Update app.py

app.py

@@ -370,7 +370,7 @@ def suggest_one_per_band(synth: pd.DataFrame, sigma_mkt: float, universe_user: L
         out[band.lower()] = chosen
     return out
 
-# -------------- UI helpers --------------
+# -------------- UI helpers (restored) --------------
 def empty_positions_df():
     return pd.DataFrame(columns=["ticker", "amount_usd", "weight_exposure", "beta"])
 
@@ -386,6 +386,47 @@ def set_horizon(years: float):
     RF_CODE = code
     RF_ANN = rf
 
+def search_tickers_cb(q: str):
+    opts = yahoo_search(q)
+    note = "Select a symbol and click 'Add selected to portfolio'." if opts else "No matches."
+    return note, gr.update(choices=opts, value=None)
+
+def add_symbol(selection: str, table: Optional[pd.DataFrame]):
+    if not selection:
+        return table if isinstance(table, pd.DataFrame) else pd.DataFrame(columns=["ticker","amount_usd"]), "Pick a row in Matches first."
+    symbol = selection.split("|")[0].strip().upper()
+
+    current = []
+    if isinstance(table, pd.DataFrame) and not table.empty:
+        current = [str(x).upper() for x in table["ticker"].tolist() if str(x) != "nan"]
+    tickers = current if symbol in current else current + [symbol]
+
+    val = validate_tickers(tickers, years=DEFAULT_LOOKBACK_YEARS)
+    tickers = [t for t in tickers if t in val]
+
+    amt_map = {}
+    if isinstance(table, pd.DataFrame) and not table.empty:
+        for _, r in table.iterrows():
+            t = str(r.get("ticker", "")).upper()
+            if t in tickers:
+                amt_map[t] = float(pd.to_numeric(r.get("amount_usd", 0.0), errors="coerce") or 0.0)
+
+    new_table = pd.DataFrame({"ticker": tickers, "amount_usd": [amt_map.get(t, 0.0) for t in tickers]})
+    if len(new_table) > MAX_TICKERS:
+        new_table = new_table.iloc[:MAX_TICKERS]
+        return new_table, f"Reached max of {MAX_TICKERS}."
+    return new_table, f"Added {symbol}."
+
+def lock_ticker_column(tb: Optional[pd.DataFrame]):
+    if not isinstance(tb, pd.DataFrame) or tb.empty:
+        return pd.DataFrame(columns=["ticker", "amount_usd"])
+    tickers = [str(x).upper() for x in tb["ticker"].tolist()]
+    amounts = pd.to_numeric(tb["amount_usd"], errors="coerce").fillna(0.0).tolist()
+    val = validate_tickers(tickers, years=DEFAULT_LOOKBACK_YEARS)
+    tickers = [t for t in tickers if t in val]
+    amounts = amounts[:len(tickers)] + [0.0] * max(0, len(tickers) - len(amounts))
+    return pd.DataFrame({"ticker": tickers, "amount_usd": amounts})
+
 # -------------- main compute --------------
 UNIVERSE: List[str] = [MARKET_TICKER, "QQQ", "VTI", "SOXX", "IBIT"]
 
@@ -417,13 +458,10 @@ def compute(
 
     symbols = [t for t in df["ticker"].tolist() if t]
     if len(symbols) == 0:
-        # hide suggestions
         hide = gr.update(visible=False)
         return (
             None, "Add at least one ticker.", empty_positions_df(), empty_suggestion_df(), None,
-            gr.update(visible=False),  # sugg header
-            gr.update(visible=False), gr.update(visible=False), gr.update(visible=False),  # low/med/high text
-            gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)   # buttons
+            hide, hide, hide, hide, hide, hide, hide
         )
 
     symbols = validate_tickers(symbols, years_lookback)
@@ -462,7 +500,7 @@ def compute(
     a_sigma, b_sigma, mu_eff_same_sigma = efficient_same_sigma(sigma_hist, rf_ann, erp_ann, sigma_mkt)
     a_mu, b_mu, sigma_eff_same_mu = efficient_same_return(mu_capm, rf_ann, erp_ann, sigma_mkt)
 
-    # Synthetic dataset & suggestions — exactly the user's tickers
+    # Synthetic dataset & suggestions
     user_universe = list(symbols)
     synth = build_synthetic_dataset(user_universe, covA, betas, rf_ann, erp_ann, sigma_mkt, n_rows=SYNTH_ROWS)
     csv_path = os.path.join(DATA_DIR, f"investor_profiles_{int(time.time())}.csv")
@@ -510,7 +548,6 @@ def compute(
         sugg_sigma_hist=chosen_sigma, sugg_mu_capm=chosen_mu
     )
 
-    # summary text
     info = "\n".join([
         "### Inputs",
         f"- Lookback years {years_lookback}",
@@ -531,7 +568,6 @@ def compute(
         "Weights may exceed 1 (leverage) or be negative (borrowing). If leverage isn’t allowed, scale toward 1.0."
     ])
 
-    # show Suggestions section now
     show = gr.update(visible=True)
     return (
         img, info, pos_table, sugg_table, csv_path,