app.py

import gradio as gr
import pandas as pd
import sys
sys.path.append("rd2l_pred")
from training_data_prep import list_format, modification, league_money, df_gen
from feature_engineering import heroes, hero_information


def fetch_data(user_id, mmr, comf_1, comf_2, comf_3, comf_4, comf_5):
    player_id = user_id.split("/")[-1]

    series = {"player_id" : player_id, "mmr" : mmr, "p1" : comf_1, "p2" : comf_2, "p3" : comf_3, "p4" : comf_4, "p5" : comf_5}

    money = pd.read_csv("result_money.csv")    

    print()
    print(f"Reading player {player_id}.  Starting now")

    print(money)
    print(money.values)

    for item in money.values:
        series.update({item[0] : float(item[1])})

    print("Corrected Series")
    print(series)
    
    d = pd.Series(series)
    print(d)

    # This the original section used to add the money section to the series.
    # d = d.assign(count=lambda x: money[player_season].loc['count'], mean=lambda x: money[player_season].loc['mean'], std=lambda x: money[player_season].loc['std'], min=lambda x: money[player_season].loc['min'], max=lambda x: money[player_season].loc['max'], sum=lambda x: money[player_season].loc['sum'])

    # We need to generate the inputs for the sheet using hugging face
    # We also need to load the money values from the generated csv file
    # df_gen(draft, league_money(captains, data_type), data_type)

    print(f"Done reading player {player_id}")
    print()

    return player_id

demo = gr.Interface(
    fn=fetch_data,
    inputs=[
        gr.Textbox(label="Player ID or Link to OpenDota/Dotabuff"),
        gr.Textbox(label="MMR"),
        gr.Slider(1, 5, value=1, step=1, label="Comfort (Pos 1)"),
        gr.Slider(1, 5, value=1, step=1, label="Comfort (Pos 2)"),
        gr.Slider(1, 5, value=1, step=1, label="Comfort (Pos 3)"),
        gr.Slider(1, 5, value=1, step=1, label="Comfort (Pos 4)"),
        gr.Slider(1, 5, value=1, step=1, label="Comfort (Pos 5)")
    ],
    # examples=[
    #     [np.asarray(Image.open("examples/1500_maze.jpg")), "Bulge", True, 0.25, 0.5, 0.5, 0.5],
    #     [np.asarray(Image.open("examples/2048_maze.jpg")), "Bulge", True, 0.25, 0.5, 0.5, 0.5],
    #     [np.asarray(Image.open("examples/2300_fresh.jpg")), "Bulge", True, 0.25, 0.5, 0.5, 0.5],
    #     [np.asarray(Image.open("examples/50_fresh.jpg")), "Bulge", True, 0.25, 0.5, 0.5, 0.5]
    # ],
    outputs=[
        "text"
    ],
    title="RD2L Pricing Prediction",
    article="Uhhhhh this is the article",
    description="Uhhhhh this is the description"
)
demo.launch()