Update app.py

app.py

@@ -20,13 +20,12 @@ def interpret_growth_metrics(df, metric_list, section_title):
     """
     Generate a dynamic and formatted interpretation.
     It covers recent values, comparisons, summary statistics,
-    and tangible insights specific to the section.
+    and insights for the section.
     """
     existing_cols = [m for m in metric_list if m in df.columns]
     if not existing_cols or df.empty:
-        return f"**{section_title}**: Data is not available."
+        return f"**{section_title}**: Data not available."
 
-    # Drop rows with all NaNs in these columns.
     df_valid = df[['date'] + existing_cols].dropna(subset=existing_cols, how='all')
     if df_valid.empty:
         return f"**{section_title}**: No valid data entries."
@@ -48,14 +47,13 @@ def interpret_growth_metrics(df, metric_list, section_title):
     max_vals = values_only.max()
     std_vals = values_only.std()
 
-    # Build the interpretation text with clear sections.
     text = f"##### {section_title}\n\n"
     text += "**Recent Data:**\n"
     text += f"- **Latest Record Date:** {latest_date.date()}\n"
     for col in existing_cols:
         latest_val = latest_row[col]
         if pd.isna(latest_val):
-            text += f"- **{col}:** Data missing in the latest record.\n"
+            text += f"- **{col}:** Missing in latest record.\n"
         else:
             text += f"- **{col}:** {latest_val:.2f}\n"
 
@@ -65,7 +63,7 @@ def interpret_growth_metrics(df, metric_list, section_title):
             latest_val = latest_row[col]
             prior_val = prior_row[col]
             if pd.isna(latest_val) or pd.isna(prior_val):
-                text += f"- **{col}:** Comparison not possible due to missing data.\n"
+                text += f"- **{col}:** No comparison (missing data).\n"
             else:
                 diff = latest_val - prior_val
                 if diff > 0:
@@ -73,7 +71,7 @@ def interpret_growth_metrics(df, metric_list, section_title):
                 elif diff < 0:
                     text += f"- **{col}:** Decreased by {abs(diff):.2f}.\n"
                 else:
-                    text += f"- **{col}:** Remained the same.\n"
+                    text += f"- **{col}:** No change.\n"
 
     text += "\n**Historical Summary:**\n"
     for col in existing_cols:
@@ -81,205 +79,474 @@ def interpret_growth_metrics(df, metric_list, section_title):
                  f"Min = {min_vals[col]:.2f}, Max = {max_vals[col]:.2f}, "
                  f"Std Dev = {std_vals[col]:.2f}.\n")
 
-    # Section-specific final interpretation.
     text += "\n**Final Interpretation:**\n"
-    if "Profitability" in section_title:
-        text += ("- Rising revenue, gross profit, EBIT, operating income, or net income "
-                 "suggests better market performance and stronger operations. "
-                 "A decline might mean increased costs or reduced sales. "
-                 "Stable figures show consistency in performance.\n")
-    elif "EPS" in section_title:
-        text += ("- Increases in EPS imply higher earnings per share, which may attract investors. "
-                 "A drop can hint at dilution or rising costs. "
-                 "Stable EPS reflects steady earnings.\n")
-    elif "Share Count" in section_title:
-        text += ("- An increase in share count may result from issuing new shares or stock-based compensation, "
-                 "which can dilute value. "
-                 "A decrease may reflect share buybacks, often seen as positive.\n")
-    elif "Dividend" in section_title:
-        text += ("- Growing dividend figures indicate higher cash returns to shareholders "
-                 "and may reflect a strong cash position. "
-                 "Declining dividends can signal caution or cash retention.\n")
-    elif "Cash Flow" in section_title:
-        text += ("- An increase in cash flow points to efficient operations and solid liquidity. "
-                 "A decrease may reveal issues with cash generation or higher working capital needs. "
-                 "Steady cash flow suggests stability.\n")
-    elif any(keyword in section_title for keyword in ["Revenue", "Net Income", "Operating Cash Flow", "Shareholders’ Equity"]):
-        text += ("- Sustained long-term growth is a positive sign of company strength. "
-                 "Declines over several years may indicate underlying challenges. "
-                 "Stable trends show reliable performance over time.\n")
-    elif "Balance Sheet" in section_title:
-        text += ("- Increases in assets and book value per share typically indicate financial strength. "
-                 "However, rising receivables, inventory, or debt should be analyzed for efficiency issues. "
-                 "Balance sheet trends require careful review.\n")
-    elif "Expense" in section_title:
-        text += ("- Higher R&D expenses may point to investments in future growth, while rising SGA expenses "
-                 "could indicate increased operating costs. "
-                 "Stable expenses are a sign of controlled costs.\n")
-    else:
-        text += ("- Review these trends with your own analysis to understand what the changes mean.\n")
-
+    text += "- Track these changes over time to see if they align with expectations.\n"
     return text
 
 # ----------------------------
-# App header and description
-# ----------------------------
-st.title("Key Financial Growth")
-st.markdown("""
-This dashboard shows key financial growth metrics. 
-Metrics are grouped by category for side-by-side comparison. 
-Use the sidebar to set inputs and click **Run Analysis**.
-""")
-
-# ----------------------------
-# Sidebar inputs
-# ----------------------------
-st.sidebar.header("Inputs")
-with st.sidebar.expander("Settings", expanded=True):
-    symbol = st.text_input("Company Symbol", value="AAPL", help="Enter the company's stock symbol (e.g., AAPL).")
-    period = st.selectbox("Period", options=["annual", "quarter"], help="Choose to view quarterly or annual data.")
-
-run_button = st.sidebar.button("Run Analysis")
-
-# ----------------------------
-# Main Analysis and Visualization
+# Pages
 # ----------------------------
-if run_button:
+def show_financial_growth(symbol, period):
+    """
+    Original page.
+    Calls /financial-growth/ endpoint.
+    Uses the existing sections of analysis.
+    """
     url = f"https://financialmodelingprep.com/api/v3/financial-growth/{symbol}?period={period}&apikey={API_KEY}"
     try:
         response = requests.get(url)
         response.raise_for_status()
         data = response.json()
-
         if not data:
-            st.error("No data returned. Check the symbol or period.")
-        else:
-            df = pd.DataFrame(data)
-            if "date" in df.columns:
-                df['date'] = pd.to_datetime(df['date'], errors='coerce')
-                df.sort_values("date", inplace=True)
-            st.success("Data loaded successfully!")
-            st.write("Each section below shows a chart and its interpretation.")
-
-            # 1. Income Statement – Profitability Metrics
-            st.subheader("1. Income Statement – Profitability Metrics")
-            profitability_vars = ["revenueGrowth", "grossProfitGrowth", "ebitgrowth", "operatingIncomeGrowth", "netIncomeGrowth"]
-            try:
-                fig1 = px.line(df, x="date", y=profitability_vars, title="Profitability Metrics")
-                fig1.update_layout(xaxis_title="Date", yaxis_title="Growth Rate", legend_title="Metric")
-                st.plotly_chart(fig1, use_container_width=True)
-            except Exception:
-                st.error("Error generating the Profitability Metrics chart.")
-            with st.expander("Interpretation"):
-                interp_text = interpret_growth_metrics(df, profitability_vars, "Profitability Metrics")
-                st.markdown(interp_text)
+            st.error("No data returned. Check symbol or period.")
+            return
+        df = pd.DataFrame(data)
+        if "date" in df.columns:
+            df["date"] = pd.to_datetime(df["date"], errors="coerce")
+            df.sort_values("date", inplace=True)
 
-            # 2. Earnings Per Share Metrics
-            st.subheader("2. Earnings Per Share Metrics")
-            eps_vars = ["epsgrowth", "epsdilutedGrowth"]
-            try:
-                fig2 = px.line(df, x="date", y=eps_vars, title="EPS Metrics")
-                fig2.update_layout(xaxis_title="Date", yaxis_title="Growth Rate", legend_title="Metric")
-                st.plotly_chart(fig2, use_container_width=True)
-            except Exception:
-                st.error("Error generating the EPS Metrics chart.")
-            with st.expander("Interpretation"):
-                interp_text = interpret_growth_metrics(df, eps_vars, "EPS Metrics")
-                st.markdown(interp_text)
+        st.success("Data loaded successfully.")
+        st.write("Below are key growth metrics. Expand each section for interpretation.")
 
-            # 3. Share Count Adjustments
-            st.subheader("3. Share Count Adjustments")
-            share_vars = ["weightedAverageSharesGrowth", "weightedAverageSharesDilutedGrowth"]
-            try:
-                fig3 = px.line(df, x="date", y=share_vars, title="Share Count Adjustments")
-                fig3.update_layout(xaxis_title="Date", yaxis_title="Growth Rate", legend_title="Metric")
-                st.plotly_chart(fig3, use_container_width=True)
-            except Exception:
-                st.error("Error generating the Share Count Adjustments chart.")
-            with st.expander("Interpretation"):
-                interp_text = interpret_growth_metrics(df, share_vars, "Share Count Adjustments")
-                st.markdown(interp_text)
+        # 1. Income Statement – Profitability Metrics
+        st.subheader("1. Profitability")
+        profitability_vars = [
+            "revenueGrowth", "grossProfitGrowth", "ebitgrowth", 
+            "operatingIncomeGrowth", "netIncomeGrowth"
+        ]
+        try:
+            fig1 = px.line(df, x="date", y=profitability_vars, title="Profitability Metrics")
+            fig1.update_layout(xaxis_title="Date", yaxis_title="Growth Rate", legend_title="Metric")
+            st.plotly_chart(fig1, use_container_width=True)
+        except Exception:
+            st.error("Could not plot Profitability Metrics.")
+        with st.expander("Interpretation"):
+            interp_text = interpret_growth_metrics(df, profitability_vars, "Profitability Metrics")
+            st.markdown(interp_text)
 
-            # 4. Dividend Metric
-            st.subheader("4. Dividend Metric")
-            try:
-                fig4 = px.line(df, x="date", y=["dividendsperShareGrowth"], title="Dividend per Share Growth")
-                fig4.update_layout(xaxis_title="Date", yaxis_title="Growth Rate", legend_title="Metric")
-                st.plotly_chart(fig4, use_container_width=True)
-            except Exception:
-                st.error("Error generating the Dividend Metric chart.")
-            with st.expander("Interpretation"):
-                interp_text = interpret_growth_metrics(df, ["dividendsperShareGrowth"], "Dividend Growth")
-                st.markdown(interp_text)
+        # 2. EPS
+        st.subheader("2. Earnings Per Share")
+        eps_vars = ["epsgrowth", "epsdilutedGrowth"]
+        try:
+            fig2 = px.line(df, x="date", y=eps_vars, title="EPS Metrics")
+            fig2.update_layout(xaxis_title="Date", yaxis_title="Growth Rate", legend_title="Metric")
+            st.plotly_chart(fig2, use_container_width=True)
+        except Exception:
+            st.error("Could not plot EPS Metrics.")
+        with st.expander("Interpretation"):
+            interp_text = interpret_growth_metrics(df, eps_vars, "EPS Metrics")
+            st.markdown(interp_text)
 
-            # 5. Cash Flow Metrics
-            st.subheader("5. Cash Flow Metrics")
-            cashflow_vars = ["operatingCashFlowGrowth", "freeCashFlowGrowth"]
-            try:
-                fig5 = px.line(df, x="date", y=cashflow_vars, title="Cash Flow Metrics")
-                fig5.update_layout(xaxis_title="Date", yaxis_title="Growth Rate", legend_title="Metric")
-                st.plotly_chart(fig5, use_container_width=True)
-            except Exception:
-                st.error("Error generating the Cash Flow Metrics chart.")
-            with st.expander("Interpretation"):
-                interp_text = interpret_growth_metrics(df, cashflow_vars, "Cash Flow Metrics")
-                st.markdown(interp_text)
+        # 3. Share Count
+        st.subheader("3. Share Count")
+        share_vars = ["weightedAverageSharesGrowth", "weightedAverageSharesDilutedGrowth"]
+        try:
+            fig3 = px.line(df, x="date", y=share_vars, title="Share Count Adjustments")
+            fig3.update_layout(xaxis_title="Date", yaxis_title="Growth Rate", legend_title="Metric")
+            st.plotly_chart(fig3, use_container_width=True)
+        except Exception:
+            st.error("Could not plot Share Count.")
+        with st.expander("Interpretation"):
+            interp_text = interpret_growth_metrics(df, share_vars, "Share Count Adjustments")
+            st.markdown(interp_text)
 
-            # 6. Multi-Year Growth Metrics (Per Share Basis)
-            st.subheader("6. Multi-Year Growth Metrics (Per Share Basis)")
-            multi_year_groups = {
-                "Revenue Growth": ["tenYRevenueGrowthPerShare", "fiveYRevenueGrowthPerShare", "threeYRevenueGrowthPerShare"],
-                "Operating Cash Flow Growth": ["tenYOperatingCFGrowthPerShare", "fiveYOperatingCFGrowthPerShare", "threeYOperatingCFGrowthPerShare"],
-                "Net Income Growth": ["tenYNetIncomeGrowthPerShare", "fiveYNetIncomeGrowthPerShare", "threeYNetIncomeGrowthPerShare"],
-                "Shareholders’ Equity Growth": ["tenYShareholdersEquityGrowthPerShare", "fiveYShareholdersEquityGrowthPerShare", "threeYShareholdersEquityGrowthPerShare"],
-                "Dividend per Share Growth": ["tenYDividendperShareGrowthPerShare", "fiveYDividendperShareGrowthPerShare", "threeYDividendperShareGrowthPerShare"]
-            }
-            for subgroup, vars_list in multi_year_groups.items():
-                st.markdown(f"**{subgroup}**")
-                try:
-                    fig = px.line(df, x="date", y=vars_list, title=subgroup)
-                    fig.update_layout(xaxis_title="Date", yaxis_title="Growth Rate", legend_title="Metric")
-                    st.plotly_chart(fig, use_container_width=True)
-                except Exception:
-                    st.error(f"Error generating chart for {subgroup}.")
-                with st.expander("Interpretation"):
-                    interp_text = interpret_growth_metrics(df, vars_list, subgroup)
-                    st.markdown(interp_text)
-
-            # 7. Balance Sheet Metrics
-            st.subheader("7. Balance Sheet Metrics")
-            balance_vars = ["receivablesGrowth", "inventoryGrowth", "assetGrowth", "bookValueperShareGrowth", "debtGrowth"]
-            try:
-                fig7 = px.line(df, x="date", y=balance_vars, title="Balance Sheet Metrics")
-                fig7.update_layout(xaxis_title="Date", yaxis_title="Growth Rate", legend_title="Metric")
-                st.plotly_chart(fig7, use_container_width=True)
-            except Exception:
-                st.error("Error generating the Balance Sheet Metrics chart.")
-            with st.expander("Interpretation"):
-                interp_text = interpret_growth_metrics(df, balance_vars, "Balance Sheet Metrics")
-                st.markdown(interp_text)
+        # 4. Dividend
+        st.subheader("4. Dividend per Share")
+        try:
+            fig4 = px.line(df, x="date", y=["dividendsperShareGrowth"], title="Dividend Growth")
+            fig4.update_layout(xaxis_title="Date", yaxis_title="Growth Rate", legend_title="Metric")
+            st.plotly_chart(fig4, use_container_width=True)
+        except Exception:
+            st.error("Could not plot Dividend Growth.")
+        with st.expander("Interpretation"):
+            interp_text = interpret_growth_metrics(df, ["dividendsperShareGrowth"], "Dividend Growth")
+            st.markdown(interp_text)
+
+        # 5. Cash Flow
+        st.subheader("5. Cash Flow")
+        cashflow_vars = ["operatingCashFlowGrowth", "freeCashFlowGrowth"]
+        try:
+            fig5 = px.line(df, x="date", y=cashflow_vars, title="Cash Flow Metrics")
+            fig5.update_layout(xaxis_title="Date", yaxis_title="Growth Rate", legend_title="Metric")
+            st.plotly_chart(fig5, use_container_width=True)
+        except Exception:
+            st.error("Could not plot Cash Flow Metrics.")
+        with st.expander("Interpretation"):
+            interp_text = interpret_growth_metrics(df, cashflow_vars, "Cash Flow Metrics")
+            st.markdown(interp_text)
 
-            # 8. Expense Metrics
-            st.subheader("8. Expense Metrics")
-            expense_vars = ["rdexpenseGrowth", "sgaexpensesGrowth"]
+        # 6. Multi-Year Growth
+        st.subheader("6. Multi-Year Growth (Per Share)")
+        multi_year_groups = {
+            "Revenue Growth": [
+                "tenYRevenueGrowthPerShare", "fiveYRevenueGrowthPerShare", 
+                "threeYRevenueGrowthPerShare"
+            ],
+            "Operating Cash Flow Growth": [
+                "tenYOperatingCFGrowthPerShare", "fiveYOperatingCFGrowthPerShare", 
+                "threeYOperatingCFGrowthPerShare"
+            ],
+            "Net Income Growth": [
+                "tenYNetIncomeGrowthPerShare", "fiveYNetIncomeGrowthPerShare", 
+                "threeYNetIncomeGrowthPerShare"
+            ],
+            "Shareholders’ Equity Growth": [
+                "tenYShareholdersEquityGrowthPerShare", "fiveYShareholdersEquityGrowthPerShare", 
+                "threeYShareholdersEquityGrowthPerShare"
+            ],
+            "Dividend per Share Growth": [
+                "tenYDividendperShareGrowthPerShare", "fiveYDividendperShareGrowthPerShare", 
+                "threeYDividendperShareGrowthPerShare"
+            ]
+        }
+        for subgroup, vars_list in multi_year_groups.items():
+            st.markdown(f"**{subgroup}**")
             try:
-                fig8 = px.line(df, x="date", y=expense_vars, title="Expense Metrics")
-                fig8.update_layout(xaxis_title="Date", yaxis_title="Growth Rate", legend_title="Metric")
-                st.plotly_chart(fig8, use_container_width=True)
+                fig = px.line(df, x="date", y=vars_list, title=subgroup)
+                fig.update_layout(xaxis_title="Date", yaxis_title="Growth Rate", legend_title="Metric")
+                st.plotly_chart(fig, use_container_width=True)
             except Exception:
-                st.error("Error generating the Expense Metrics chart.")
+                st.error(f"Error generating chart for {subgroup}.")
             with st.expander("Interpretation"):
-                interp_text = interpret_growth_metrics(df, expense_vars, "Expense Metrics")
+                interp_text = interpret_growth_metrics(df, vars_list, subgroup)
                 st.markdown(interp_text)
 
+        # 7. Balance Sheet
+        st.subheader("7. Balance Sheet")
+        balance_vars = ["receivablesGrowth", "inventoryGrowth", "assetGrowth", 
+                        "bookValueperShareGrowth", "debtGrowth"]
+        try:
+            fig7 = px.line(df, x="date", y=balance_vars, title="Balance Sheet Metrics")
+            fig7.update_layout(xaxis_title="Date", yaxis_title="Growth Rate", legend_title="Metric")
+            st.plotly_chart(fig7, use_container_width=True)
+        except Exception:
+            st.error("Could not plot Balance Sheet Metrics.")
+        with st.expander("Interpretation"):
+            interp_text = interpret_growth_metrics(df, balance_vars, "Balance Sheet Metrics")
+            st.markdown(interp_text)
+
+        # 8. Expense
+        st.subheader("8. Expense")
+        expense_vars = ["rdexpenseGrowth", "sgaexpensesGrowth"]
+        try:
+            fig8 = px.line(df, x="date", y=expense_vars, title="Expense Metrics")
+            fig8.update_layout(xaxis_title="Date", yaxis_title="Growth Rate", legend_title="Metric")
+            st.plotly_chart(fig8, use_container_width=True)
+        except Exception:
+            st.error("Could not plot Expense Metrics.")
+        with st.expander("Interpretation"):
+            interp_text = interpret_growth_metrics(df, expense_vars, "Expense Metrics")
+            st.markdown(interp_text)
+
+    except requests.exceptions.RequestException:
+        st.error("Error fetching data. Check connection and inputs.")
+
+def show_balance_sheet_growth(symbol, period):
+    """
+    Page for balance sheet growth.
+    Calls /balance-sheet-statement-growth/ endpoint.
+    Displays asset, liability, equity, and debt growth.
+    """
+    url = f"https://financialmodelingprep.com/api/v3/balance-sheet-statement-growth/{symbol}?period={period}&apikey={API_KEY}"
+    try:
+        r = requests.get(url)
+        r.raise_for_status()
+        data = r.json()
+        if not data:
+            st.error("No data returned. Check symbol or period.")
+            return
+        df = pd.DataFrame(data)
+        if "date" in df.columns:
+            df["date"] = pd.to_datetime(df["date"], errors="coerce")
+            df.sort_values("date", inplace=True)
+
+        st.success("Data loaded successfully.")
+        st.write("These sections show balance sheet growth metrics.")
+
+        # Assets
+        st.subheader("1. Asset Growth")
+        asset_vars = [
+            "growthCashAndCashEquivalents", "growthShortTermInvestments",
+            "growthCashAndShortTermInvestments", "growthNetReceivables",
+            "growthInventory", "growthTotalCurrentAssets",
+            "growthPropertyPlantEquipmentNet", "growthTotalNonCurrentAssets",
+            "growthTotalAssets"
+        ]
+        try:
+            fig_a = px.line(df, x="date", y=asset_vars, title="Asset Growth")
+            fig_a.update_layout(xaxis_title="Date", yaxis_title="Growth", legend_title="Metric")
+            st.plotly_chart(fig_a, use_container_width=True)
+        except Exception:
+            st.error("Could not plot Asset Growth.")
+        with st.expander("Interpretation"):
+            interp_text = interpret_growth_metrics(df, asset_vars, "Asset Growth")
+            st.markdown(interp_text)
+
+        # Liabilities
+        st.subheader("2. Liability Growth")
+        liability_vars = [
+            "growthAccountPayables", "growthShortTermDebt", "growthTaxPayables",
+            "growthTotalCurrentLiabilities", "growthLongTermDebt", 
+            "growthTotalNonCurrentLiabilities", "growthTotalLiabilities"
+        ]
+        try:
+            fig_l = px.line(df, x="date", y=liability_vars, title="Liability Growth")
+            fig_l.update_layout(xaxis_title="Date", yaxis_title="Growth", legend_title="Metric")
+            st.plotly_chart(fig_l, use_container_width=True)
+        except Exception:
+            st.error("Could not plot Liability Growth.")
+        with st.expander("Interpretation"):
+            interp_text = interpret_growth_metrics(df, liability_vars, "Liability Growth")
+            st.markdown(interp_text)
+
+        # Equity
+        st.subheader("3. Equity Growth")
+        equity_vars = [
+            "growthCommonStock", "growthRetainedEarnings",
+            "growthAccumulatedOtherComprehensiveIncomeLoss",
+            "growthTotalStockholdersEquity"
+        ]
+        try:
+            fig_e = px.line(df, x="date", y=equity_vars, title="Equity Growth")
+            fig_e.update_layout(xaxis_title="Date", yaxis_title="Growth", legend_title="Metric")
+            st.plotly_chart(fig_e, use_container_width=True)
+        except Exception:
+            st.error("Could not plot Equity Growth.")
+        with st.expander("Interpretation"):
+            interp_text = interpret_growth_metrics(df, equity_vars, "Equity Growth")
+            st.markdown(interp_text)
+
+        # Debt
+        st.subheader("4. Debt Metrics")
+        debt_vars = [
+            "growthTotalDebt", "growthNetDebt"
+        ]
+        try:
+            fig_d = px.line(df, x="date", y=debt_vars, title="Debt Metrics")
+            fig_d.update_layout(xaxis_title="Date", yaxis_title="Growth", legend_title="Metric")
+            st.plotly_chart(fig_d, use_container_width=True)
+        except Exception:
+            st.error("Could not plot Debt Metrics.")
+        with st.expander("Interpretation"):
+            interp_text = interpret_growth_metrics(df, debt_vars, "Debt Metrics")
+            st.markdown(interp_text)
+
+    except requests.exceptions.RequestException:
+        st.error("Error fetching data. Check connection and inputs.")
+
+def show_income_growth(symbol, period):
+    """
+    Page for income statement growth.
+    Calls /income-statement-growth/ endpoint.
+    Groups items into profitability, expenses, net, etc.
+    """
+    url = f"https://financialmodelingprep.com/api/v3/income-statement-growth/{symbol}?period={period}&apikey={API_KEY}"
+    try:
+        r = requests.get(url)
+        r.raise_for_status()
+        data = r.json()
+        if not data:
+            st.error("No data returned. Check symbol or period.")
+            return
+        df = pd.DataFrame(data)
+        if "date" in df.columns:
+            df["date"] = pd.to_datetime(df["date"], errors="coerce")
+            df.sort_values("date", inplace=True)
+
+        st.success("Data loaded successfully.")
+        st.write("These sections show income statement growth metrics.")
+
+        # Profitability
+        st.subheader("1. Profitability")
+        profitability_vars = [
+            "growthRevenue", "growthGrossProfit", "growthOperatingIncome",
+            "growthNetIncome", "growthEBITDA"
+        ]
+        try:
+            fig_p = px.line(df, x="date", y=profitability_vars, title="Profitability Growth")
+            fig_p.update_layout(xaxis_title="Date", yaxis_title="Growth", legend_title="Metric")
+            st.plotly_chart(fig_p, use_container_width=True)
+        except Exception:
+            st.error("Could not plot Profitability Growth.")
+        with st.expander("Interpretation"):
+            interp_text = interpret_growth_metrics(df, profitability_vars, "Profitability Growth")
+            st.markdown(interp_text)
+
+        # Expense
+        st.subheader("2. Expense")
+        expense_vars = [
+            "growthCostOfRevenue", "growthOperatingExpenses",
+            "growthResearchAndDevelopmentExpenses", "growthSellingAndMarketingExpenses"
+        ]
+        try:
+            fig_e = px.line(df, x="date", y=expense_vars, title="Expense Growth")
+            fig_e.update_layout(xaxis_title="Date", yaxis_title="Growth", legend_title="Metric")
+            st.plotly_chart(fig_e, use_container_width=True)
+        except Exception:
+            st.error("Could not plot Expense Growth.")
+        with st.expander("Interpretation"):
+            interp_text = interpret_growth_metrics(df, expense_vars, "Expense Growth")
+            st.markdown(interp_text)
+
+        # Net and Tax
+        st.subheader("3. Net and Tax Metrics")
+        net_tax_vars = [
+            "growthIncomeBeforeTax", "growthIncomeTaxExpense", 
+            "growthNetIncome", "growthNetIncomeRatio"
+        ]
+        try:
+            fig_n = px.line(df, x="date", y=net_tax_vars, title="Net Income and Tax Growth")
+            fig_n.update_layout(xaxis_title="Date", yaxis_title="Growth", legend_title="Metric")
+            st.plotly_chart(fig_n, use_container_width=True)
+        except Exception:
+            st.error("Could not plot Net Income/Tax Growth.")
+        with st.expander("Interpretation"):
+            interp_text = interpret_growth_metrics(df, net_tax_vars, "Net Income/Tax Growth")
+            st.markdown(interp_text)
+
+        # EPS
+        st.subheader("4. EPS Growth")
+        eps_vars = ["growthEPS", "growthEPSDiluted"]
+        try:
+            fig_eps = px.line(df, x="date", y=eps_vars, title="EPS Growth")
+            fig_eps.update_layout(xaxis_title="Date", yaxis_title="Growth", legend_title="Metric")
+            st.plotly_chart(fig_eps, use_container_width=True)
+        except Exception:
+            st.error("Could not plot EPS Growth.")
+        with st.expander("Interpretation"):
+            interp_text = interpret_growth_metrics(df, eps_vars, "EPS Growth")
+            st.markdown(interp_text)
+
     except requests.exceptions.RequestException:
-        st.error("Error fetching data. Check your connection and inputs.")
+        st.error("Error fetching data. Check connection and inputs.")
 
+def show_cashflow_growth(symbol, period):
+    """
+    Page for cash flow statement growth.
+    Calls /cash-flow-statement-growth/ endpoint.
+    Groups items into operating, investing, financing metrics.
+    """
+    url = f"https://financialmodelingprep.com/api/v3/cash-flow-statement-growth/{symbol}?period={period}&apikey={API_KEY}"
+    try:
+        r = requests.get(url)
+        r.raise_for_status()
+        data = r.json()
+        if not data:
+            st.error("No data returned. Check symbol or period.")
+            return
+        df = pd.DataFrame(data)
+        if "date" in df.columns:
+            df["date"] = pd.to_datetime(df["date"], errors="coerce")
+            df.sort_values("date", inplace=True)
+
+        st.success("Data loaded successfully.")
+        st.write("These sections show cash flow statement growth metrics.")
+
+        # Operating
+        st.subheader("1. Operating Activities")
+        operating_vars = [
+            "growthNetIncome", "growthDepreciationAndAmortization",
+            "growthChangeInWorkingCapital", "growthNetCashProvidedByOperatingActivites",
+            "growthOperatingCashFlow"
+        ]
+        try:
+            fig_op = px.line(df, x="date", y=operating_vars, title="Operating Cash Flow Growth")
+            fig_op.update_layout(xaxis_title="Date", yaxis_title="Growth", legend_title="Metric")
+            st.plotly_chart(fig_op, use_container_width=True)
+        except Exception:
+            st.error("Could not plot Operating CF Growth.")
+        with st.expander("Interpretation"):
+            interp_text = interpret_growth_metrics(df, operating_vars, "Operating CF Growth")
+            st.markdown(interp_text)
+
+        # Investing
+        st.subheader("2. Investing Activities")
+        investing_vars = [
+            "growthInvestmentsInPropertyPlantAndEquipment", "growthAcquisitionsNet",
+            "growthPurchasesOfInvestments", "growthSalesMaturitiesOfInvestments",
+            "growthNetCashUsedForInvestingActivites"
+        ]
+        try:
+            fig_inv = px.line(df, x="date", y=investing_vars, title="Investing Cash Flow Growth")
+            fig_inv.update_layout(xaxis_title="Date", yaxis_title="Growth", legend_title="Metric")
+            st.plotly_chart(fig_inv, use_container_width=True)
+        except Exception:
+            st.error("Could not plot Investing CF Growth.")
+        with st.expander("Interpretation"):
+            interp_text = interpret_growth_metrics(df, investing_vars, "Investing CF Growth")
+            st.markdown(interp_text)
+
+        # Financing
+        st.subheader("3. Financing Activities")
+        financing_vars = [
+            "growthDebtRepayment", "growthCommonStockRepurchased",
+            "growthDividendsPaid", "growthNetCashUsedProvidedByFinancingActivities"
+        ]
+        try:
+            fig_fin = px.line(df, x="date", y=financing_vars, title="Financing Cash Flow Growth")
+            fig_fin.update_layout(xaxis_title="Date", yaxis_title="Growth", legend_title="Metric")
+            st.plotly_chart(fig_fin, use_container_width=True)
+        except Exception:
+            st.error("Could not plot Financing CF Growth.")
+        with st.expander("Interpretation"):
+            interp_text = interpret_growth_metrics(df, financing_vars, "Financing CF Growth")
+            st.markdown(interp_text)
+
+        # Free Cash Flow
+        st.subheader("4. Free Cash Flow")
+        fcf_vars = ["growthFreeCashFlow"]
+        try:
+            fig_fcf = px.line(df, x="date", y=fcf_vars, title="Free Cash Flow Growth")
+            fig_fcf.update_layout(xaxis_title="Date", yaxis_title="Growth", legend_title="Metric")
+            st.plotly_chart(fig_fcf, use_container_width=True)
+        except Exception:
+            st.error("Could not plot Free Cash Flow Growth.")
+        with st.expander("Interpretation"):
+            interp_text = interpret_growth_metrics(df, fcf_vars, "Free Cash Flow Growth")
+            st.markdown(interp_text)
+
+    except requests.exceptions.RequestException:
+        st.error("Error fetching data. Check connection and inputs.")
+
+# ----------------------------
+# Main
+# ----------------------------
+st.title("Company Growth Rates with Multi-Page Analysis")
+st.markdown("""
+This app provides four pages:
+1. Financial Growth  
+2. Balance Sheet Growth  
+3. Income Growth  
+4. Cash Flow Growth  
+
+Use the radio button below to switch between them.  
+Enter your symbol and period in the sidebar, then click **Run**.
+""")
+
+page = st.radio("Select Page", 
+                ["Financial Growth", "Balance Sheet Growth", "Income Growth", "Cash Flow Growth"],
+                index=0)
+
+st.sidebar.header("Inputs")
+symbol = st.sidebar.text_input("Company Symbol", value="AAPL")
+period = st.sidebar.selectbox("Period", options=["annual", "quarter"])
+run_button = st.sidebar.button("Run")
+
+if run_button:
+    if page == "Financial Growth":
+        show_financial_growth(symbol, period)
+    elif page == "Balance Sheet Growth":
+        show_balance_sheet_growth(symbol, period)
+    elif page == "Income Growth":
+        show_income_growth(symbol, period)
+    elif page == "Cash Flow Growth":
+        show_cashflow_growth(symbol, period)
 
+# Hide Streamlit style
 hide_streamlit_style = """
 <style>
 #MainMenu {visibility: hidden;}
 footer {visibility: hidden;}
 </style>
 """
-st.markdown(hide_streamlit_style, unsafe_allow_html=True)
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)