Update streamlit_app.py

streamlit_app.py

@@ -66,29 +66,6 @@ def load_daily_summary() -> dict:
 
 
 def normalize_summary_payload(summary: dict) -> dict:
-    """
-    Supports both the improved API shape and the previous legacy shape.
-
-    Preferred shape:
-    {
-        "summary_date": "...",
-        "generated_at": "...",
-        "executive_summary": "...",
-        "key_signal": "...",
-        "recommended_focus": "...",
-        "decision_implications": [...],
-        "watchlist": [...],
-        "top_stories": [...]
-    }
-
-    Legacy shape:
-    {
-        "summary_date": "...",
-        "short_summary": "...",
-        "key_focus": "...",
-        "top_stories": "{\"executive_summary\": ..., \"top_stories\": [...]}"
-    }
-    """
     normalized = dict(summary)
 
     nested_summary = summary.get("summary_json") or summary.get("top_stories")
@@ -131,8 +108,8 @@ def normalize_summary_payload(summary: dict) -> dict:
 def apply_filters(df: pd.DataFrame) -> pd.DataFrame:
     st.sidebar.header("Filters")
 
-    label_options = sorted(df["label"].dropna().unique().tolist()) if not df.empty else []
-    source_options = sorted(df["source"].dropna().unique().tolist()) if not df.empty else []
+    label_options = sorted(df["label"].dropna().unique().tolist()) if "label" in df else []
+    source_options = sorted(df["source"].dropna().unique().tolist()) if "source" in df else []
 
     default_labels = [
         label
@@ -152,8 +129,8 @@ def apply_filters(df: pd.DataFrame) -> pd.DataFrame:
         default=[],
     )
 
-    min_date = df["published_date"].min() if not df.empty else None
-    max_date = df["published_date"].max() if not df.empty else None
+    min_date = df["published_date"].min() if "published_date" in df and not df.empty else None
+    max_date = df["published_date"].max() if "published_date" in df and not df.empty else None
 
     date_range = None
     if min_date and max_date:
@@ -207,7 +184,7 @@ def render_metrics(df: pd.DataFrame, filtered_df: pd.DataFrame) -> None:
     c1, c2, c3, c4 = st.columns(4)
 
     c1.metric("Articles", len(df))
-    c2.metric("Filtered", len(filtered_df))
+    c2.metric("Shown", len(filtered_df))
     c3.metric("Sources", df["source"].nunique() if "source" in df else 0)
     c4.metric("Categories", df["label"].nunique() if "label" in df else 0)
 
@@ -395,16 +372,19 @@ def main() -> None:
         st.warning("No classified articles found yet. Check whether the API is live and returning data.")
         return
 
-    filtered_df = apply_filters(df)
-
-    render_metrics(df, filtered_df)
-
-    tab1, tab2 = st.tabs(["Daily Summary", "Articles"])
+    section = st.segmented_control(
+        "View",
+        options=["Daily Summary", "Articles"],
+        default="Daily Summary",
+    )
 
-    with tab1:
+    if section == "Daily Summary":
+        render_metrics(df, df)
         render_daily_summary(summary)
 
-    with tab2:
+    elif section == "Articles":
+        filtered_df = apply_filters(df)
+        render_metrics(df, filtered_df)
         render_article_browser(filtered_df)