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

# Update the default color sequence for the plotly_dark template to green.
pio.templates["plotly_dark"].layout.colorway = ["#00cc44"]

# Global API key and constant limit (backend only)
API_KEY = os.getenv("FMP_API_KEY")
BASE_URL = "https://financialmodelingprep.com/api/v3/stock-screener"
LIMIT = 1000

# Set wide layout and page title
st.set_page_config(layout="wide", page_title="Stock Screener")

# App explanation in main area
st.title("Stock Screener")

st.write(
    "A research tool for filtering stocks by price, volume, market cap, sector, and other fundamentals. "
    "Useful for identifying sets of securities based on specific criteria."
)

st.info(
    "Use the sidebar to set filters. Click **Run Analysis** to retrieve results and display charts. "
    "All filters are optional. If no results appear, try loosening the constraints."
)

# Sidebar inputs
st.sidebar.header("Filters")

# Numeric Filters
with st.sidebar.expander("Numeric Filters", expanded=True):
    market_cap_min = st.number_input(
        "Market Cap More Than",
        value=1000000000,
        help="Minimum market capitalization."
    )
    market_cap_max = st.number_input(
        "Market Cap Lower Than",
        value=50000000000,
        help="Maximum market capitalization."
    )
    price_min = st.number_input(
        "Price More Than",
        value=10.0,
        help="Minimum stock price."
    )
    price_max = st.number_input(
        "Price Lower Than",
        value=500.0,
        help="Maximum stock price."
    )
    beta_min = st.number_input(
        "Beta More Than",
        value=0.5,
        help="Minimum beta value."
    )
    beta_max = st.number_input(
        "Beta Lower Than",
        value=2.0,
        help="Maximum beta value."
    )
    volume_min = st.number_input(
        "Volume More Than",
        value=10000,
        help="Minimum trading volume."
    )
    volume_max = st.number_input(
        "Volume Lower Than",
        value=1000000,
        help="Maximum trading volume."
    )
    dividend_min = st.number_input(
        "Dividend More Than",
        value=0.0,
        help="Minimum dividend value."
    )
    dividend_max = st.number_input(
        "Dividend Lower Than",
        value=5.0,
        help="Maximum dividend value."
    )

# Categorical Filters
with st.sidebar.expander("Categorical Filters", expanded=True):
    sector_options = [
        "Technology", "Financial Services", "Consumer Cyclical", "Energy", "Industrials",
        "Basic Materials", "Communication Services", "Consumer Defensive", "Healthcare",
        "Real Estate", "Utilities", "Industrial Goods", "Financial", "Services", "Conglomerates"
    ]
    selected_sectors = st.multiselect(
        "Sector",
        options=sector_options,
        help="Select one or more sectors."
    )
    industry_options = [
        "Autos", "Banks", "Banks Diversified", "Software", "Banks Regional",
        "Beverages Alcoholic", "Beverages Brewers", "Beverages Non-Alcoholic"
    ]
    selected_industries = st.multiselect(
        "Industry",
        options=industry_options,
        help="Select one or more industries."
    )
    country_options = ["US", "UK", "MX", "BR", "RU", "HK", "CA"]
    selected_countries = st.multiselect(
        "Country",
        options=country_options,
        help="Select one or more countries."
    )
    exchange_options = ["nyse", "nasdaq", "amex", "euronext", "tsx", "etf", "mutual_fund"]
    selected_exchanges = st.multiselect(
        "Exchange",
        options=exchange_options,
        help="Select one or more exchanges."
    )

# Boolean Filters
with st.sidebar.expander("Boolean Filters", expanded=True):
    is_etf = st.checkbox(
        "Is ETF",
        value=False,
        help="Check to return only ETFs."
    )
    is_fund = st.checkbox(
        "Is Fund",
        value=False,
        help="Check to return only funds."
    )
    is_actively_trading = st.checkbox(
        "Is Actively Trading",
        value=True,
        help="Check to return only actively traded stocks."
    )

# Run Analysis button (placed outside expanders)
run_analysis = st.sidebar.button("Run Analysis")

def get_stock_data(params):
    # Copy parameters and add API key.
    filters = params.copy()
    filters["apikey"] = API_KEY
    # Convert list values to comma-separated strings.
    for key, value in filters.items():
        if isinstance(value, list) and value:
            filters[key] = ",".join(map(str, value))
    try:
        response = requests.get(BASE_URL, params=filters, timeout=10)
        response.raise_for_status()
        data = response.json()
        if not data:
            st.error("No results found for the provided filters.")
            return pd.DataFrame()
        return pd.DataFrame(data)
    except Exception:
        st.error("An error occurred while fetching data.")
        return pd.DataFrame()

if run_analysis:
    # Build parameter dictionary.
    params = {}
    params["marketCapMoreThan"] = market_cap_min
    params["marketCapLowerThan"] = market_cap_max
    params["priceMoreThan"] = price_min
    params["priceLowerThan"] = price_max
    params["betaMoreThan"] = beta_min
    params["betaLowerThan"] = beta_max
    params["volumeMoreThan"] = volume_min
    params["volumeLowerThan"] = volume_max
    params["dividendMoreThan"] = dividend_min
    params["dividendLowerThan"] = dividend_max

    if selected_sectors:
        params["sector"] = selected_sectors
    if selected_industries:
        params["industry"] = selected_industries
    if selected_countries:
        params["country"] = selected_countries
    if selected_exchanges:
        params["exchange"] = selected_exchanges

    params["isEtf"] = is_etf
    params["isFund"] = is_fund
    params["isActivelyTrading"] = is_actively_trading

    # Set limit in the backend.
    params["limit"] = LIMIT

    with st.spinner("Fetching stock data..."):
        df = get_stock_data(params)

    if not df.empty:
        st.success("Data fetched successfully!")
        
        #with st.expander("Results", expanded=True):
        with st.container(border=True):
            # Display the data table.
            with st.container(border=True):
                st.dataframe(df)

            # Common hover data for scatter plots.
            hover_fields = ["symbol", "companyName", "sector", "industry", "exchangeShortName", "country"]

            # Chart 1: Price vs Market Cap (Scatter)
            with st.container(border=True):
                try:
                    fig1 = px.scatter(
                        df,
                        x="price",
                        y="marketCap",
                        size="volume",
                        title="Price vs Market Cap",
                        hover_data=hover_fields,
                        template="plotly_dark"
                    )
                    st.plotly_chart(fig1, use_container_width=True)
                except Exception:
                    st.warning("Scatter plot could not be generated.")

            # Chart 2: Sector Distribution (Bar Chart)
            with st.container(border=True):
                try:
                    sector_counts = df["sector"].value_counts().reset_index()
                    sector_counts.columns = ["Sector", "Count"]
                    fig2 = px.bar(
                        sector_counts,
                        x="Sector",
                        y="Count",
                        title="Sector Distribution",
                        template="plotly_dark"
                    )
                    st.plotly_chart(fig2, use_container_width=True)
                except Exception:
                    st.warning("Sector distribution chart could not be generated.")

            # Chart 3: Price Distribution (Histogram)
            with st.container(border=True):
                try:
                    fig3 = px.histogram(
                        df,
                        x="price",
                        nbins=30,
                        title="Price Distribution",
                        template="plotly_dark"
                    )
                    st.plotly_chart(fig3, use_container_width=True)
                except Exception:
                    st.warning("Price distribution chart could not be generated.")

            # Chart 4: Market Cap vs Volume (Scatter)
            with st.container(border=True):
                try:
                    fig4 = px.scatter(
                        df,
                        x="volume",
                        y="marketCap",
                        size="price",
                        title="Market Cap vs Volume",
                        hover_data=hover_fields,
                        template="plotly_dark"
                    )
                    st.plotly_chart(fig4, use_container_width=True)
                except Exception:
                    st.warning("Market Cap vs Volume chart could not be generated.")

            # Chart 5: Country Breakdown (Bar Chart)
            with st.container(border=True):
                try:
                    country_counts = df["country"].value_counts().reset_index()
                    country_counts.columns = ["Country", "Count"]
                    fig5 = px.bar(
                        country_counts,
                        x="Country",
                        y="Count",
                        title="Country Breakdown",
                        template="plotly_dark",
                        #color_discrete_sequence=px.colors.qualitative.Plotly  # override global green
                    )
                    st.plotly_chart(fig5, use_container_width=True)
                except Exception:
                    st.warning("Country breakdown chart could not be generated.")

            # Chart 6: Dividend vs Price (Scatter)
            with st.container(border=True):
                try:
                    if "lastAnnualDividend" in df.columns:
                        fig6 = px.scatter(
                            df,
                            x="price",
                            y="lastAnnualDividend",
                            title="Dividend vs Price",
                            hover_data=hover_fields,
                            template="plotly_dark"
                        )
                        st.plotly_chart(fig6, use_container_width=True)
                    else:
                        st.info("Dividend data is not available.")
                except Exception:
                    st.warning("Dividend vs Price chart could not be generated.")

            # Chart 7: Beta Distribution (Histogram)
            with st.container(border=True):
                try:
                    fig7 = px.histogram(
                        df,
                        x="beta",
                        nbins=30,
                        title="Beta Distribution",
                        template="plotly_dark"
                    )
                    st.plotly_chart(fig7, use_container_width=True)
                except Exception:
                    st.warning("Beta distribution chart could not be generated.")

            # Chart 8: Exchange Breakdown (Bar Chart)
            with st.container(border=True):
                try:
                    if "exchangeShortName" in df.columns:
                        exchange_counts = df["exchangeShortName"].value_counts().reset_index()
                        exchange_counts.columns = ["Exchange", "Count"]
                        fig8 = px.bar(
                            exchange_counts,
                            x="Exchange",
                            y="Count",
                            title="Exchange Breakdown",
                            template="plotly_dark"
                        )
                        st.plotly_chart(fig8, use_container_width=True)
                    else:
                        st.info("Exchange data is not available.")
                except Exception:
                    st.warning("Exchange breakdown chart could not be generated.")
            
# Hide default Streamlit style
st.markdown(
    """
    <style>
    #MainMenu {visibility: hidden;}
    footer {visibility: hidden;}
    </style>
    """,
    unsafe_allow_html=True
)