Delete app.py

app.py

@@ -1,183 +0,0 @@
-import os
-import time
-import h5py
-import numpy as np
-import gradio as gr
-import plotly.graph_objects as go
-from railnet_model import RailNetSystem
-
-from huggingface_hub import hf_hub_download
-
-os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
-os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-
-# model = RailNetSystem.from_pretrained(".").cuda()
-
-model = RailNetSystem.from_pretrained("Tournesol-Saturday/railNet-tooth-segmentation-in-CBCT-image").cuda()
-
-model.load_weights(from_hub=True, repo_id="Tournesol-Saturday/railNet-tooth-segmentation-in-CBCT-image")
-
-
-# def wait_for_stable_file(file_path, timeout=5, check_interval=0.2):
-#     start_time = time.time()
-#     last_size = -1
-#     while time.time() - start_time < timeout:
-#         current_size = os.path.getsize(file_path)
-#         if current_size == last_size:
-#             return True
-#         last_size = current_size
-#         time.sleep(check_interval)
-#     return False
-
-# def process_cbct_file(h5_file, save_dir="./output"):
-#     if not wait_for_stable_file(h5_file.name):
-#         raise RuntimeError("File upload has not been completed or is unstable, please try again.")
-
-#     try:
-#         with h5py.File(h5_file.name, "r") as f:
-#             if "image" not in f or "label" not in f:
-#                 raise KeyError("The file is missing ‘image’ or ‘label’ value")
-#             image = f["image"][:]
-#             label = f["label"][:]
-#     except Exception as e:
-#         raise RuntimeError(f"Failed to read the .h5 file: {str(e)}")
-
-#     name = os.path.basename(h5_file.name).replace(".h5", "")
-#     pred, dice, jc, hd, asd = model(image, label, save_dir, name)
-
-#     img_path = os.path.join(save_dir, f"{name}_img.nii.gz")
-#     pred_path = os.path.join(save_dir, f"{name}_pred.nii.gz")
-
-#     return pred, f"Dice: {dice:.4f}, Jaccard: {jc:.4f}, 95HD: {hd:.2f}, ASD: {asd:.2f}", img_path, pred_path
-
-def render_plotly_volume(pred, x_eye=1.25, y_eye=1.25, z_eye=1.25):
-    downsample_factor = 2
-    pred_ds = pred[::downsample_factor, ::downsample_factor, ::downsample_factor]
-
-    fig = go.Figure(data=go.Volume(
-        x=np.repeat(np.arange(pred_ds.shape[0]), pred_ds.shape[1] * pred_ds.shape[2]),
-        y=np.tile(np.repeat(np.arange(pred_ds.shape[1]), pred_ds.shape[2]), pred_ds.shape[0]),
-        z=np.tile(np.arange(pred_ds.shape[2]), pred_ds.shape[0] * pred_ds.shape[1]),
-        value=pred_ds.flatten(),
-        isomin=0.5,
-        isomax=1.0,
-        opacity=0.1,
-        surface_count=1,
-        colorscale=[[0, 'rgb(255, 0, 0)'], [1, 'rgb(255, 0, 0)']],
-        showscale=False
-    ))
-
-    fig.update_layout(
-        scene=dict(
-            xaxis=dict(visible=False),
-            yaxis=dict(visible=False),
-            zaxis=dict(visible=False),
-            camera=dict(eye=dict(x=x_eye, y=y_eye, z=z_eye))
-        ),
-        margin=dict(l=0, r=0, b=0, t=0)
-    )
-    return fig
-
-
-def handle_example(filename):
-    repo_id = "Tournesol-Saturday/railNet-tooth-segmentation-in-CBCT-image"
-    h5_path = hf_hub_download(repo_id=repo_id, filename=f"example_input_file/{filename}")
-   
-    with h5py.File(h5_path, "r") as f:
-        image = f["image"][:]
-        label = f["label"][:]
-    
-    name = filename.replace(".h5", "")
-    pred, dice, jc, hd, asd = model(image, label, "./output", name)
-    
-    fig = render_plotly_volume(pred)
-    
-    img_path = f"./output/{name}_img.nii.gz"
-    pred_path = f"./output/{name}_pred.nii.gz"
-    
-    metrics = f"Dice: {dice:.4f}, Jaccard: {jc:.4f}, 95HD: {hd:.2f}, ASD: {asd:.2f}"
-    
-    return metrics, pred, fig, img_path, pred_path
-
-
-def clear_all():
-    return "", None, None, None, None
-
-with gr.Blocks() as demo:
-    gr.HTML("<div style='text-align: center; font-size: 22px; font-weight: bold;'>🦷 Demo of RailNet: A CBCT Tooth Segmentation System</div>")
-    gr.HTML("<div style='text-align: center; font-size: 15px'>✅ Steps: Select a CBCT example file (.h5)  →  Automatic inference and metrics display  →  View 3D segmentation result (Mouse drag and scroll wheel zooming)</div>")
-
-    # gr.HTML("<div style='font-size: 15px; font-weight: bold;'>📂 Step 1: Upload the .h5 example file containing both ‘image’ and ‘label’ values</div>")
-    gr.HTML("""
-    <style>
-    .code-style {
-        font-family: monospace;
-        background-color: #2f363d;
-        color: #ffffff;
-        padding: 2px 6px;
-        border-radius: 4px;
-        font-size: 90%;
-    }
-    </style>
-
-    <div style='font-size: 15px; font-weight: bold;'>
-        📂 Step 1: Select a <span class='code-style'>.h5</span> example file from the <span class='code-style'>example_input_file</span> folder in our
-        <a href='https://huggingface.co/Tournesol-Saturday/railNet-tooth-segmentation-in-CBCT-image' target='_blank' style='text-decoration: none; color: #1f6feb; font-weight: bold;'>
-        Hugging Face model
-        </a> repository. 
-    </div>
-    """)
-
-    # file_input = gr.File()
-
-
-    example_files = ["CBCT_01.h5", "CBCT_02.h5", "CBCT_03.h5", "CBCT_04.h5"] 
-    dropdown = gr.Dropdown(choices=example_files, label="Example File", value=example_files[0])
-
-
-    with gr.Row():
-        clear_btn = gr.Button("清除", variant="secondary")
-        submit_btn = gr.Button("提交", variant="primary")
-
-    gr.HTML("<div style='font-size: 15px; font-weight: bold;'>📊 Step 2: Metrics (Dice, Jaccard, 95HD, ASD)</div>")
-    result_text = gr.Textbox()
-    hidden_pred = gr.State(value=None) 
-
-    gr.HTML("<div style='font-size: 15px; font-weight: bold;'>👁️ Step 3: 3D Visualisation</div>")
-    plot_output = gr.Plot()
-
-    hidden_img_file = gr.File(visible=False)
-    hidden_pred_file = gr.File(visible=False)
-
-    gr.HTML("<div style='font-size: 15px; font-weight: bold;'>⬇️ Step 4: Download <span class='code-style'>NIfTI</span> files for accurate 1:1 visualization using <span class='code-style'>ITK-SNAP</span> software</div>")
-    with gr.Row():
-        download_img_btn = gr.Button("Download Original Image")
-        download_pred_btn = gr.Button("Download Segmentation Result")
-
-    # def handle_upload(h5_file):
-    #     pred, metrics, img_path, pred_path = process_cbct_file(h5_file)
-    #     fig = render_plotly_volume(pred)
-    #     return metrics, pred, fig, img_path, pred_path
-
-    submit_btn.click(
-        fn=handle_example,
-        inputs=[dropdown],
-        outputs=[result_text, hidden_pred, plot_output, hidden_img_file, hidden_pred_file]
-    )
-
-    def update_view(pred, x_eye, y_eye, z_eye):
-        if pred is None:
-            return gr.update()
-        return render_plotly_volume(pred, x_eye, y_eye, z_eye)
-
-    clear_btn.click(
-        fn=clear_all,
-        inputs=[],
-        outputs=[result_text, hidden_pred, plot_output, hidden_img_file, hidden_pred_file]
-    )
-
-    download_img_btn.click(fn=lambda f: f, inputs=[hidden_img_file], outputs=[hidden_img_file])
-    download_pred_btn.click(fn=lambda f: f, inputs=[hidden_pred_file], outputs=[hidden_pred_file])
-
-demo.launch()
-