new version of app supporting url parameter and example file

README.md

@@ -1,21 +1,33 @@
----
-title: Lungs Segmentation Web App
-emoji: 🖥️
-colorFrom: indigo
-colorTo: green
-sdk: gradio
-sdk_version: 5.23.1
-app_file: app.py
-pinned: false
----
-
 # 🖥️ Lungs segmentation web application
 A web-based application for automated lung segmentation using deep learning, powered by **Gradio** and **PyTorch**. This tool allows users to upload lung images and obtain segmented outputs efficiently.
 
 <p align="center">
-    <img src="https://raw.githubusercontent.com/qchapp/lungs-segmentation-app/refs/heads/master/images/app.png" height="700">
+    <img src="images/app.png" height="700">
 </p>
 
+---
+
+## Try the app
+The application is running on [Hugging Face](https://huggingface.co/), try it using this [link](https://huggingface.co/spaces/qchapp/3d-lungs-segmentation)!
+
+#### Example File
+If you don't have your own `.tif` image, the app includes a built-in example file that can be used directly from the UI by clicking **"Try an example!"**.
+
+#### Load from URL (file_url parameter)
+You can also provide a `.tif` file hosted online using a URL parameter.
+
+To do so, simply append `?file_url=...` to your app's URL.
+
+##### Example (local):
+`http://localhost:7860/?file_url=https://zenodo.org/record/8099852/files/lungs_ct.tif`
+
+##### Example (hosted on Hugging Face):
+`https://huggingface.co/spaces/qchapp/3d-lungs-segmentation/?file_url=https://zenodo.org/record/8099852/files/lungs_ct.tif`
+
+The application will automatically download the file and load it into the viewer.
+
+---
+
 ## Installation
 We recommend performing the installation in a clean Python environment.
 
@@ -26,6 +38,8 @@ After that please run the following command:
 pip install -r requirements.txt
 ```
 
+---
+
 ## Usage
 Run:
 ```sh
@@ -33,5 +47,9 @@ python app.py
 ```
 And go to http://localhost:7860/.
 
+---
+
 ## About Lungs Segmentation
-If you are interesten in the package used for segmentation please check the following [GitHub repository](https://github.com/qchapp/lungs-segmentation)!
+If you are interesten in the package used for segmentation please check the following [GitHub repository](https://github.com/qchapp/lungs-segmentation)!
+
+---

app.py

@@ -1,6 +1,9 @@
 import gradio as gr
 from core.utils import *
 
+import urllib.request
+import tempfile
+
 def get_axis_max(volume, axis):
     """Get the maximum index of each axis."""
     if volume is None:
@@ -31,6 +34,14 @@ with gr.Blocks() as demo:
 
     file_input = gr.File(file_types=[".tif", ".tiff"], label="Upload your 3D TIF or TIFF file")
 
+    # ---- Example loader ----
+    gr.Examples(
+        examples=[[example_file_path]],
+        inputs=[file_input],
+        label="Try an example!",
+        examples_per_page=1
+    )
+
     # ---- RAW SLICES VIEWER ----
     with gr.Group(visible=False) as group_input:
         gr.Markdown("### Raw Volume Slices")
@@ -59,7 +70,7 @@ with gr.Blocks() as demo:
 
     reset_btn = gr.Button("Reset")
 
-    gr.Markdown("#### 📝 This work is based on the Bachelor Thesis of Quentin Chappuis 2024; for more information, consult the [repository](https://github.com/qchapp/lungs-segmentation)!")
+    gr.Markdown("#### 📝 This work is based on the Bachelor Project of Quentin Chappuis 2024; for more information, consult the [repository](https://github.com/qchapp/lungs-segmentation)!")
 
     # ---- CALLBACKS ----
 
@@ -148,5 +159,62 @@ with gr.Blocks() as demo:
         ]
     )
 
+    # ---- HANDLE QUERY PARAMETERS ----
+    @demo.load(
+        outputs=[
+            file_input,
+            volume_state,
+            group_input,
+            segment_btn,
+            z_slider, y_slider, x_slider,
+            z_img, y_img, x_img
+        ]
+    )
+    def load_from_query(request: gr.Request):
+        params = request.query_params
+
+        if "file_url" in params:
+            try:
+                # A) Download the file from the URL to a temporary path
+                url = params["file_url"]
+                tmp_path = tempfile.mktemp(suffix=".tif")
+                urllib.request.urlretrieve(url, tmp_path)
+
+                # B) Open the file as a binary object
+                with open(tmp_path, "rb") as f:
+                    volume = load_volume(f)
+
+                # C) Return values for all components
+                return [
+                    gr.update(value=tmp_path),
+                    volume,
+                    gr.update(visible=True),
+                    gr.update(visible=True),
+                    gr.update(maximum=get_axis_max(volume, "Z")),
+                    gr.update(maximum=get_axis_max(volume, "Y")),
+                    gr.update(maximum=get_axis_max(volume, "X")),
+                    browse_axis("Z", 0, volume),
+                    browse_axis("Y", 0, volume),
+                    browse_axis("X", 0, volume)
+                ]
+
+            except Exception as e:
+                print(f"[Error loading file_url] {e}")
+
+        # Fallback if no file_url or failure
+        return [
+            None,
+            None,
+            gr.update(visible=False),
+            gr.update(visible=False),
+            gr.update(maximum=0),
+            gr.update(maximum=0),
+            gr.update(maximum=0),
+            None,
+            None,
+            None
+        ]
+
+
 if __name__ == "__main__":
     demo.launch()

core/utils.py

@@ -1,5 +1,8 @@
 import numpy as np
 import tifffile
+import os
+import tempfile
+import urllib.request
 from PIL import Image
 from unet_lungs_segmentation import LungsPredict
 
@@ -68,3 +71,17 @@ def browse_overlay_axis(axis, idx, volume, seg):
     alpha = 0.3
     blended = (1 - alpha) * raw_rgb + alpha * mask_rgb
     return Image.fromarray(blended.astype(np.uint8))
+
+# Example file
+def get_example_file():
+    url = "https://zenodo.org/record/8099852/files/lungs_ct.tif?download=1"
+    tmp_dir = tempfile.gettempdir()
+    tmp_path = os.path.join(tmp_dir, "example_lungs.tif")
+
+    # Only download if it doesn't already exist
+    if not os.path.exists(tmp_path):
+        urllib.request.urlretrieve(url, tmp_path)
+
+    return tmp_path
+
+example_file_path = get_example_file()

requirements.txt

@@ -1,4 +1,2 @@
 unet_lungs_segmentation
-gradio==4.44.1
-torch==2.6.0
-torchvision==0.21.0
+gradio==5.25.1