Switch viewer and training to patient1 fixed flat CT

app.py

@@ -31,7 +31,7 @@ from metrics import crop_to_common_shape, ddf_summary, registration_metrics, sli
 st.set_page_config(
     page_title="VoxelMorph 颈部 CT 配准工作台",
     layout="wide",
-    initial_sidebar_state="expanded",
+    initial_sidebar_state="collapsed",
 )
 
 
@@ -608,31 +608,70 @@ def run_inference_from_ui(moving_path: str, fixed_path: str, checkpoint_path: st
     )
 
 
+def run_training_from_ui(moving_path: str, fixed_path: str, checkpoint_path: str) -> None:
+    from model_and_train import train_pair
+
+    train_pair(
+        moving_path=moving_path,
+        fixed_path=fixed_path,
+        checkpoint_path=checkpoint_path,
+        epochs=80,
+        learning_rate=1e-4,
+        smooth_weight=0.01,
+        image_loss="mse",
+        nb_features=(8, 8, 8, 8, 8),
+        save_every=20,
+    )
+
+
 def main() -> None:
     st.markdown(CUSTOM_CSS, unsafe_allow_html=True)
     st.title("VoxelMorph 颈部 CT 配准工作台")
 
-    candidates = discover_nifti_files()
-    checkpoint_candidates = discover_checkpoint_files()
-    with st.sidebar:
-        st.header("输入")
-        moving_path = choose_path("Moving", DEFAULT_MOVING_NIFTI, candidates)
-        fixed_path = choose_path("Fixed", DEFAULT_FIXED_NIFTI, candidates)
-        checkpoint_path = choose_path("模型权重", DEFAULT_CHECKPOINT, checkpoint_candidates)
-        out_dir = st.text_input("输出目录", value=str(INFERENCE_DIR))
-        display_max_voxels = st.slider("Web显示体素上限", 2_000_000, 30_000_000, 14_000_000, 1_000_000)
+    moving_path = str(DEFAULT_MOVING_NIFTI)
+    fixed_path = str(DEFAULT_FIXED_NIFTI)
+    checkpoint_path = str(DEFAULT_CHECKPOINT)
+    out_dir = str(INFERENCE_DIR)
+    display_max_voxels = 30_000_000
 
-        start = st.button("开始推理", type="primary", width="stretch")
-        if start:
-            with st.spinner("推理运行中"):
-                try:
-                    load_nifti_cached.clear()
-                    result = run_inference_from_ui(moving_path, fixed_path, checkpoint_path, out_dir)
-                    load_nifti_cached.clear()
-                    st.session_state["last_result"] = result
-                    st.success("推理完成")
-                except Exception as exc:
-                    st.error(str(exc))
+    st.caption(
+        "患者1专用：固定图像 = 平扫CT；移动图像 = 仰头CT。"
+        "推理前会重采样、归一化并裁剪/填充到同一模型网格。"
+    )
+    info_cols = st.columns(4)
+    info_cols[0].metric("Fixed", "患者1-平扫CT")
+    info_cols[1].metric("Moving", "患者1-仰头CT")
+    info_cols[2].metric("模型", Path(checkpoint_path).name)
+    info_cols[3].metric("输出", Path(out_dir).name)
+
+    action_cols = st.columns([1, 1, 4])
+    with action_cols[0]:
+        train_now = st.button("重新训练模型", type="primary", width="stretch")
+    with action_cols[1]:
+        start = st.button("开始推理", width="stretch")
+
+    if train_now:
+        with st.spinner("患者1模型训练中"):
+            try:
+                load_nifti_cached.clear()
+                run_training_from_ui(moving_path, fixed_path, checkpoint_path)
+                result = run_inference_from_ui(moving_path, fixed_path, checkpoint_path, out_dir)
+                load_nifti_cached.clear()
+                st.session_state["last_result"] = result
+                st.success("训练和推理完成")
+            except Exception as exc:
+                st.error(str(exc))
+
+    if start:
+        with st.spinner("推理运行中"):
+            try:
+                load_nifti_cached.clear()
+                result = run_inference_from_ui(moving_path, fixed_path, checkpoint_path, out_dir)
+                load_nifti_cached.clear()
+                st.session_state["last_result"] = result
+                st.success("推理完成")
+            except Exception as exc:
+                st.error(str(exc))
 
     result_paths = load_result_paths(out_dir)
     last_result = st.session_state.get("last_result", {})