import pandas as pd
import gradio as gr
import os
from gradio_rangeslider import RangeSlider

from src.filter_utils import filter, filter_cols

# Main Leaderboard containing everything
text_leaderboard = pd.read_csv(os.path.join('assets', 'merged_data.csv'))
text_leaderboard = text_leaderboard.sort_values(by='Clemscore', ascending=False)  

open_weight_df = text_leaderboard[text_leaderboard['Open Weight'] == True]
if not open_weight_df.empty:  # Check if filtered df is non-empty
    max_parameter_size = open_weight_df['Parameters (B)'].max()

# Short leaderboard containing fixed columns
short_leaderboard = filter_cols(text_leaderboard)

## Extract data
langs = []
licenses = []
ip_prices = []
op_prices = []
latencies = []
parameters = []
contexts = []
dates = []

for i in range(len(text_leaderboard)):
    lang_splits = text_leaderboard.iloc[i]['Languages'].split(',')
    lang_splits = [s.strip() for s in lang_splits]
    langs += lang_splits
    license_name = text_leaderboard.iloc[i]['License Name']

    licenses.append(license_name)
    ip_prices.append(text_leaderboard.iloc[i]['Input $/1M tokens'])
    op_prices.append(text_leaderboard.iloc[i]['Output $/1M tokens'])
    latencies.append(text_leaderboard.iloc[i]['Latency (s)'])
    parameters.append(text_leaderboard.iloc[i]['Parameters (B)'])
    contexts.append(text_leaderboard.iloc[i]['Context Size (k)'])
    dates.append(text_leaderboard.iloc[i]['Release Date'])


langs = list(set(langs))
langs.sort()

licenses = list(set(licenses))
licenses.sort()

max_input_price = max(ip_prices)
max_output_price = max(op_prices)
max_latency = max(latencies)

min_parameters = 0 if pd.isna(min(parameters)) else min(parameters)
max_parameter = max_parameter_size
parameter_step = 1
print(f"MIN {min_parameters}, MAX {max_parameter}")

min_context = min(contexts)
max_context = max(contexts)
context_step = 8

min_date = min(dates)
max_date = max(dates)

TITLE = """<h1 align="center" id="space-title"> LLM Calculator ⚖️⚡ 📏💰</h1>"""
CSS = """
#double-slider-1 {height: 100px}
#double-slider-2 {height: 100px}
#double-slider-3 {height: 100px}
#double-slider-4 {height: 100px}
"""

llm_calc_app = gr.Blocks(css=CSS)
with llm_calc_app:

    gr.HTML(TITLE)

    ##################################################

    with gr.Row():

        #####################################
        # First Column
        ####################################
        ## Language Select
        with gr.Column():

            with gr.Row():
                lang_dropdown = gr.Dropdown(
                    choices=langs,
                    value=[],
                    multiselect=True,
                    label="Select Languages 🗣️"
                )

            with gr.Row():
                start_date = gr.DateTime(
                )

                end_date = gr.DateTime(
                )


    llm_calc_app.load()
llm_calc_app.queue()
llm_calc_app.launch()



"""
model_name, input_price, output_price,
multimodality_image,multimodality_multiple_image,multimodality_audio,multimodality_video,
source,licence_name,licence_url,languages,release_date,
parameters_estimated,parameters_actual,

open_weight,context, 

additional_prices_context_caching,
additional_prices_context_storage,
additional_prices_image_input,additional_prices_image_output,additional_prices_video_input,additional_prices_video_output,additional_prices_audio_input,additional_prices_audio_output,clemscore_v1.6.5_multimodal,clemscore_v1.6.5_ascii,clemscore_v1.6,latency_v1.6,latency_v1.6.5_multimodal,latency_v1.6.5_ascii,

average_clemscore,average_latency,parameters

Final list

model_name, input_price, output_price,
multimodality_image,multimodality_multiple_image,multimodality_audio,multimodality_video,
source,licence_name,licence_url,languages,release_date, open_weight,context, average_clemscore,average_latency,parameters


Filter
multimodality_image,multimodality_multiple_image,multimodality_audio,multimodality_video,
licence_name+licence_url, languages, release_date, open_weight

RR
model_name, input_price, output_price,
source, release_date

"""