header update

HEADER.md

@@ -0,0 +1,27 @@
+# MIPHEI-ViT Demo: 16-channel mIF Prediction
+
+<p align="center">
+  <a href="https://huggingface.co/Estabousi/MIPHEI-vit" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+    <img src="https://img.shields.io/badge/🤗 Model-MIPHEI--ViT-lightgrey?logo=huggingface" height="25">
+  </a>
+  <a href="https://github.com/Sanofi-Public/MIPHEI-ViT" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+    <img src="https://img.shields.io/badge/GitHub-Repository-black?logo=github" height="25">
+  </a>
+</p>
+
+**MIPHEI-ViT** predicts **multiplex immunofluorescence (mIF)** from standard **H&E-stained** histology tiles.
+
+Upload a colorectal H&E tile (256×256 px at 0.5 µm/px recommended). Images will be resized if needed — this may affect resolution and output quality.
+
+The model returns **16 grayscale images**, each representing a predicted mIF marker. Inference is slow because the Space is using CPU.
+
+---
+
+Try it with low-zoom screenshots from public datasets:
+
+**ORION (in-domain test set):**
+- [CRC2](https://labsyspharm.github.io/orion-crc/minerva/P37_S30-CRC02/index.html#s=0&w=0&g=5&m=-1&a=-100_-100&v=1.0673_0.6057_0.5&o=-100_-100_1_1&p=Q)
+- [CRC11](https://labsyspharm.github.io/orion-crc/minerva/P37_S43-CRC11/index.html#s=0&w=0&g=5&m=-1&a=-100_-100&v=0.4823_0.6723_0.5097&o=-100_-100_1_1&p=Q)
+
+**TCGA (external):**
+- [GDC Slide Viewer](https://portal.gdc.cancer.gov/image-viewer/MultipleImageViewerPage?isCohortCentric=true&additionalFilters=%7B%22mode%22%3A%22and%22%2C%22root%22%3A%7B%22files.data_type%22%3A%7B%22operator%22%3A%22includes%22%2C%22field%22%3A%22files.data_type%22%2C%22operands%22%3A%5B%22Slide%20Image%22%5D%7D%2C%22cases.samples.preservation_method%22%3A%7B%22operator%22%3A%22includes%22%2C%22field%22%3A%22cases.samples.preservation_method%22%2C%22operands%22%3A%5B%22ffpe%22%5D%7D%2C%22files.access%22%3A%7B%22operator%22%3A%22includes%22%2C%22field%22%3A%22files.access%22%2C%22operands%22%3A%5B%22open%22%5D%7D%7D%7D) (Filter Cases: TCGA*)

app.py

@@ -38,22 +38,23 @@ def predict(image):
         pil_ch = Image.fromarray(ch_img, mode='L')
         channel_imgs.append(pil_ch)
 
-    # Return list: input + 16 channels
+    # Return predicted 16 channels
     return channel_imgs
 
-# Prepare Gradio UI
-demo = gr.Interface(
-    fn=predict,
-    inputs=gr.Image(type="pil", label="Input H&E"),
-    outputs=[gr.Image(type="pil", label=f"mIF Channel {channel_names[i]}") for i in range(16)],
-    title="MIPHEI-ViT: Full mIF Prediction",
-    description=(
-        "Upload an H&E image tile (colorectal 256×256 pxs at 0.5 µm/px recommended). "
-        "The image will be resized to (256×256) if needed.\n"
-        "The model predicts 16-channel multiplex immunofluorescence, "
-        "with each marker shown as a grayscale image."
-)
-)
+# Markdown header
+with open("HEADER.md", "r", encoding="utf-8") as f:
+    HEADER_MD = f.read()
+
+# Build interface using Blocks
+with gr.Blocks() as demo:
+    gr.Markdown(HEADER_MD)
+    gr.Interface(
+        fn=predict,
+        inputs=gr.Image(type="pil", label="Input H&E"),
+        outputs=[gr.Image(type="pil", label=f"mIF Channel {channel_names[i]}") for i in range(16)],
+        title=None,
+        description=None
+    )
 
 if __name__ == "__main__":
     demo.launch()