Update app.py

app.py

@@ -392,20 +392,14 @@ def set_horizon(years: float):
 def search_tickers_cb(q: str):
     opts = yahoo_search(q)
     if not opts:
-        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=first,
-        info="Select a symbol and click 'Add selected to portfolio'."
-    )
+        opts = ["No matches found"]
+    # First match auto-selected, helper info inside dropdown
+    first = opts[0] if opts and ("No matches" not in opts[0]) else None
+    info = "Select a symbol and click 'Add selected to portfolio'." if first else "No matches."
+    return gr.update(choices=opts, value=first, info=info)
 
 def add_symbol(selection: str, table: Optional[pd.DataFrame]):
-    if (not selection) or ("No matches" in selection):
+    if (not selection) or ("No matches" in selection) or ("Select a symbol" in selection) or ("type above" 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()
 
@@ -478,46 +472,18 @@ def compute(
 
     symbols = [t for t in df["ticker"].tolist() if t]
     if len(symbols) == 0:
-        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(visible=True),              # show results group after compute
-            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
+            None, "Add at least one ticker.", empty_positions_df(), empty_suggestion_df(), None,
+            "", "", "",
+            None, None, None, None, None, None, None, None, None
         )
 
     symbols = validate_tickers(symbols, years_lookback)
     if len(symbols) == 0:
-        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(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),
+            None, "Could not validate any tickers.", empty_positions_df(), empty_suggestion_df(), None,
+            "", "", "",
+            None, None, None, None, None, None, None, None, None
         )
 
     global UNIVERSE
@@ -527,41 +493,32 @@ def compute(
     amounts = {r["ticker"]: float(r["amount_usd"]) for _, r in df.iterrows()}
     rf_ann = RF_ANN
 
-    progress(0.20, desc="Downloading prices & computing returns...")
+    progress(0.25, desc="Downloading prices & computing moments...")
+
+    # Moments
     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"]
 
+    # Weights
     gross = sum(abs(v) for v in amounts.values())
     if gross <= 1e-12:
-        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(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),
+            None, "All amounts are zero.", empty_positions_df(), empty_suggestion_df(), None,
+            "", "", "",
+            rf_ann, erp_ann, sigma_mkt, None, None, None, None, None, None
         )
     weights = {k: v / gross for k, v in amounts.items()}
 
-    progress(0.35, desc="Computing CAPM stats...")
+    # Portfolio CAPM stats
     beta_p, mu_capm, sigma_hist = portfolio_stats(weights, covA, betas, rf_ann, erp_ann)
 
-    progress(0.50, desc="Efficient mixes on CML...")
+    progress(0.55, desc="Building synthetic dataset...")
+
+    # Efficient alternatives 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)
 
-    progress(0.70, desc="Building 1,000 candidate mixes...")
+    # Synthetic dataset & suggestions — exactly the user's tickers
     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")
@@ -570,7 +527,8 @@ def compute(
     except Exception:
         csv_path = None
 
-    progress(0.85, desc="Ranking suggestions...")
+    progress(0.8, desc="Selecting suggestions...")
+
     picks = suggest_one_per_band(synth, sigma_mkt, user_universe)
 
     def _fmt(row: pd.Series) -> str:
@@ -591,6 +549,7 @@ def compute(
     else:
         chosen_sigma = float(chosen["sigma_hist"])
         chosen_mu = float(chosen["mu_capm"])
+        # holdings table from chosen suggestion
         sugg_table = _holdings_table_from_row(chosen, budget=gross)
 
     pos_table = pd.DataFrame(
@@ -603,7 +562,6 @@ 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,
@@ -632,139 +590,145 @@ def compute(
         "If leverage isn’t allowed, scale both weights proportionally toward 1.0 to fit your constraints.",
     ])
 
-    progress(1.0, desc="Done.")
+    progress(1.0, desc="Done")
+
     return (
-        gr.update(visible=True),                  # show results 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
+        img, info, pos_table, sugg_table, csv_path,
+        txt_low, txt_med, txt_high,
+        rf_ann, erp_ann, sigma_mkt, sigma_hist, mu_capm, mu_eff_same_sigma, sigma_eff_same_mu,
+        chosen_sigma, chosen_mu
     )
 
 # -------------- UI --------------
-with gr.Blocks(title="Efficient Portfolio Advisor", theme=gr.themes.Soft(primary_hue="violet")) 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."
-    )
+APP_CSS = """
+:root {
+  --accent: hsl(340 70% 50%);
+  --radius: 14px;
+}
+* { font-family: ui-sans-serif, system-ui, -apple-system, "Inter", Segoe UI, Roboto, "Helvetica Neue", Arial, "Noto Sans", sans-serif; }
+.gr-button { border-radius: var(--radius); }
+.gr-textbox, .gr-dropdown, .gr-slider, .gr-number, .gr-dataframe { border-radius: var(--radius); }
+"""
+
+with gr.Blocks(title="Efficient Portfolio Advisor", css=APP_CSS) as demo:
+    gr.Markdown("## Efficient Portfolio Advisor")
+
+    # States to absorb extra returns
+    s1 = gr.State(); s2 = gr.State(); s3 = gr.State(); s4 = gr.State(); s5 = gr.State()
+    s6 = gr.State(); s7 = gr.State(); s8 = gr.State(); s9 = gr.State()
+
+    with gr.Row():
+        with gr.Column(scale=1) as left_col:
+            # --- Input column (full-width before compute) ---
+            q = gr.Textbox(label="Search symbol")
+            search_btn = gr.Button("Search")
+            matches = gr.Dropdown(choices=[], label="Matches", value=None, info="")
+            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")
+            )
 
-    # Single column inputs before compute
-    with gr.Column():
-        # --- 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")
+
+            # Compute button (shows progress bar while compute runs)
+            run_btn = gr.Button("Compute (build dataset & suggest)")
+
+            # Suggestions (hidden until first compute)
+            sugg_hdr = gr.Markdown("### Suggestions", visible=False)
+            with gr.Row(visible=False) as sugg_btn_row:
+                btn_low = gr.Button("Show Low")
+                btn_med = gr.Button("Show Medium")
+                btn_high = gr.Button("Show High")
+            low_txt  = gr.Markdown(visible=False)
+            med_txt  = gr.Markdown(visible=False)
+            high_txt = gr.Markdown(visible=False)
+
+        # --- Output column (hidden until first compute) ---
+        with gr.Column(scale=1, visible=False) as right_col:
+            plot = gr.Image(label="Capital Market Line (CAPM)", type="pil")
+            summary = gr.Markdown(label="Inputs & Results")
+            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)
 
-        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")
-
-    # Results section appears only after compute, as two columns
-    results_group = gr.Group(visible=False)
-    with results_group:
-        md_sugg = gr.Markdown("### Suggestions", visible=False)
-        with gr.Row():
-            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)
-                with gr.Row():
-                    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):
-                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
+    # --- Wiring ---
     search_btn.click(fn=search_tickers_cb, inputs=q, outputs=matches)
     add_btn.click(fn=add_symbol_table_only, inputs=[matches, table], outputs=table)
     table.change(fn=lock_ticker_column, inputs=table, outputs=table)
-
-    # horizon updates globals silently
     horizon.change(fn=set_horizon, inputs=horizon, outputs=[])
 
-    # compute + reveal UI group first, default to Medium band
-    run_btn.click(
+    # Compute (default band = Medium) -> populate outputs
+    run_compute = run_btn.click(
         fn=compute,
         inputs=[lookback, table, gr.State("Medium")],
         outputs=[
-            results_group,  # newly added to toggle visibility
             plot, summary, positions, sugg_table, dl,
             low_txt, med_txt, high_txt,
-            md_sugg, btn_low, btn_med, btn_high,
-        ]
+            s1, s2, s3, s4, s5, s6, s7, s8, s9
+        ],
+        show_progress=True,
+        scroll_to_output=True,
+    )
+
+    # After compute: reveal right column + suggestions
+    def _reveal():
+        return (
+            gr.update(visible=True),  # right_col
+            gr.update(visible=True),  # sugg_hdr
+            gr.update(visible=True),  # sugg_btn_row
+            gr.update(visible=True),  # low_txt
+            gr.update(visible=True),  # med_txt
+            gr.update(visible=True),  # high_txt
+        )
+
+    run_compute.then(
+        fn=_reveal,
+        inputs=[],
+        outputs=[right_col, sugg_hdr, sugg_btn_row, low_txt, med_txt, high_txt]
     )
 
-    # band buttons recompute picks quickly while keeping everything visible
+    # Band buttons -> recompute quickly, keeping layout visible
+    def _band_low(): return "Low"
+    def _band_med(): return "Medium"
+    def _band_high(): return "High"
+
     btn_low.click(
         fn=compute,
-        inputs=[lookback, table, gr.State("Low")],
-        outputs=[
-            results_group,
-            plot, summary, positions, sugg_table, dl,
-            low_txt, med_txt, high_txt,
-            md_sugg, btn_low, btn_med, btn_high,
-        ]
+        inputs=[lookback, table, gr.State(_band_low())],
+        outputs=[plot, summary, positions, sugg_table, dl, low_txt, med_txt, high_txt, s1, s2, s3, s4, s5, s6, s7, s8, s9],
+        show_progress=True
     )
     btn_med.click(
         fn=compute,
-        inputs=[lookback, table, gr.State("Medium")],
-        outputs=[
-            results_group,
-            plot, summary, positions, sugg_table, dl,
-            low_txt, med_txt, high_txt,
-            md_sugg, btn_low, btn_med, btn_high,
-        ]
+        inputs=[lookback, table, gr.State(_band_med())],
+        outputs=[plot, summary, positions, sugg_table, dl, low_txt, med_txt, high_txt, s1, s2, s3, s4, s5, s6, s7, s8, s9],
+        show_progress=True
     )
     btn_high.click(
         fn=compute,
-        inputs=[lookback, table, gr.State("High")],
-        outputs=[
-            results_group,
-            plot, summary, positions, sugg_table, dl,
-            low_txt, med_txt, high_txt,
-            md_sugg, btn_low, btn_med, btn_high,
-        ]
+        inputs=[lookback, table, gr.State(_band_high())],
+        outputs=[plot, summary, positions, sugg_table, dl, low_txt, med_txt, high_txt, s1, s2, s3, s4, s5, s6, s7, s8, s9],
+        show_progress=True
     )
 
 # initialize risk-free at launch
@@ -772,4 +736,4 @@ RF_CODE = fred_series_for_horizon(HORIZON_YEARS)
 RF_ANN = fetch_fred_yield_annual(RF_CODE)
 
 if __name__ == "__main__":
-    demo.queue().launch(server_name="0.0.0.0", server_port=7860, show_api=False)
+    demo.launch(server_name="0.0.0.0", server_port=7860, show_api=False)