import streamlit as st
import requests
import pandas as pd
import os
import asyncio
import aiohttp

# Global API key and default parameters
API_KEY = os.getenv("FMP_API_KEY")
DEFAULT_COMPANY = "syros"
NUM_PAGES = 10  # Backend variable: number of pages to fetch

# Initialize session state run flags and inputs if not already set
if "run_mna_feed" not in st.session_state:
    st.session_state.run_mna_feed = False
if "run_mna_search" not in st.session_state:
    st.session_state.run_mna_search = False
if "search_company" not in st.session_state:
    st.session_state.search_company = DEFAULT_COMPANY
if "feed_date" not in st.session_state:
    st.session_state.feed_date = pd.to_datetime("2025-01-01").date()

##############################################
# ASYNCHRONOUS FUNCTIONS FOR M&A FEED
##############################################
async def fetch_mna_feed_page(session, page):
    url = f"https://financialmodelingprep.com/api/v4/mergers-acquisitions-rss-feed?page={page}&apikey={API_KEY}"
    try:
        async with session.get(url) as response:
            if response.status == 200:
                data = await response.json()
                return pd.DataFrame(data)
            else:
                return pd.DataFrame()
    except Exception:
        # Fail gracefully without exposing the data source
        return pd.DataFrame()

async def fetch_all_mna_feed(num_pages=NUM_PAGES):
    async with aiohttp.ClientSession() as session:
        tasks = [fetch_mna_feed_page(session, page) for page in range(num_pages)]
        results = []
        progress_bar = st.progress(0)
        completed = 0
        for task in asyncio.as_completed(tasks):
            result = await task
            results.append(result)
            completed += 1
            progress_bar.progress(completed / num_pages)
        return results

def async_fetch_mna_feed(num_pages=NUM_PAGES) -> pd.DataFrame:
    results = asyncio.run(fetch_all_mna_feed(num_pages))
    if results:
        df = pd.concat(results, ignore_index=True)
        if "transactionDate" in df.columns:
            df["transactionDate"] = pd.to_datetime(df["transactionDate"], errors="coerce")
        return df
    return pd.DataFrame()

##############################################
# CACHED FUNCTION FOR M&A SEARCH
##############################################
@st.cache_data(show_spinner=False)
def fetch_mna_search(company_name: str) -> pd.DataFrame:
    """
    Fetch M&A news filtered by company name using the search endpoint.
    """
    url = f"https://financialmodelingprep.com/api/v4/mergers-acquisitions/search?name={company_name}&apikey={API_KEY}"
    try:
        response = requests.get(url)
        response.raise_for_status()
        data = response.json()
    except:
        return pd.DataFrame()
    if not data:
        return pd.DataFrame()
    return pd.DataFrame(data)

##############################################
# MAIN APP
##############################################
def main():
    st.set_page_config(page_title="M&A Feed Dashboard", layout="wide")
    st.title("M&A Transaction Analysis")
    st.write(
        "This dashboard provides a real-time stream of Mergers & Acquisitions news and announcements. "
        "Use the side menu below to choose between viewing the full M&A live feed or searching for M&A news by company name."
    )
    
    # Sidebar (inside an expander) for navigation and options
    with st.sidebar.expander("Navigation and Options", expanded=True):
        page = st.radio(
            "Select Page",
            ("M&A Feed", "M&A Search"),
            help="Choose 'M&A Feed' to view the complete RSS feed or 'M&A Search' to search for news by company name."
        )
        if page == "M&A Feed":
            feed_date = st.date_input(
                "From Date",
                value=pd.to_datetime("2025-01-01").date(),
                help="Select a starting date. Only M&A transactions on or after this date will be shown."
            )
            st.session_state.feed_date = feed_date
            if st.button("Run M&A Feed"):
                st.session_state.run_mna_feed = True
        else:
            company = st.text_input(
                "Company Name",
                value=DEFAULT_COMPANY,
                help="Enter the company name to search for M&A news (default is 'syros')."
            )
            st.session_state.search_company = company
            if st.button("Run M&A Search"):
                st.session_state.run_mna_search = True

    # Display page content based on the selected page
    if page == "M&A Feed":
        st.header("M&A RSS Feed")
        st.write(
            "Below is the latest M&A live feed data filtered by transaction date. "
            "Only M&A transactions on or after the selected date are shown. "
            "The table displays details such as the acquiring company, target company, transaction date, and URL."
        )
        if st.session_state.run_mna_feed:
            df_feed = async_fetch_mna_feed()
            if df_feed.empty:
                st.error("No data returned from the M&A feed.")
            else:
                if "transactionDate" in df_feed.columns:
                    filtered_df = df_feed[df_feed["transactionDate"].dt.date >= st.session_state.feed_date]
                else:
                    filtered_df = df_feed
                if filtered_df.empty:
                    st.error("No M&A transactions found on or after the selected date.")
                else:
                    st.dataframe(filtered_df, use_container_width=True)
        else:
            st.info("Click 'Run M&A Feed' in the sidebar to fetch data.")
    else:
        st.header("M&A Search")
        st.write(
            "Search for M&A news by company name. "
            "The table below displays the matching announcements, including details such as the acquiring company, "
            "target company, transaction date, and announcement URL."
        )
        if st.session_state.run_mna_search:
            df_search = fetch_mna_search(st.session_state.search_company)
            if df_search.empty:
                st.error("No M&A news found for the specified company name.")
            else:
                st.dataframe(df_search, use_container_width=True)
        else:
            st.info("Enter a company name in the sidebar and click 'Run M&A Search' to fetch data.")

if __name__ == "__main__":
    main()

hide_streamlit_style = """
<style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
</style>
"""
st.markdown(hide_streamlit_style, unsafe_allow_html=True)