gradio_app.py

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

os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
os.environ["CUDA_VISIBLE_DEVICES"] = "0"

model = RailNetSystem.from_pretrained(".").cuda()
model.load_weights(".")

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)
    return pred, f"Dice: {dice:.4f}, Jaccard: {jc:.4f}, 95HD: {hd:.2f}, ASD: {asd:.2f}"

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 clear_all():
    return None, "", None

with gr.Blocks() as demo:
    gr.Markdown("<div style='text-align: center; font-size: 28px; font-weight: bold;'>🦷 Demo of RailNet: A CBCT Tooth Segmentation System</div>")
    gr.Markdown("<div style='text-align: center; font-size: 20px'>✅ Steps: Upload a CBCT example file (.h5)  →  Automatic inference and metrics display  →  View 3D segmentation result (Mouse drag and scroll wheel zooming)</div>")

    gr.Markdown("<div style='height: 20px;'></div>")
    gr.Markdown("<div style='font-size: 20px; font-weight: bold;'>📂 Step 1: Upload the .h5 example file containing both ‘image’ and ‘label’ values</div>")
    file_input = gr.File()
    with gr.Row():
        clear_btn = gr.Button("清除", variant="secondary")
        submit_btn = gr.Button("提交", variant="primary")

    gr.Markdown("<div style='height: 20px;'></div>")
    gr.Markdown("<div style='font-size: 20px; font-weight: bold;'>📊 Step 2: Metrics (Dice, Jaccard, 95HD, ASD)</div>")
    result_text = gr.Textbox()
    hidden_pred = gr.State(value=None) 

    gr.Markdown("<div style='height: 20px;'></div>")
    gr.Markdown("<div style='font-size: 20px; font-weight: bold;'>👁️ Step 3: 3D Visualisation</div>")
    plot_output = gr.Plot()

    def handle_upload(h5_file):
        pred, metrics = process_cbct_file(h5_file)
        fig = render_plotly_volume(pred)
        return metrics, pred, fig

    submit_btn.click(
        fn=handle_upload,
        inputs=[file_input],
        outputs=[result_text, hidden_pred, plot_output]
    )

    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=[file_input, result_text, plot_output]
    )

demo.launch()