Update app.py

app.py

@@ -392,19 +392,21 @@ def set_horizon(years: float):
 def search_tickers_cb(q: str):
     opts = yahoo_search(q)
     if not opts:
-        opts = ["No matches found"]
-    # Put helper text into Matches box
+        return gr.update(
+            choices=["No matches found"],
+            value=None,
+            info="No matches."
+        )
+    first = opts[0]  # preselect the first hit
     return gr.update(
         choices=opts,
-        value=None,
+        value=first,
         info="Select a symbol and click 'Add selected to portfolio'."
     )
 
 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."
-    if "No matches" in str(selection):
-        return table if isinstance(table, pd.DataFrame) else pd.DataFrame(columns=["ticker","amount_usd"]), "No symbol to add."
+    if (not selection) or ("No matches" in selection):
+        return table if isinstance(table, pd.DataFrame) else pd.DataFrame(columns=["ticker","amount_usd"]), "Pick a valid match first."
     symbol = selection.split("|")[0].strip().upper()
 
     current = []
@@ -429,8 +431,7 @@ def add_symbol(selection: str, table: Optional[pd.DataFrame]):
     return new_table, f"Added {symbol}."
 
 def add_symbol_table_only(selection: str, table: Optional[pd.DataFrame]):
-    """Wrapper that only returns the updated table (no status string)."""
-    new_table, _ = add_symbol(selection, table)
+    new_table, _msg = add_symbol(selection, table)
     return new_table
 
 def lock_ticker_column(tb: Optional[pd.DataFrame]):
@@ -460,11 +461,9 @@ def compute(
     years_lookback: int,
     table: Optional[pd.DataFrame],
     pick_band_to_show: str,  # "Low" | "Medium" | "High"
-    progress=gr.Progress(track_tqdm=True)
+    progress=gr.Progress(track_tqdm=True),
 ):
-    # progress indicator
-    progress(0, desc="Validating inputs...")
-    time.sleep(0.05)
+    progress(0.05, desc="Validating tickers...")
 
     # sanitize table
     if isinstance(table, pd.DataFrame):
@@ -479,35 +478,44 @@ def compute(
 
     symbols = [t for t in df["ticker"].tolist() if t]
     if len(symbols) == 0:
-        msg = "Add at least one ticker (and ensure VOO is present in data history)."
+        out_empty = gr.update(visible=True, value="Add at least one ticker.")
+        empty_df = gr.update(visible=True, value=empty_positions_df())
+        empty_sugg = gr.update(visible=True, value=empty_suggestion_df())
+        none_file = gr.update(visible=True, value=None)
         return (
-            gr.update(value=None, visible=True),  # plot placeholder stays blank
-            gr.update(value="### Error\n" + msg, visible=True),
-            gr.update(value=empty_positions_df(), visible=True),
-            gr.update(value=empty_suggestion_df(), visible=True),
-            gr.update(value=None, visible=False),
-            gr.update(value="", visible=True),
-            gr.update(value="", visible=True),
-            gr.update(value="", visible=True),
-            gr.update(visible=True),   # show outputs group so message is visible
-            gr.update(visible=True)    # show suggestions container (buttons/text)
+            gr.update(visible=True, value=None),  # plot
+            out_empty,                            # summary
+            empty_df,                             # positions
+            empty_sugg,                           # sugg_table
+            none_file,                            # file
+            gr.update(visible=True, value=""),    # low_txt
+            gr.update(visible=True, value=""),    # med_txt
+            gr.update(visible=True, value=""),    # high_txt
+            gr.update(visible=True),              # md_sugg
+            gr.update(visible=True),              # btn_low
+            gr.update(visible=True),              # btn_med
+            gr.update(visible=True),              # btn_high
         )
 
-    progress(0.15, desc="Validating tickers...")
     symbols = validate_tickers(symbols, years_lookback)
     if len(symbols) == 0:
-        msg = "Could not validate any tickers for the chosen lookback (VOO data required)."
+        out_empty = gr.update(visible=True, value="Could not validate any tickers.")
+        empty_df = gr.update(visible=True, value=empty_positions_df())
+        empty_sugg = gr.update(visible=True, value=empty_suggestion_df())
+        none_file = gr.update(visible=True, value=None)
         return (
-            gr.update(value=None, visible=True),
-            gr.update(value="### Error\n" + msg, visible=True),
-            gr.update(value=empty_positions_df(), visible=True),
-            gr.update(value=empty_suggestion_df(), visible=True),
-            gr.update(value=None, visible=False),
-            gr.update(value="", visible=True),
-            gr.update(value="", visible=True),
-            gr.update(value="", visible=True),
+            gr.update(visible=True, value=None),
+            out_empty,
+            empty_df,
+            empty_sugg,
+            none_file,
+            gr.update(visible=True, value=""),
+            gr.update(visible=True, value=""),
+            gr.update(visible=True, value=""),
+            gr.update(visible=True),
+            gr.update(visible=True),
+            gr.update(visible=True),
             gr.update(visible=True),
-            gr.update(visible=True)
         )
 
     global UNIVERSE
@@ -517,37 +525,40 @@ def compute(
     amounts = {r["ticker"]: float(r["amount_usd"]) for _, r in df.iterrows()}
     rf_ann = RF_ANN
 
-    progress(0.35, desc="Downloading prices & estimating moments...")
+    progress(0.20, desc="Downloading prices & computing returns...")
     moms = estimate_all_moments_aligned(symbols, years_lookback, rf_ann)
     betas, covA, erp_ann, sigma_mkt = moms["betas"], moms["cov_ann"], moms["erp_ann"], moms["sigma_m_ann"]
 
     gross = sum(abs(v) for v in amounts.values())
     if gross <= 1e-12:
-        msg = "All amounts are zero."
+        out_empty = gr.update(visible=True, value="All amounts are zero.")
+        empty_df = gr.update(visible=True, value=empty_positions_df())
+        empty_sugg = gr.update(visible=True, value=empty_suggestion_df())
+        none_file = gr.update(visible=True, value=None)
         return (
-            gr.update(value=None, visible=True),
-            gr.update(value="### Error\n" + msg, visible=True),
-            gr.update(value=empty_positions_df(), visible=True),
-            gr.update(value=empty_suggestion_df(), visible=True),
-            gr.update(value=None, visible=False),
-            gr.update(value="", visible=True),
-            gr.update(value="", visible=True),
-            gr.update(value="", visible=True),
+            gr.update(visible=True, value=None),
+            out_empty,
+            empty_df,
+            empty_sugg,
+            none_file,
+            gr.update(visible=True, value=""),
+            gr.update(visible=True, value=""),
+            gr.update(visible=True, value=""),
+            gr.update(visible=True),
+            gr.update(visible=True),
+            gr.update(visible=True),
             gr.update(visible=True),
-            gr.update(visible=True)
         )
     weights = {k: v / gross for k, v in amounts.items()}
 
-    # Portfolio CAPM stats
-    progress(0.55, desc="Computing CAPM stats...")
+    progress(0.35, desc="Computing CAPM stats...")
     beta_p, mu_capm, sigma_hist = portfolio_stats(weights, covA, betas, rf_ann, erp_ann)
 
-    # Efficient alternatives on CML
+    progress(0.50, desc="Efficient mixes on CML...")
     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
-    progress(0.75, desc="Building synthetic dataset & suggestions...")
+    progress(0.70, desc="Building 1,000 candidate mixes...")
     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")
@@ -556,6 +567,7 @@ def compute(
     except Exception:
         csv_path = None
 
+    progress(0.85, desc="Ranking suggestions...")
     picks = suggest_one_per_band(synth, sigma_mkt, user_universe)
 
     def _fmt(row: pd.Series) -> str:
@@ -572,11 +584,11 @@ def compute(
     if chosen is None or chosen.empty:
         chosen_sigma = None
         chosen_mu = None
-        sugg_table_df = empty_suggestion_df()
+        sugg_table = empty_suggestion_df()
     else:
         chosen_sigma = float(chosen["sigma_hist"])
         chosen_mu = float(chosen["mu_capm"])
-        sugg_table_df = _holdings_table_from_row(chosen, budget=gross)
+        sugg_table = _holdings_table_from_row(chosen, budget=gross)
 
     pos_table = pd.DataFrame(
         [{
@@ -588,6 +600,7 @@ def compute(
         columns=["ticker", "amount_usd", "weight_exposure", "beta"]
     )
 
+    progress(0.95, desc="Rendering chart...")
     img = plot_cml(
         rf_ann, erp_ann, sigma_mkt,
         sigma_hist, mu_capm,
@@ -595,7 +608,6 @@ def compute(
         sugg_sigma_hist=chosen_sigma, sugg_mu_capm=chosen_mu
     )
 
-    # Summary text (clean)
     info = "\n".join([
         "### Inputs",
         f"- Lookback years {years_lookback}",
@@ -617,87 +629,84 @@ def compute(
         "If leverage isn’t allowed, scale both weights proportionally toward 1.0 to fit your constraints.",
     ])
 
-    progress(0.98, desc="Rendering...")
-    time.sleep(0.05)
-
+    progress(1.0, desc="Done.")
     return (
-        gr.update(value=img, visible=True),
-        gr.update(value=info, visible=True),
-        gr.update(value=pos_table, visible=True),
-        gr.update(value=sugg_table_df, visible=True),
-        gr.update(value=csv_path, visible=bool(csv_path)),
-        gr.update(value=txt_low, visible=True),
-        gr.update(value=txt_med, visible=True),
-        gr.update(value=txt_high, visible=True),
-        gr.update(visible=True),  # show outputs group
-        gr.update(visible=True)   # show suggestions group
+        gr.update(visible=True, value=img),         # plot
+        gr.update(visible=True, value=info),        # summary
+        gr.update(visible=True, value=pos_table),   # positions
+        gr.update(visible=True, value=sugg_table),  # sugg_table
+        gr.update(visible=True, value=csv_path),    # file
+        gr.update(visible=True, value=txt_low),     # low_txt
+        gr.update(visible=True, value=txt_med),     # med_txt
+        gr.update(visible=True, value=txt_high),    # high_txt
+        gr.update(visible=True),                    # md_sugg
+        gr.update(visible=True),                    # btn_low
+        gr.update(visible=True),                    # btn_med
+        gr.update(visible=True),                    # btn_high
     )
 
 # -------------- UI --------------
-with gr.Blocks(title="Efficient Portfolio Advisor") as demo:
+with gr.Blocks(title="Efficient Portfolio Advisor", theme=gr.themes.Soft()) as demo:
     gr.Markdown(
         "## Efficient Portfolio Advisor\n"
-        "Search symbols, enter **dollar amounts**, set horizon. Returns use Yahoo Finance monthly data; risk-free from FRED. "
-        "Plot shows **your CAPM point on the CML** plus efficient market/bills points."
+        "Search symbols, enter **dollar amounts**, set horizon. Returns use Yahoo Finance monthly data; risk-free from FRED."
     )
 
-    # LEFT: controls (fill page initially)
-    with gr.Group(visible=True) as controls_group:
-        # Search flow: Search -> Button -> Matches -> Add
-        q = gr.Textbox(label="Search symbol")
-        search_btn = gr.Button("Search")
-        matches = gr.Dropdown(choices=[], label="Matches")
-        add_btn = gr.Button("Add selected to portfolio")
-
-        gr.Markdown("### Portfolio positions")
-        table = gr.Dataframe(
-            headers=["ticker", "amount_usd"],
-            datatype=["str", "number"],
-            row_count=0,
-            col_count=(2, "fixed")
-        )
+    with gr.Row():
+        with gr.Column(scale=1):
+            # --- Vertical flow: Search -> Button -> Matches -> Add ---
+            q = gr.Textbox(label="Search symbol")
+            search_btn = gr.Button("Search")
+            matches = gr.Dropdown(choices=[], label="Matches", allow_custom_value=False)
+            add_btn = gr.Button("Add selected to portfolio")
+            # ----------------------------------------------------------
+
+            gr.Markdown("### Portfolio positions")
+            table = gr.Dataframe(
+                headers=["ticker", "amount_usd"],
+                datatype=["str", "number"],
+                row_count=0,
+                col_count=(2, "fixed")
+            )
 
-        horizon = gr.Number(label="Horizon in years (1–100)", value=HORIZON_YEARS, precision=0)
-        lookback = gr.Slider(1, 15, value=DEFAULT_LOOKBACK_YEARS, step=1, label="Lookback years for betas & covariances")
+            horizon = gr.Number(label="Horizon in years (1–100)", value=HORIZON_YEARS, precision=0)
+            lookback = gr.Slider(1, 15, value=DEFAULT_LOOKBACK_YEARS, step=1, label="Lookback years for betas & covariances")
 
-        # Compute button directly under lookback slider
-        run_btn = gr.Button("Compute (build dataset & suggest)")
+            # compute button directly under lookback slider
+            run_btn = gr.Button("Compute (build dataset & suggest)")
 
-        # Suggestions container (hidden until compute)
-        with gr.Group(visible=False) as sugg_group:
-            gr.Markdown("### Suggestions")
+            # Suggestions section (hidden until first compute)
+            md_sugg = gr.Markdown("### Suggestions", visible=False)
             with gr.Row():
-                btn_low = gr.Button("Show Low")
-                btn_med = gr.Button("Show Medium")
-                btn_high = gr.Button("Show High")
-            low_txt = gr.Markdown()
-            med_txt = gr.Markdown()
-            high_txt = gr.Markdown()
-
-    # RIGHT: outputs (hidden until compute)
-    with gr.Group(visible=False) as out_group:
-        with gr.Row():
-            with gr.Column(scale=1):
-                plot = gr.Image(label="Capital Market Line (CAPM)", type="pil")
-                summary = gr.Markdown(label="Inputs & Results")
-            with gr.Column(scale=1):
-                positions = gr.Dataframe(
-                    label="Computed positions",
-                    headers=["ticker", "amount_usd", "weight_exposure", "beta"],
-                    datatype=["str", "number", "number", "number"],
-                    col_count=(4, "fixed"),
-                    value=empty_positions_df(),
-                    interactive=False
-                )
-                sugg_table = gr.Dataframe(
-                    label="Selected suggestion holdings (% / $)",
-                    headers=["ticker", "weight_%", "amount_$"],
-                    datatype=["str", "number", "number"],
-                    col_count=(3, "fixed"),
-                    value=empty_suggestion_df(),
-                    interactive=False
-                )
-                dl = gr.File(label="Generated dataset CSV", value=None, visible=True)
+                btn_low = gr.Button("Show Low", visible=False)
+                btn_med = gr.Button("Show Medium", visible=False)
+                btn_high = gr.Button("Show High", visible=False)
+            low_txt = gr.Markdown(visible=False)
+            med_txt = gr.Markdown(visible=False)
+            high_txt = gr.Markdown(visible=False)
+
+        with gr.Column(scale=1):
+            plot = gr.Image(label="Capital Market Line (CAPM)", type="pil", visible=False)
+            summary = gr.Markdown(label="Inputs & Results", visible=False)
+            positions = gr.Dataframe(
+                label="Computed positions",
+                headers=["ticker", "amount_usd", "weight_exposure", "beta"],
+                datatype=["str", "number", "number", "number"],
+                col_count=(4, "fixed"),
+                value=empty_positions_df(),
+                interactive=False,
+                visible=False
+            )
+            sugg_table = gr.Dataframe(
+                label="Selected suggestion holdings (% / $)",
+                headers=["ticker", "weight_%", "amount_$"],
+                datatype=["str", "number", "number"],
+                col_count=(3, "fixed"),
+                value=empty_suggestion_df(),
+                interactive=False,
+                visible=False
+            )
+            dl = gr.File(label="Generated dataset CSV", value=None, visible=False)
 
     # wire search / add / locking
     search_btn.click(fn=search_tickers_cb, inputs=q, outputs=matches)
@@ -707,25 +716,25 @@ with gr.Blocks(title="Efficient Portfolio Advisor") as demo:
     # horizon updates globals silently (no UI output)
     horizon.change(fn=set_horizon, inputs=horizon, outputs=[])
 
-    # compute + render (default to Medium band)
+    # compute + reveal UI (default to Medium band)
     run_btn.click(
         fn=compute,
         inputs=[lookback, table, gr.State("Medium")],
         outputs=[
             plot, summary, positions, sugg_table, dl,
             low_txt, med_txt, high_txt,
-            out_group, sugg_group
+            md_sugg, btn_low, btn_med, btn_high,
         ]
     )
 
-    # band buttons recompute picks quickly (keep groups visible)
+    # band buttons recompute picks quickly (keep everything visible)
     btn_low.click(
         fn=compute,
         inputs=[lookback, table, gr.State("Low")],
         outputs=[
             plot, summary, positions, sugg_table, dl,
             low_txt, med_txt, high_txt,
-            out_group, sugg_group
+            md_sugg, btn_low, btn_med, btn_high,
         ]
     )
     btn_med.click(
@@ -734,7 +743,7 @@ with gr.Blocks(title="Efficient Portfolio Advisor") as demo:
         outputs=[
             plot, summary, positions, sugg_table, dl,
             low_txt, med_txt, high_txt,
-            out_group, sugg_group
+            md_sugg, btn_low, btn_med, btn_high,
         ]
     )
     btn_high.click(
@@ -743,7 +752,7 @@ with gr.Blocks(title="Efficient Portfolio Advisor") as demo:
         outputs=[
             plot, summary, positions, sugg_table, dl,
             low_txt, med_txt, high_txt,
-            out_group, sugg_group
+            md_sugg, btn_low, btn_med, btn_high,
         ]
     )
 
@@ -752,6 +761,4 @@ RF_CODE = fred_series_for_horizon(HORIZON_YEARS)
 RF_ANN = fetch_fred_yield_annual(RF_CODE)
 
 if __name__ == "__main__":
-    # enable queue for visible progress bar
     demo.queue().launch(server_name="0.0.0.0", server_port=7860, show_api=False)
-