Create app.py

app.py

@@ -0,0 +1,410 @@
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+import requests
+import os
+
+# -------------------------------------------------------
+# GLOBAL CONFIG
+# -------------------------------------------------------
+API_KEY = os.getenv("FMP_API_KEY")
+st.set_page_config(page_title="Financial Statements", layout="wide")
+
+# Initialize session state for caching
+if 'data_cache' not in st.session_state:
+    st.session_state.data_cache = {}
+
+# -------------------------------------------------------
+# CACHED FETCH FUNCTIONS
+# -------------------------------------------------------
+@st.cache_data
+def fetch_income_statement(symbol: str, period: str, api_key: str) -> pd.DataFrame:
+    url = f"https://financialmodelingprep.com/api/v3/income-statement/{symbol}?period={period}&apikey={api_key}"
+    r = requests.get(url)
+    r.raise_for_status()
+    data = r.json() if r.status_code == 200 else []
+    df = pd.DataFrame(data)
+    if not df.empty and "date" in df.columns:
+        df["date"] = pd.to_datetime(df["date"], errors="coerce")
+        df.sort_values("date", inplace=True)
+    return df
+
+@st.cache_data
+def fetch_balance_sheet(symbol: str, period: str, api_key: str) -> pd.DataFrame:
+    url = f"https://financialmodelingprep.com/api/v3/balance-sheet-statement/{symbol}?period={period}&apikey={api_key}"
+    r = requests.get(url)
+    r.raise_for_status()
+    data = r.json() if r.status_code == 200 else []
+    df = pd.DataFrame(data)
+    if not df.empty and "date" in df.columns:
+        df["date"] = pd.to_datetime(df["date"], errors="coerce")
+        df.sort_values("date", inplace=True)
+    return df
+
+@st.cache_data
+def fetch_cash_flow(symbol: str, period: str, api_key: str) -> pd.DataFrame:
+    url = f"https://financialmodelingprep.com/api/v3/cash-flow-statement/{symbol}?period={period}&apikey={api_key}"
+    r = requests.get(url)
+    r.raise_for_status()
+    data = r.json() if r.status_code == 200 else []
+    df = pd.DataFrame(data)
+    if not df.empty and "date" in df.columns:
+        df["date"] = pd.to_datetime(df["date"], errors="coerce")
+        df.sort_values("date", inplace=True)
+    return df
+
+# -------------------------------------------------------
+# HELPER: CREATE DUAL-AXIS SUBPLOT
+# -------------------------------------------------------
+def create_dual_axis_figure(df: pd.DataFrame, vars_list: list[str], title: str, period: str) -> go.Figure:
+    shift_val = 1 if period == "annual" else 4
+    df_local = df.copy()
+
+    for var in vars_list:
+        if var in df_local.columns:
+            df_local[var + "_yoy"] = (
+                (df_local[var] - df_local[var].shift(shift_val))
+                / df_local[var].shift(shift_val)
+            ) * 100
+        else:
+            df_local[var + "_yoy"] = None
+
+    fig = make_subplots(specs=[[{"secondary_y": True}]])
+    colors = px.colors.qualitative.Plotly
+
+    for idx, var in enumerate(vars_list):
+        color_idx = idx % len(colors)
+        base_color = colors[color_idx]
+        fig.add_trace(
+            go.Scatter(
+                x=df_local["date"],
+                y=df_local[var],
+                name=var,
+                mode="lines+markers",
+                line=dict(width=2, color=base_color),
+                hovertemplate=(f"<b>{var}</b><br>Date: %{{x}}<br>Value: %{{y:.2f}}<extra></extra>"),
+            ),
+            secondary_y=False
+        )
+        yoy_col = var + "_yoy"
+        fig.add_trace(
+            go.Scatter(
+                x=df_local["date"],
+                y=df_local[yoy_col],
+                name=f"{var} YoY (%)",
+                mode="lines+markers",
+                line=dict(width=2, dash="dash", color=base_color),
+                opacity=0.3,
+                hovertemplate=(f"<b>{var} YoY</b><br>Date: %{{x}}<br>Change: %{{y:.2f}}%<extra></extra>"),
+            ),
+            secondary_y=True
+        )
+
+    fig.update_layout(
+        title=title,
+        hovermode="closest",
+        legend=dict(x=0, y=-0.2, orientation="h", tracegroupgap=0),
+    )
+    fig.update_xaxes(title_text="Date")
+    fig.update_yaxes(title_text="Absolute Value", secondary_y=False)
+    fig.update_yaxes(title_text="YoY Change (%)", secondary_y=True)
+    return fig
+
+# -------------------------------------------------------
+# HELPER: ENHANCED INTERPRETATION TEXT
+# -------------------------------------------------------
+def interpret_financials(df: pd.DataFrame, metric_list: list[str], section_title: str, period: str) -> str:
+    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 for analysis."
+
+    df_valid = df[['date'] + existing_cols].dropna(subset=existing_cols, how='all')
+    if df_valid.empty:
+        return f"**{section_title}**: No valid data entries available."
+
+    df_valid = df_valid.sort_values("date")
+    latest_row = df_valid.iloc[-1]
+    latest_date = latest_row['date']
+    shift = 1 if period == "annual" else 4
+    period_type = "Year-over-Year" if period == "annual" else "Quarter-over-Quarter"
+
+    prior_row = df_valid.iloc[-1 - shift] if len(df_valid) > shift else None
+    prior_date = prior_row['date'] if prior_row is not None else None
+
+    values_only = df_valid[existing_cols].astype(float)
+    mean_vals = values_only.mean()
+    min_vals = values_only.min()
+    max_vals = values_only.max()
+    std_vals = values_only.std()
+
+    text = f"### {section_title}\n\n"
+    text += f"**Latest Data ({latest_date.date()}):**  \n"
+    for col in existing_cols:
+        latest_val = latest_row[col]
+        text += f"- **{col.replace('_', ' ').title()}**: {latest_val:,.2f}  \n" if pd.notna(latest_val) else f"- **{col.replace('_', ' ').title()}**: Data unavailable  \n"
+
+    if prior_row is not None:
+        text += f"\n**{period_type} Change (vs. {prior_date.date()}):**  \n"
+        for col in existing_cols:
+            latest_val = latest_row[col]
+            prior_val = prior_row[col]
+            if pd.notna(latest_val) and pd.notna(prior_val) and prior_val != 0:
+                pct_change = ((latest_val - prior_val) / abs(prior_val)) * 100
+                diff = latest_val - prior_val
+                direction = "increased" if diff > 0 else "decreased" if diff < 0 else "unchanged"
+                text += f"- **{col.replace('_', ' ').title()}**: {direction.capitalize()} by {abs(diff):,.2f} ({pct_change:+.1f}%)  \n"
+            else:
+                text += f"- **{col.replace('_', ' ').title()}**: Insufficient data for comparison  \n"
+
+    text += "\n**Historical Trends:**  \n"
+    for col in existing_cols:
+        text += (f"- **{col.replace('_', ' ').title()}**: Mean = {mean_vals[col]:,.2f}, "
+                 f"Min = {min_vals[col]:,.2f}, Max = {max_vals[col]:,.2f}, "
+                 f"Std Dev = {std_vals[col]:,.2f}  \n")
+
+    text += "\n**Investor Insights:**  \n"
+    if section_title == "Revenue & Gross Profit":
+        text += (
+            "- Strong revenue growth paired with expanding gross profit margins signals operational efficiency and market strength.  \n"
+            "- Declining trends may reflect competitive pressures or rising costs, impacting profitability.  \n"
+            "- Volatility in these metrics could indicate cyclical demand or pricing instability.  \n"
+        )
+    elif section_title == "Operating Expenses":
+        text += (
+            "- Rising expenses with stable revenue may erode margins, suggesting inefficiencies or investment in growth.  \n"
+            "- Controlled or declining expenses reflect disciplined cost management.  \n"
+            "- High variability could point to inconsistent operational strategies.  \n"
+        )
+    elif section_title == "Net Income & Operating Income":
+        text += (
+            "- Consistent growth in operating and net income underscores sustainable earnings power.  \n"
+            "- Divergence between operating income and net income may highlight tax or interest burdens.  \n"
+            "- Sharp declines warrant investigation into cost structures or extraordinary items.  \n"
+        )
+    elif section_title == "Earnings Per Share":
+        text += (
+            "- Rising EPS reflects enhanced shareholder value.  \n"
+            "- Stagnant or falling EPS may signal dilution or profitability challenges.  \n"
+            "- Compare diluted vs. basic EPS to assess the impact of potential equity issuance.  \n"
+        )
+    elif section_title == "Assets":
+        text += (
+            "- Growth in total assets, especially liquid ones, indicates balance sheet strength and investment capacity.  \n"
+            "- Declines may suggest asset sales or write-downs, potentially weakening financial flexibility.  \n"
+            "- A balanced asset mix is key to supporting long-term growth.  \n"
+        )
+    elif section_title == "Liabilities":
+        text += (
+            "- Increasing liabilities with stable assets raise leverage concerns.  \n"
+            "- Controlled liability growth supports a stable capital structure.  \n"
+            "- High short-term liabilities relative to cash may pressure liquidity.  \n"
+        )
+    elif section_title == "Stockholders' Equity":
+        text += (
+            "- Rising equity reflects retained earnings growth or capital infusions.  \n"
+            "- Declines may indicate losses or share repurchasing, affecting leverage ratios.  \n"
+            "- Consistent equity growth enhances investor confidence.  \n"
+        )
+    elif section_title == "Operating Activities":
+        text += (
+            "- Strong cash flow from operations indicates robust core business health.  \n"
+            "- Negative or declining trends may reflect working capital issues.  \n"
+            "- High depreciation relative to net income suggests significant non-cash adjustments.  \n"
+        )
+    elif section_title == "Investing Activities":
+        text += (
+            "- Heavy investment in property or equipment signals long-term growth focus but may strain near-term cash.  \n"
+            "- Positive cash from sales/maturities indicates strategic divestitures.  \n"
+            "- Persistent negative flows suggest aggressive expansion.  \n"
+        )
+    elif section_title == "Financing Activities":
+        text += (
+            "- Debt repayment or dividend increases reflect confidence in cash flows.  \n"
+            "- Significant stock repurchasing may signal undervaluation or reduced growth.  \n"
+            "- High financing inflows could indicate reliance on external capital.  \n"
+        )
+    text += "\n*Recommendation*: Cross-reference these insights with industry benchmarks and broader market conditions."
+
+    return text
+
+# -------------------------------------------------------
+# PAGES
+# -------------------------------------------------------
+def page_income_statement(symbol: str, period: str):
+    key = f"income_{symbol}_{period}"
+    if key not in st.session_state.data_cache:
+        st.session_state.data_cache[key] = fetch_income_statement(symbol, period, API_KEY)
+    df = st.session_state.data_cache[key]
+
+    if df.empty:
+        st.error("No income statement data returned. Check symbol or period.")
+        return
+
+    st.success("Income Statement data loaded successfully.")
+    st.write("Charts display absolute values and period-over-period changes.")
+
+    st.subheader("1. Revenue & Gross Profit")
+    rev_vars = ["revenue", "grossProfit"]
+    fig_rev = create_dual_axis_figure(df, rev_vars, "Revenue & Gross Profit", period)
+    st.plotly_chart(fig_rev, use_container_width=True)
+    with st.expander("Interpretation"):
+        st.markdown(interpret_financials(df, rev_vars, "Revenue & Gross Profit", period))
+
+    st.subheader("2. Operating Expenses")
+    op_vars = ["researchAndDevelopmentExpenses", "sellingGeneralAndAdministrativeExpenses", "operatingExpenses"]
+    fig_op = create_dual_axis_figure(df, op_vars, "Operating Expenses", period)
+    st.plotly_chart(fig_op, use_container_width=True)
+    with st.expander("Interpretation"):
+        st.markdown(interpret_financials(df, op_vars, "Operating Expenses", period))
+
+    st.subheader("3. Net Income & Operating Income")
+    net_vars = ["netIncome", "operatingIncome", "incomeBeforeTax"]
+    fig_net = create_dual_axis_figure(df, net_vars, "Net Income & Operating Income", period)
+    st.plotly_chart(fig_net, use_container_width=True)
+    with st.expander("Interpretation"):
+        st.markdown(interpret_financials(df, net_vars, "Net Income & Operating Income", period))
+
+    st.subheader("4. Earnings Per Share")
+    eps_vars = ["eps", "epsdiluted"]
+    fig_eps = create_dual_axis_figure(df, eps_vars, "Earnings Per Share", period)
+    st.plotly_chart(fig_eps, use_container_width=True)
+    with st.expander("Interpretation"):
+        st.markdown(interpret_financials(df, eps_vars, "Earnings Per Share", period))
+        
+    st.subheader("Complete Income Statement Data")
+    with st.expander("Show Complete Data"):
+        st.dataframe(df)
+
+def page_balance_sheet(symbol: str, period: str):
+    key = f"balance_{symbol}_{period}"
+    if key not in st.session_state.data_cache:
+        st.session_state.data_cache[key] = fetch_balance_sheet(symbol, period, API_KEY)
+    df = st.session_state.data_cache[key]
+
+    if df.empty:
+        st.error("No balance sheet data returned. Check symbol or period.")
+        return
+
+    st.success("Balance Sheet data loaded successfully.")
+    st.write("Charts display absolute values and period-over-period changes.")
+
+    st.subheader("1. Assets")
+    asset_vars = ["cashAndShortTermInvestments", "totalCurrentAssets", "totalNonCurrentAssets", "totalAssets"]
+    fig_a = create_dual_axis_figure(df, asset_vars, "Assets", period)
+    st.plotly_chart(fig_a, use_container_width=True)
+    with st.expander("Interpretation"):
+        st.markdown(interpret_financials(df, asset_vars, "Assets", period))
+
+    st.subheader("2. Liabilities")
+    liability_vars = ["totalCurrentLiabilities", "totalNonCurrentLiabilities", "totalLiabilities"]
+    fig_l = create_dual_axis_figure(df, liability_vars, "Liabilities", period)
+    st.plotly_chart(fig_l, use_container_width=True)
+    with st.expander("Interpretation"):
+        st.markdown(interpret_financials(df, liability_vars, "Liabilities", period))
+
+    st.subheader("3. Stockholders' Equity")
+    equity_vars = ["commonStock", "retainedEarnings", "accumulatedOtherComprehensiveIncomeLoss", "totalStockholdersEquity"]
+    fig_e = create_dual_axis_figure(df, equity_vars, "Stockholders' Equity", period)
+    st.plotly_chart(fig_e, use_container_width=True)
+    with st.expander("Interpretation"):
+        st.markdown(interpret_financials(df, equity_vars, "Stockholders' Equity", period))
+        
+    st.subheader("Complete Balance Sheet Data")
+    with st.expander("Show Complete Data"):
+        st.dataframe(df)
+
+def page_cash_flow(symbol: str, period: str):
+    key = f"cash_{symbol}_{period}"
+    if key not in st.session_state.data_cache:
+        st.session_state.data_cache[key] = fetch_cash_flow(symbol, period, API_KEY)
+    df = st.session_state.data_cache[key]
+
+    if df.empty:
+        st.error("No cash flow data returned. Check symbol or period.")
+        return
+
+    st.success("Cash Flow data loaded successfully.")
+    st.write("Charts display absolute values and period-over-period changes.")
+
+    st.subheader("1. Operating Activities")
+    op_vars = ["netIncome", "depreciationAndAmortization", "changeInWorkingCapital", "netCashProvidedByOperatingActivities"]
+    fig_op = create_dual_axis_figure(df, op_vars, "Operating Activities", period)
+    st.plotly_chart(fig_op, use_container_width=True)
+    with st.expander("Interpretation"):
+        st.markdown(interpret_financials(df, op_vars, "Operating Activities", period))
+
+    st.subheader("2. Investing Activities")
+    inv_vars = ["investmentsInPropertyPlantAndEquipment", "purchasesOfInvestments", "salesMaturitiesOfInvestments", "netCashUsedForInvestingActivites"]
+    fig_inv = create_dual_axis_figure(df, inv_vars, "Investing Activities", period)
+    st.plotly_chart(fig_inv, use_container_width=True)
+    with st.expander("Interpretation"):
+        st.markdown(interpret_financials(df, inv_vars, "Investing Activities", period))
+
+    st.subheader("3. Financing Activities")
+    fin_vars = ["debtRepayment", "commonStockRepurchased", "dividendsPaid", "netCashUsedProvidedByFinancingActivities"]
+    fig_fin = create_dual_axis_figure(df, fin_vars, "Financing Activities", period)
+    st.plotly_chart(fig_fin, use_container_width=True)
+    with st.expander("Interpretation"):
+        st.markdown(interpret_financials(df, fin_vars, "Financing Activities", period))
+        
+    st.subheader("Complete Cash Flow Data")
+    with st.expander("Show Complete Data"):
+        st.dataframe(df)
+
+# -------------------------------------------------------
+# MAIN
+# -------------------------------------------------------
+st.title("Financial Statements Analysis")
+st.markdown("""
+This tool presents key financial statements for your review.
+It displays the Income Statement, Balance Sheet, and Cash Flow Statement.
+Charts show absolute numbers on the left and changes over time on the right.
+""")
+
+
+# Sidebar: Navigation and Inputs
+with st.sidebar.expander("Navigation", expanded=True):
+    selected_page = st.radio("Select Page", ["Income Statement", "Balance Sheet", "Cash Flow"], index=0)
+    st.session_state.page = selected_page
+
+with st.sidebar.expander("Inputs", expanded=True):
+    symbol = st.text_input("Symbol or CIK", value="AAPL")
+    period = st.selectbox("Period", options=["annual", "quarter"])
+    run_button = st.button("Run Analysis")
+
+# When run is pressed, update symbol/period and refresh only the active page.
+if run_button:
+    st.session_state.symbol = symbol
+    st.session_state.period = period
+    current_page = st.session_state.page
+    if current_page == "Income Statement":
+        st.session_state.data_cache[f"income_{symbol}_{period}"] = fetch_income_statement(symbol, period, API_KEY)
+    elif current_page == "Balance Sheet":
+        st.session_state.data_cache[f"balance_{symbol}_{period}"] = fetch_balance_sheet(symbol, period, API_KEY)
+    elif current_page == "Cash Flow":
+        st.session_state.data_cache[f"cash_{symbol}_{period}"] = fetch_cash_flow(symbol, period, API_KEY)
+
+# Retrieve the latest inputs from session state.
+symbol = st.session_state.get('symbol', 'AAPL')
+period = st.session_state.get('period', 'annual')
+current_page = st.session_state.get('page', 'Income Statement')
+
+if current_page == "Income Statement":
+    page_income_statement(symbol, period)
+elif current_page == "Balance Sheet":
+    page_balance_sheet(symbol, period)
+elif current_page == "Cash Flow":
+    page_cash_flow(symbol, period)
+
+# Hide default Streamlit style
+st.markdown(
+    """
+    <style>
+    #MainMenu {visibility: hidden;}
+    footer {visibility: hidden;}
+    </style>
+    """,
+    unsafe_allow_html=True
+)