import streamlit as st
import pandas as pd
import yfinance as yf
import plotly.express as px
import os
from datetime import datetime, timedelta

# ✅ Ceci doit être le premier appel Streamlit
st.set_page_config(layout="wide", page_title="📈 Euronext Growth - Analyse Interactive")


# --- Constantes
#CACHE_FILE = "cac40_data.json"
CACHE_FILE = os.path.join("/tmp", "euronext_growth_data.json")
CACHE_DURATION_HOURS = 1
#TICKERS = [
#    "ALNEV.PA", "ALSRS.PA", "ALNOV.PA", "ALTD.PA", "ALTBG.PA", "ALBOO.PA", "ALNRG.PA", "ALHG.PA",
#    "ALSPW.PA", "ALCBI.PA", "ALTAO.PA", "ALARF.PA", "ALADO.PA", "ALAFY.PA", "ALAGP.PA", "ALGR.PA",
#    "ALCHI.PA"
#]

TICKERS = [
    "ALNEV.PA", "ALSRS.PA", "ALNOV.PA", "ALTD.PA", "ALTBG.PA", "ALBOO.PA", "ALNRG.PA", "ALHG.PA",
    "ALSPW.PA", "ALCBI.PA", "ALTAO.PA", "ALARF.PA", "ALADO.PA"
]

# --- Fonctions cache / data
def is_cache_valid(path, duration_hours):
    if not os.path.exists(path):
        return False
    mtime = datetime.fromtimestamp(os.path.getmtime(path))
    return datetime.now() - mtime < timedelta(hours=duration_hours)

def load_cached_data(path):
    return pd.read_json(path)

def save_data_to_cache(df, path):
    df.to_json(path, orient="records", indent=2)

def fetch_cac40_data():
    data = []
    for ticker in TICKERS:
        stock = yf.Ticker(ticker)
        info = stock.info
        try:
            data.append({
                "Name": info.get("shortName", ticker),
                "Price Change (%)": round(info.get("regularMarketChangePercent", 0), 2),
                "Volume": info.get("regularMarketVolume", 0),
                "Nb shares": info.get("sharesOutstanding", 0),
                "Price": round(info.get("regularMarketPrice", 0), 2),
                "Sector": info.get("industry", "N/A"),
                "Effectif": info.get("fullTimeEmployees", 0)
            })
        except:
            continue
    return pd.DataFrame(data)

def get_data():
    if is_cache_valid(CACHE_FILE, CACHE_DURATION_HOURS):
        return load_cached_data(CACHE_FILE)
    else:
        df = fetch_cac40_data()
        save_data_to_cache(df, CACHE_FILE)
        return df

# --- Streamlit UI



st.title("📈 Euronext Growth : Capitalisations, secteurs et variations en un coup d'œil")
st.markdown("Affichage des sociétés du CAC 40 avec variation de prix et capitalisation boursière.")

df = get_data()
df["Market Cap (B eur)"] = round(df["Nb shares"] * df["Price"] / 1e9, 2)
df["return_ratio_text_info"] = df["Price Change (%)"].apply(lambda x: f"{x:+.2f}")
df["Root"] = "📊 Euronext Growth"

fig = px.treemap(
    df,
    path=["Root", "Sector", "Name"],
    values="Market Cap (B eur)",
    color="Price Change (%)",
    color_continuous_scale=px.colors.diverging.RdYlGn,
    color_continuous_midpoint=0,
    custom_data=[
        "Price", "return_ratio_text_info", "Volume", "Nb shares",
        "Market Cap (B eur)", "Sector", "Effectif"
    ],
    width=1200,
    height=700
)

fig.update_traces(
    root_color="#f0f0f0",
    textposition="middle center",
    texttemplate="<b>%{label}</b><br>%{customdata[1]}%",
    hovertemplate="<b>%{label}</b><br>" +
        "Secteur : %{customdata[5]}<br>" +
        "Cours : %{customdata[0]:.2f} €<br>" +
        "Variation : %{customdata[1]}%<br>" +
        "Volume : %{customdata[2]:,}<br>" +
        "Capitalisation : %{customdata[4]:.2f} Mds €<br>" +
        "Effectif : %{customdata[6]:,}"
)

st.plotly_chart(fig, use_container_width=True)