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
)