from dash import Input, Output
import plotly.express as px
import os

from helpers.processor import Processor
from helpers.s3 import S3Client
from helpers.models import S3Config
from global_vars import BUCKET_NAME
from app import app
from dotenv import load_dotenv
load_dotenv(".env")
print("*********************c")
print(os.getenv("AWS_ENDPOINT_URL_S3"))
# Initialisation des clients
s3_config = S3Config(
    bucket_name=BUCKET_NAME,
    endpoint_url=os.getenv("AWS_ENDPOINT_URL_S3"),
    access_key=os.getenv("AWS_ACCESS_KEY_ID"),
    secret_key=os.getenv("AWS_SECRET_ACCESS_KEY")
)
s3_client = S3Client(config=s3_config)
processor = Processor()


@app.callback(
    [
        Output("chapter-dropdown-contrib", "options"),
        Output("page-dropdown-contrib", "options"),
    ],
    Input("chapter-dropdown-contrib", "value"),
)
def update_dropdowns(chapter_value):
    df_contributions = processor.get_contribution_data(s3_client)
    if df_contributions.empty:
        return [], []

    # chapitres unique
    chapters = sorted(df_contributions["chapter"].unique())
    chapter_options = [{"label": "Tous les chapitres", "value": "all"}] + [
        {"label": ch, "value": ch} for ch in chapters
    ]

    # Récupération des pages en fonction du chapitre sélectionné
    if chapter_value and chapter_value != "all":
        pages = sorted(
            df_contributions[df_contributions["chapter"] == chapter_value]["page"].unique()
        )
    else:
        pages = sorted(df_contributions["page"].unique())

    page_options = [{"label": "Toutes les pages", "value": "all"}] + [
        {"label": p, "value": p} for p in pages
    ]

    return chapter_options, page_options


@app.callback(
    [Output("contributions-graph", "figure"), Output("data-summary", "children")],
    [
        Input("chapter-dropdown-contrib", "value"),
        Input("page-dropdown-contrib", "value"),
        Input("mode-radio", "value"),
    ],
)
def update_graph(chapter, page, mode):
    df_contributions = processor.get_contribution_data(s3_client)

    if df_contributions.empty:
        return px.bar(title="Aucune donnée disponible."), "Aucune donnée chargée."

    filtered_df_contributions = df_contributions.copy()
    if chapter != "all":
        filtered_df_contributions = filtered_df_contributions[
            filtered_df_contributions["chapter"] == chapter
        ]
    if page != "all":
        filtered_df_contributions = filtered_df_contributions[
            filtered_df_contributions["page"] == page
        ]

    contributions = (
        filtered_df_contributions.groupby("user_id").size().reset_index(name="value")
    )

    if mode == "percentage":
        total = contributions["value"].sum()
        contributions["value"] = (contributions["value"] / total * 100).round(2)
        value_label = "Contributions (%)"
    else:
        value_label = "Nombre de Contributions"

    fig = px.bar(
        contributions,
        y="user_id",
        x="value",
        labels={"user_id": "Contributeur", "value": value_label},
        title=f"Résumé des Contributions ({chapter}/{page})",
        text="value",
        orientation="h",  # Barre horizontale
    )

    fig.update_traces(textposition="outside")
    fig.update_layout(
        xaxis_title=value_label, yaxis_title="ID Contributeur", bargap=0.2
    )

    summary = f"Affichage de {len(filtered_df_contributions)} contributions de {len(contributions)} contributeurs."

    return fig, summary