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import streamlit as st |
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from pages import set_app_title_and_logo, qb_gpt_page, contacts_and_disclaimers |
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import json |
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import pandas as pd |
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import numpy as np |
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from tools import tokenizer |
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from assets.models import QBGPT |
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moves_to_pred = 11170 |
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input_size = 11172 |
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starts_size = 1954 |
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scrimmage_size = 100 |
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positions_id = 29 |
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temp_ids = 52 |
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off_def_size = 2 |
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token_type_size = 3 |
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play_type_size = 9 |
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qbgpt = QBGPT(input_vocab_size = input_size, |
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positional_vocab_size = temp_ids, |
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position_vocab_size=positions_id, |
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start_vocab_size=starts_size, |
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scrimmage_vocab_size=scrimmage_size, |
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offdef_vocab_size = off_def_size, |
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type_vocab_size = token_type_size, |
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playtype_vocab_size = play_type_size, |
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embedding_dim = 256, |
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hidden_dim = 256, |
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num_heads = 3, |
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diag_masks = False, |
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to_pred_size = moves_to_pred) |
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qbgpt.load_weights("assets/model_mediumv2/QBGPT") |
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qb_tok = tokenizer(moves_index="./assets/moves_index.parquet", |
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play_index="./assets/plays_index.parquet", |
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positions_index="./assets/positions_index.parquet", |
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scrimmage_index="./assets/scrimmage_index.parquet", |
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starts_index="./assets/starts_index.parquet", |
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time_index="./assets/time_index.parquet", |
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window_size=20) |
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with open('./assets/ref.json', 'r') as fp: |
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ref_json = json.load(fp) |
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def convert_numpy(d): |
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return {k:np.array(v) for k,v in d.items()} |
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ref_json = {int(k):convert_numpy(v) for k,v in ref_json.items()} |
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ref_df = pd.read_json("./assets/ref_df.json") |
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def main(): |
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set_app_title_and_logo() |
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st.sidebar.title("Navigation") |
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page = st.sidebar.radio("Go to:", ("QB-GPT", "Contacts and Disclaimers")) |
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if page == "QB-GPT": |
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st.title("QB-GPT") |
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qb_gpt_page(ref_df, ref_json, qb_tok, qbgpt) |
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if page == "Contacts and Disclaimers": |
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contacts_and_disclaimers() |
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if __name__ == "__main__": |
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main() |
