initial commit

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,114 @@
+import gradio as gr
+from transformers import SegformerForSemanticSegmentation, SegformerImageProcessor
+from PIL import Image
+import torch
+import numpy as np
+
+# --- Documentation Strings ---
+
+USAGE_GUIDELINES = """
+## 1. Quick Start Guide: CellVision AI (Grayscale Mask)
+
+CellVision AI generates a grayscale segmentation mask for microscopy images.
+
+Steps:
+1. Upload your image.
+2. Click "Analyze Image".
+3. Review the gray-white mask result.
+"""
+
+INPUT_EXPLANATION = """
+## 2. Input Requirements
+
+| Field | Format |
+|-------|--------|
+| Image Upload | JPG / PNG |
+
+Image is resized to 512×512 before inference.
+"""
+
+OUTPUT_EXPLANATION = """
+## 3. Output Description (Gray & White Mask)
+
+• Background = White  
+• Segmented Objects = Gray  
+• Enlarged by 300% (3×)  
+• Subtle grayscale research-style output
+"""
+
+# --------------------
+# Model
+# --------------------
+processor = SegformerImageProcessor(do_reduce_labels=False)
+model = SegformerForSemanticSegmentation.from_pretrained(
+    "nvidia/segformer-b0-finetuned-ade-512-512"
+)
+model.eval()
+
+def segment_image(input_image):
+    if input_image is None:
+        gr.Warning("Upload an image first.")
+        return None
+
+    inputs = processor(images=input_image, return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    logits = outputs.logits
+    pred_mask = torch.argmax(logits, dim=1)[0].cpu().numpy()
+
+    # Gray & White mask
+    gray_mask = np.where(pred_mask == 0, 255, 128).astype(np.uint8)
+
+    output_image = Image.fromarray(gray_mask)
+
+    # Scale 3x
+    scale_factor = 3
+    new_size = (output_image.width * scale_factor, output_image.height * scale_factor)
+    return output_image.resize(new_size, resample=Image.NEAREST)
+
+# --------------------
+# UI
+# --------------------
+with gr.Blocks(title="CellVision AI - Segment the Malaria Cells from Blood smeers", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("<h1 style='text-align:center; background-color:#1a1a1a; color:#00ffcc; padding:12px;'>CellVision AI - Grayscale Segmentation</h1>")
+
+    with gr.Accordion(" Documentation", open=False):
+        gr.Markdown(USAGE_GUIDELINES)
+        gr.Markdown("---")
+        gr.Markdown(INPUT_EXPLANATION)
+        gr.Markdown("---")
+        gr.Markdown(OUTPUT_EXPLANATION)
+
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("## Step 1: Upload Blood Smear Image")
+            # Define Input component directly inside the column (No .render() needed)
+            input_image = gr.Image(type="pil", label="Upload Microscopy Image", width=600, height=600) 
+    
+            gr.Markdown("## Step 2: Click Submit for Segmentation")
+            with gr.Row():
+                submit_button = gr.Button("Analyze Image", variant="Secondary")
+        with gr.Column(scale=1):
+            gr.Markdown("## Output")
+             # Define Output component directly inside the column (No .render() needed)
+            output_image = gr.Image(type="pil", label="Gray & White Mask (3x)", width=600, height=600)
+
+
+    gr.Markdown("---")
+    gr.Markdown("## Example Images")
+    gr.Examples(
+        examples=["data/1.png", "data/2.png", "data/3.png"],
+        inputs=input_image,
+        outputs=output_image,
+        fn=segment_image,
+        cache_examples=False,
+    )
+
+
+    submit_button.click(segment_image, input_image, output_image)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,8 @@
+torch
+torchvision
+transformers
+gradio
+Pillow
+numpy<2
+# gradio==3.50.2 
+# gradio-client==0.6.1