import streamlit as st
import pandas as pd
import plotly.express as px
import requests
import os

# Optional environment key
API_KEY = os.getenv("FMP_API_KEY")

# Page config
st.set_page_config(page_title="Financial Growth Metrics", layout="wide")

# ----------------------------
# Caching function with session_state
# ----------------------------
def fetch_data(url, cache_key):
    """Load data from session_state if cached, else fetch from URL."""
    if cache_key in st.session_state:
        return st.session_state[cache_key]
    try:
        resp = requests.get(url)
        resp.raise_for_status()
        data = resp.json()
        st.session_state[cache_key] = data
        return data
    except requests.exceptions.RequestException:
        st.session_state[cache_key] = []
        return []

# ----------------------------
# Helper for interpretations
# ----------------------------
def interpret_growth_metrics(df, metric_list, section_title):
    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 not available."
    
    df_valid = df[['date'] + existing_cols].dropna(subset=existing_cols, how='all')
    if df_valid.empty:
        return f"**{section_title}**: No valid data entries."
    
    df_valid = df_valid.sort_values("date")
    latest_row = df_valid.iloc[-1]
    latest_date = latest_row['date']
    
    if len(df_valid) > 1:
        prior_row = df_valid.iloc[-2]
        prior_date = prior_row['date']
    else:
        prior_row = None
        prior_date = None

    # Basic stats
    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 += "**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}:** Missing in latest record.\n"
        else:
            text += f"- **{col}:** {latest_val:.2f}\n"

    if prior_row is not None:
        text += f"\n**Comparison with previous record ({prior_date.date()}):**\n"
        for col in existing_cols:
            latest_val = latest_row[col]
            prior_val = prior_row[col]
            if pd.isna(latest_val) or pd.isna(prior_val):
                text += f"- **{col}:** No comparison (missing data).\n"
            else:
                diff = latest_val - prior_val
                if diff > 0:
                    text += f"- **{col}:** Increased by {diff:.2f}.\n"
                elif diff < 0:
                    text += f"- **{col}:** Decreased by {abs(diff):.2f}.\n"
                else:
                    text += f"- **{col}:** No change.\n"

    text += "\n**Historical Summary:**\n"
    for col in existing_cols:
        text += (f"- **{col}:** Mean={mean_vals[col]:.2f}, "
                 f"Min={min_vals[col]:.2f}, Max={max_vals[col]:.2f}, "
                 f"StdDev={std_vals[col]:.2f}\n")

    text += "\n**Final Interpretation:**\n"
    text += "- Track these changes over time.\n"
    return text

# ----------------------------
# Page Functions
# ----------------------------
def show_financial_growth(symbol, period):
    url = f"https://financialmodelingprep.com/api/v3/financial-growth/{symbol}?period={period}&apikey={API_KEY}"
    data = fetch_data(url, f"financial_growth_{symbol}_{period}")
    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("Below are key growth metrics. Expand each section for interpretation.")

    # 1. Profitability
    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"):
        st.markdown(interpret_growth_metrics(df, profitability_vars, "Profitability Metrics"))
    with st.expander("DataFrame"):
        cols = ["date"] + [c for c in profitability_vars if c in df.columns]
        st.dataframe(df[cols])

    # 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"):
        st.markdown(interpret_growth_metrics(df, eps_vars, "EPS Metrics"))
    with st.expander("DataFrame"):
        cols = ["date"] + [c for c in eps_vars if c in df.columns]
        st.dataframe(df[cols])

    # 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"):
        st.markdown(interpret_growth_metrics(df, share_vars, "Share Count Adjustments"))
    with st.expander("DataFrame"):
        cols = ["date"] + [c for c in share_vars if c in df.columns]
        st.dataframe(df[cols])

    # 4. Dividend
    st.subheader("4. Dividend per Share")
    div_vars = ["dividendsperShareGrowth"]
    try:
        fig4 = px.line(df, x="date", y=div_vars, 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"):
        st.markdown(interpret_growth_metrics(df, div_vars, "Dividend Growth"))
    with st.expander("DataFrame"):
        cols = ["date"] + [c for c in div_vars if c in df.columns]
        st.dataframe(df[cols])

    # 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"):
        st.markdown(interpret_growth_metrics(df, cashflow_vars, "Cash Flow Metrics"))
    with st.expander("DataFrame"):
        cols = ["date"] + [c for c in cashflow_vars if c in df.columns]
        st.dataframe(df[cols])

    # 6. Multi-Year Growth (Per Share)
    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:
            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"Could not plot chart for {subgroup}.")
        with st.expander("Interpretation"):
            st.markdown(interpret_growth_metrics(df, vars_list, subgroup))
        with st.expander("DataFrame"):
            cols = ["date"] + [c for c in vars_list if c in df.columns]
            st.dataframe(df[cols])

    # 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"):
        st.markdown(interpret_growth_metrics(df, balance_vars, "Balance Sheet Metrics"))
    with st.expander("DataFrame"):
        cols = ["date"] + [c for c in balance_vars if c in df.columns]
        st.dataframe(df[cols])

    # 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"):
        st.markdown(interpret_growth_metrics(df, expense_vars, "Expense Metrics"))
    with st.expander("DataFrame"):
        cols = ["date"] + [c for c in expense_vars if c in df.columns]
        st.dataframe(df[cols])

def show_balance_sheet_growth(symbol, period):
    url = f"https://financialmodelingprep.com/api/v3/balance-sheet-statement-growth/{symbol}?period={period}&apikey={API_KEY}"
    data = fetch_data(url, f"balance_sheet_growth_{symbol}_{period}")
    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.")

    # 1. Asset Growth
    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"):
        st.markdown(interpret_growth_metrics(df, asset_vars, "Asset Growth"))
    with st.expander("DataFrame"):
        cols = ["date"] + [c for c in asset_vars if c in df.columns]
        st.dataframe(df[cols])

    # 2. Liability Growth
    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"):
        st.markdown(interpret_growth_metrics(df, liability_vars, "Liability Growth"))
    with st.expander("DataFrame"):
        cols = ["date"] + [c for c in liability_vars if c in df.columns]
        st.dataframe(df[cols])

    # 3. Equity Growth
    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"):
        st.markdown(interpret_growth_metrics(df, equity_vars, "Equity Growth"))
    with st.expander("DataFrame"):
        cols = ["date"] + [c for c in equity_vars if c in df.columns]
        st.dataframe(df[cols])

    # 4. Debt Metrics
    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"):
        st.markdown(interpret_growth_metrics(df, debt_vars, "Debt Metrics"))
    with st.expander("DataFrame"):
        cols = ["date"] + [c for c in debt_vars if c in df.columns]
        st.dataframe(df[cols])

def show_income_growth(symbol, period):
    url = f"https://financialmodelingprep.com/api/v3/income-statement-growth/{symbol}?period={period}&apikey={API_KEY}"
    data = fetch_data(url, f"income_growth_{symbol}_{period}")
    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.")

    # 1. 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"):
        st.markdown(interpret_growth_metrics(df, profitability_vars, "Profitability Growth"))
    with st.expander("DataFrame"):
        cols = ["date"] + [c for c in profitability_vars if c in df.columns]
        st.dataframe(df[cols])

    # 2. 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"):
        st.markdown(interpret_growth_metrics(df, expense_vars, "Expense Growth"))
    with st.expander("DataFrame"):
        cols = ["date"] + [c for c in expense_vars if c in df.columns]
        st.dataframe(df[cols])

    # 3. Net and Tax Metrics
    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"):
        st.markdown(interpret_growth_metrics(df, net_tax_vars, "Net Income/Tax Growth"))
    with st.expander("DataFrame"):
        cols = ["date"] + [c for c in net_tax_vars if c in df.columns]
        st.dataframe(df[cols])

    # 4. EPS Growth
    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"):
        st.markdown(interpret_growth_metrics(df, eps_vars, "EPS Growth"))
    with st.expander("DataFrame"):
        cols = ["date"] + [c for c in eps_vars if c in df.columns]
        st.dataframe(df[cols])

def show_cashflow_growth(symbol, period):
    url = f"https://financialmodelingprep.com/api/v3/cash-flow-statement-growth/{symbol}?period={period}&apikey={API_KEY}"
    data = fetch_data(url, f"cashflow_growth_{symbol}_{period}")
    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.")

    # 1. Operating Activities
    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"):
        st.markdown(interpret_growth_metrics(df, operating_vars, "Operating CF Growth"))
    with st.expander("DataFrame"):
        cols = ["date"] + [c for c in operating_vars if c in df.columns]
        st.dataframe(df[cols])

    # 2. Investing Activities
    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"):
        st.markdown(interpret_growth_metrics(df, investing_vars, "Investing CF Growth"))
    with st.expander("DataFrame"):
        cols = ["date"] + [c for c in investing_vars if c in df.columns]
        st.dataframe(df[cols])

    # 3. Financing Activities
    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"):
        st.markdown(interpret_growth_metrics(df, financing_vars, "Financing CF Growth"))
    with st.expander("DataFrame"):
        cols = ["date"] + [c for c in financing_vars if c in df.columns]
        st.dataframe(df[cols])

    # 4. 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"):
        st.markdown(interpret_growth_metrics(df, fcf_vars, "Free Cash Flow Growth"))
    with st.expander("DataFrame"):
        cols = ["date"] + [c for c in fcf_vars if c in df.columns]
        st.dataframe(df[cols])

# ----------------------------
# Main
# ----------------------------
st.title("Financial Growth Metrics")
st.markdown("""
This dashboard shows key financial growth metrics across balance sheet, 
income statement, and cash flow statement. 
Use the sidebar to set inputs, then press **Run Analysis**.
After that, you can switch pages without re-running.
""")

# Sidebar Navigation
st.sidebar.header("Navigation")
page = st.sidebar.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"])

# Here is the run button
run_button = st.sidebar.button("Run Analysis")

# If user hasn't clicked "Run Analysis," do not load data
if not run_button and f"{page.lower().replace(' ', '_')}_{symbol}_{period}" not in st.session_state:
    st.warning("Press 'Run Analysis' to fetch data and see the results.")
else:
    # If user clicked "Run", or data is cached from a previous run, show the selected page
    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 default style
hide_streamlit_style = """
<style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
</style>
"""
st.markdown(hide_streamlit_style, unsafe_allow_html=True)