Update app.py

app.py

@@ -657,6 +657,16 @@ def build_shap_explainer(pipe, X_bg, max_bg=200):
     )
     return explainer
 
+def ensure_model_repo_exists(model_repo_id: str, token: str):
+    """
+    Optional helper: create the model repo if it doesn't exist.
+    Safe to call; if it exists, it will error -> you can ignore.
+    """
+    api = HfApi(token=token)
+    try:
+        api.create_repo(repo_id=model_repo_id, repo_type="model", private=False, exist_ok=True)
+    except Exception:
+        pass
 
 
 def publish_to_hub(model_repo_id: str, version_tag: str):
@@ -1483,26 +1493,28 @@ with tab_train:
 # ---------------- PUBLISH (only after training) ----------------
 
     
-        if st.session_state.get("pipe") is not None:
-            st.divider()
-            st.subheader("Publish trained model to Hugging Face Hub")
-
-            default_version = datetime.utcnow().strftime("%Y%m%d-%H%M%S")
-            version_tag = st.text_input(
-                "Version tag",
-                value=default_version,
-                help="Used as releases/<version>/ in the model repository",
-            )
-
-            if st.button("Publish model.joblib + meta.json to Model Repo"):
-                try:
-                    with st.spinner("Uploading to Hugging Face Model repo..."):
-                        paths = publish_to_hub(MODEL_REPO_ID, version_tag)
+            # ---------------- PUBLISH (only after training) ----------------
+            if st.session_state.get("pipe") is not None:
+                st.divider()
+                st.subheader("Publish trained model to Hugging Face Hub")
+            
+                default_version = datetime.utcnow().strftime("%Y%m%d-%H%M%S")
+                version_tag = st.text_input(
+                    "Version tag",
+                    value=default_version,
+                    help="Used as releases/<version>/ in the model repository",
+                )
             
-                    st.success("Uploaded successfully to your model repository.")
-                    st.json(paths)
-                except Exception as e:
-                    st.error(f"Upload failed: {e}")
+                if st.button("Publish model.joblib + meta.json to Model Repo", key="publish_btn"):
+                    try:
+                        with st.spinner("Uploading to Hugging Face Model repo..."):
+                            paths = publish_to_hub(MODEL_REPO_ID, version_tag)
+            
+                        st.success("Uploaded successfully to your model repository.")
+                        st.json(paths)
+                    except Exception as e:
+                        st.error(f"Upload failed: {e}")
+
 
 
 
@@ -2078,7 +2090,22 @@ with tab_predict:
         st.divider()
         st.subheader("Predict single patient")
 
-        thr_single = st.slider("Classification threshold", 0.0, 1.0, 0.5, 0.01, key="sp_thr")
+        m = meta.get("metrics", {})
+        default_thr = float(m.get("best_threshold", 0.5))
+        
+        thr_single = st.slider(
+            "Classification threshold",
+            0.0, 1.0, default_thr, 0.01,
+            key="sp_thr"
+        )
+        
+        # External validation threshold
+        thr_ext = st.slider(
+            "External validation threshold",
+            0.0, 1.0, default_thr, 0.01,
+            key="thr_ext"
+        )
+
         low_cut_s, high_cut_s = st.slider(
             "Risk band cutoffs (low, high)",
             0.0, 1.0, (0.2, 0.8), 0.01,