from dash import Dash, html, dcc, Input, Output
import pandas as pd
import dash_mantine_components as dmc
from graphs.model_market_share import create_stacked_area_chart, create_world_map, create_range_slider
from graphs.leaderboard import create_leaderboard
from graphs.model_characteristics import create_concentration_chart, create_line_plot
from graphs.tree import generate_model_treemap

# Initialize the app
app = Dash()
server = app.server

# Load pre-processed data frames
filtered_df = pd.read_pickle("data_frames/filtered_df.pkl")
model_topk_df = pd.read_pickle("data_frames/model_topk_df.pkl")
model_gini_df = pd.read_pickle("data_frames/model_gini_df.pkl")
model_hhi_df = pd.read_pickle("data_frames/model_hhi_df.pkl")
language_concentration_df = pd.read_pickle("data_frames/language_concentration_df.pkl")
license_concentration_df = pd.read_pickle("data_frames/download_license_cumsum_df.pkl")
download_method_cumsum_df = pd.read_pickle("data_frames/download_method_cumsum_df.pkl")
download_arch_cumsum_df = pd.read_pickle("data_frames/download_arch_cumsum_df.pkl")
nat_topk_df = pd.read_pickle("data_frames/nat_topk_df.pkl")
country_concentration_df = pd.read_pickle("data_frames/country_concentration_df.pkl")
author_concentration_df = pd.read_pickle("data_frames/author_concentration_df.pkl")
model_concentration_df = pd.read_pickle("data_frames/model_concentration_df.pkl")

# Configurations
TEMP_MODEL_EVENTS = {
    # "Yolo World Mirror": "2024-03-01",
    "Llama 3": "2024-04-17",
    "Stable Cascade": "2024-02-02",
    "Stable Diffusion 3": "2024-05-30",
    # "embed/upscale": "2023-03-24",
    "DeepSeek-R1": "2025-01-20",
    "Gemma-3 12B QAT": "2025-04-15", # gemma-3-12b-it-qat-4bit
    # "Qwen": "2025-03-05",
    # "Flux RedFlux": "2025-04-12",
    # "DeepSeek-V3": "2025-03-24",
    # "bloom": "2022-05-19",
    "DALLE2-PyTorch": "2022-06-25",
    "Stable Diffusion": "2022-08-10",
    "CLIP ViT": "2021-01-05",
    "YOLOv8": "2023-04-26",
    "Sentence Transformer MiniLM v2": "2021-08-30",
}

PALETTE_0 = [
    "#335C67",
    "#FFF3B0",
    "#E09F3E",
    "#9E2A2B",
    "#540B0E"
]

LANG_SEGMENT_ORDER = [
    'Monolingual: EN', 'Monolingual: HR', 'Monolingual: M/LR', 
    'Multilingual: HR', 'Multilingual', 'Unknown',
]

LICENSE_SEGMENT_ORDER = [
    "Open Use", "Open Use (Acceptable Use Policy)", "Open Use (Non-Commercial Only)", "Attribution", 
    "Acceptable Use Policy", "Non-Commercial Only", "Undocumented", "Undocumented (Acceptable Use Policy)",
]

METHOD_PLOT_CHOICES = {
    "cumulative": "none", # none, mean, sum
    "y_col": "percent", # percent count
    "y_log": False, # True, False
    "period": "W",
}

ARCHITECTURE_PLOT_CHOICES = {
    "cumulative": "none", # none, mean, sum
    "y_col": "percent", # percent count
    "y_log": False, # True, False
    "period": "W",
}

# Create initial figures
# Model Market Share Tab
model_market_share_area = create_stacked_area_chart(
    model_topk_df, model_gini_df, model_hhi_df, TEMP_MODEL_EVENTS, PALETTE_0
)

world_map = create_world_map(
    filtered_df
)

slider = create_range_slider(
    model_topk_df
)

time_slider = dmc.RangeSlider(
    id="time-slider",
    min=model_topk_df['time'].min().timestamp(),
    max=model_topk_df['time'].max().timestamp(),
    value=[
        model_topk_df['time'].min().timestamp(),
        model_topk_df['time'].max().timestamp()
    ],
    step=24 * 60 * 60,
    color="blue",
    size="md",
    radius="xl",
    marks=[
        {"value": model_topk_df['time'].min().timestamp(), "label": model_topk_df['time'].min().strftime("%b %Y")},
        {"value": model_topk_df['time'].max().timestamp(), "label": model_topk_df['time'].max().strftime("%b %Y")}
    ],
    style={"width": "70%", "margin": "0 auto"},
    labelAlwaysOn=False
)

# Model Characteristics Tab
language_concentration_area = create_concentration_chart(
    language_concentration_df, 'time', 'metric', 'value', LANG_SEGMENT_ORDER, PALETTE_0
)

license_concentration_area = create_concentration_chart(
    license_concentration_df, 'period', 'status', 'percent', LICENSE_SEGMENT_ORDER, PALETTE_0
)

download_method_cumsum_line = create_line_plot(
    download_method_cumsum_df, METHOD_PLOT_CHOICES, PALETTE_0
)

download_arch_cumsum_line = create_line_plot(
    download_arch_cumsum_df, ARCHITECTURE_PLOT_CHOICES, PALETTE_0
)

tree_map = generate_model_treemap(
    filtered_df
)

# App layout
app.layout = dmc.MantineProvider(
    theme={"colorScheme": "light",
           "primaryColor": "blue",
           "fontFamily": "Inter, sans-serif"},
    children=[html.Div(
    [
        html.Div(
            [
                html.Div(children='Visualizing the Open Model Ecosystem', style={'fontSize': 28, 'fontWeight': 'bold', 'marginBottom': 6}),
                html.Div(children='An interactive dashboard to explore trends in open models on Hugging Face', style={'fontSize': 16, 'marginBottom': 12}),
                html.Div(
                    children=[
                        html.A(
                            "Data Provenance Initiative",
                            href="https://www.dataprovenance.org/",
                            target="_blank",
                            style={
                                'display': 'inline-block',
                                'padding': '4px 14px',
                                'fontSize': 13,
                                'color': 'white',
                                'border': 'none',
                                'backgroundColor': '#228BE6',
                                'borderRadius': '18px',
                                'textDecoration': 'none',
                                'fontWeight': 'bold',
                                'boxShadow': '0 2px 8px rgba(37,99,235,0.08)',
                                'marginLeft': '6px',
                                'marginBottom': '4px',
                                'transition': 'background 0.2s',
                                'cursor': 'pointer'
                            }
                        )
                    ],
                    style={'fontSize': 14, 'marginBottom': 12}
                ),
                html.Hr(style={'marginTop': 8, 'marginBottom': 8}),
                html.Div(children='Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry\'s standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.', style={'fontSize': 14, 'marginBottom': 12, 'marginLeft': 100, 'marginRight': 100}),
            ],
            style={'textAlign': 'center'}
        ),
        html.Div(
            [
                dcc.Tabs([
                    dcc.Tab(label='Model Market Share', children=[
                        html.Div([
                            html.Div(children='Select time range to update all graphs below:', style={'fontSize': 16, 'marginBottom': 6, 'marginTop': 20}),
                            time_slider,
                            html.Div(
                                id='output-container-range-slider',
                                style={
                                    'textAlign': 'center',
                                    'fontSize': 20,
                                    'marginBottom': 15,
                                    'marginTop': 30,
                                    'backgroundColor': 'white',
                                    'borderRadius': '12px',
                                    'boxShadow': '0 2px 12px rgba(0,0,0,0.10)',
                                    'padding': '18px',
                                    'display': 'inline-block',
                                }
                            ),
                        ], style={'marginBottom': 12, 'justifyContent': 'center', 'textAlign': 'center'}),
                        html.Div([
                            dcc.Graph(id='stacked-area-chart'),
                        ], style={'marginBottom': 12}),
                        html.Div([
                            html.Div(
                                dcc.Graph(id='world-map-with-slider'),
                                style={'display': 'flex', 'justifyContent': 'center'}
                            ),
                            # dcc.Graph(id='leaderboard'),
                        ], style={'marginBottom': 12})
                    ]),
                    dcc.Tab(label='Leaderboard', children=[
                        create_leaderboard(
                            filtered_df
                        )
                    ]),
                    dcc.Tab(label='Model Tree Map', children=[
                        dcc.Graph(figure=tree_map)
                    ]),
                    dcc.Tab(label='Model Characteristics',children=[
                        html.Div([
                            html.H3("Language Concentration", style={'textAlign': 'center', 'marginBottom': 10}),
                            dcc.Graph(figure=language_concentration_area),
                            html.H3("License Distribution", style={'textAlign': 'center', 'marginBottom': 10}),
                            dcc.Graph(figure=license_concentration_area),
                            html.H3("Method Trends", style={'textAlign': 'center', 'marginBottom': 10}),
                            dcc.Graph(figure=download_method_cumsum_line),
                            html.H3("Architecture Trends", style={'textAlign': 'center', 'marginBottom': 10}),
                            dcc.Graph(figure=download_arch_cumsum_line),
                        ], style={'marginBottom': 12}),
                    ]),
                ])
            ],
            style={
                'backgroundColor': 'white',
                'borderRadius': '18px',
                'boxShadow': '0 4px 24px rgba(0,0,0,0.10)',
                'padding': '32px',
                'margin': '32px auto',
                'maxWidth': '1250px',
            }
        )
    ],
    style={'fontFamily': 'Inter', 'backgroundColor': '#f7f7fa', 'minHeight': '100vh'}
)])

# Callbacks for interactivity

# Model Market Share Tab
# On slider change, update output text
@app.callback(
    Output('output-container-range-slider', 'children'),
    [Input('time-slider', 'value')]
)
def update_output(value):
    if value and len(value) == 2:
        start_time = pd.to_datetime(value[0], unit='s').strftime("%b %d, %Y")
        end_time = pd.to_datetime(value[1], unit='s').strftime("%b %d, %Y")
        return f"Selected time range: {start_time} to {end_time}"
    return "Select a time range"

# On slider change, update world map
@app.callback(
    Output('world-map-with-slider', 'figure'),
    Input('time-slider', 'value')
)
def update_world_map(value):
    if value and len(value) == 2:
        start_time = pd.to_datetime(value[0], unit='s').strftime('%Y-%m-%d')
        end_time = pd.to_datetime(value[1], unit='s').strftime('%Y-%m-%d')
        updated_fig = create_world_map(
            country_concentration_df, "time", "metric", "value",
            start_time=start_time, end_time=end_time
        )
        updated_fig.update_layout(font_family="Inter")
        return updated_fig
    return world_map


# On slider change, update leaderboard
# @app.callback(
#     Output('leaderboard', 'figure'),
#     [Input('time-slider', 'relayoutData')]
# )
# def update_leaderboard(relayout_data):
#     if relayout_data and 'xaxis.range[0]' in relayout_data and 'xaxis.range[1]' in relayout_data:
#         start_time = pd.to_datetime(relayout_data['xaxis.range[0]']).strftime('%Y-%m-%d')
#         end_time = pd.to_datetime(relayout_data['xaxis.range[1]']).strftime('%Y-%m-%d')
#         updated_fig = create_leaderboard(
#             country_concentration_df, author_concentration_df, model_concentration_df, start_time=start_time, end_time=end_time
#         )
#         updated_fig.update_layout(font_family="Inter")
#         return updated_fig
#     else:
#         return leaderboard

# On slider change, update stacked area chart
@app.callback(
    Output('stacked-area-chart', 'figure'),
    Input('time-slider', 'value')
)
def update_stacked_area(value):
    if value and len(value) == 2:
        start_time = pd.to_datetime(value[0], unit='s').strftime('%Y-%m-%d')
        end_time = pd.to_datetime(value[1], unit='s').strftime('%Y-%m-%d')
        updated_fig = create_stacked_area_chart(
            model_topk_df, model_gini_df, model_hhi_df,
            TEMP_MODEL_EVENTS, PALETTE_0,
            start_time=start_time, end_time=end_time
        )
        updated_fig.update_layout(font_family="Inter")
        return updated_fig
    return model_market_share_area

# Run the app
if __name__ == '__main__':
    app.run(debug=True)