0, 255, size=(num_classes, 3))
-    palette[0] = [0, 0, 0]
-    return palette
-
-palette = create_color_palette()
-
 def segment_image(input_image):
     if input_image is None:
         gr.Warning("Please upload an image.")
@@ -71,11 +57,10 @@ def segment_image(input_image):
     logits = outputs.logits
     pred_mask = torch.argmax(logits, dim=1)[0].cpu().numpy()
 
-    # Apply color palette
-    colored_mask = palette[pred_mask]
-    colored_mask = colored_mask.astype(np.uint8)
+    # Convert to binary mask (object vs background)
+    binary_mask = np.where(pred_mask == 0, 255, 0).astype(np.uint8)
 
-    output_image = Image.fromarray(colored_mask)
+    output_image = Image.fromarray(binary_mask)
 
     # Scale 4x
     scale_factor = 4
@@ -86,21 +71,31 @@ def segment_image(input_image):
 # UI
 # --------------------
 with gr.Blocks(title="HemaScan Pro") as demo:
-    gr.Markdown("<h1 style='text-align:center; background:linear-gradient(90deg,#4facfe,#00f2fe); color:white; padding:10px;'>HemaScan Pro - Blood Smear Segmentation</h1>")
+    gr.Markdown("<h1 style='text-align:center; background:linear-gradient(90deg,#4facfe,#00f2fe); color:white; padding:10px;'>HemaScan Pro - Binary Segmentation</h1>")
 
-    with gr.Accordion("📘 Documentation & Usage", open=False):
+    with gr.Accordion(" Documentation", open=False):
         gr.Markdown(USAGE_GUIDELINES)
         gr.Markdown("---")
         gr.Markdown(INPUT_EXPLANATION)
         gr.Markdown("---")
         gr.Markdown(OUTPUT_EXPLANATION)
 
-    input_image = gr.Image(type="pil", label="Upload Blood Smear Image", width=512, height=512)
-    submit_button = gr.Button("Run Segmentation", variant="primary")
-    output_image = gr.Image(type="pil", label="Color Segmentation Mask (4x)", width=512, height=512)
+    gr.Markdown("## Step 1: Upload Blood Smear Image")
+    with gr.Row():
+        with gr.Column(scale=1):
+            # Define Input component directly inside the column (No .render() needed)
+            input_image = gr.Image(type="pil", label="Step 1: Upload Blood Smear Image", width=600, height=600) 
+    
+    gr.Markdown("## Step 2: Click Submit for Segmentation")
+    with gr.Row():
+        submit_button = gr.Button("Submit for Segmentation", variant="primary")
+    gr.Markdown("## Output")
+    with gr.Row(scale=2):
+         # Define Output component directly inside the column (No .render() needed)
+        output_image = gr.Image(type="pil", label="Step 3: Predicted Masks", width=600, height=600)
 
     gr.Examples(
-        examples=["data/1.png", "data/2.png", "data/3.png", "data/211.png"],
+        examples=["data/1.jpg", "data/2.jpg", "data/3.jpg"],
         inputs=input_image,
         outputs=output_image,
         fn=segment_image,