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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "4826cf16",
"metadata": {},
"outputs": [],
"source": [
"import ipywidgets as widgets\n",
"from IPython.display import display, HTML\n",
"import pandas as pd\n",
"from fastai.tabular.all import *\n",
"import gradio as gr"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "34fc127c",
"metadata": {},
"outputs": [],
"source": [
"path = Path()\n",
"df = pd.read_csv(\"rookie_year.csv\")\n",
"learn = load_learner(path/\"export.pkl\")\n",
"columns = [\"Name\", \"G\", \"GS\", \"Cmp\", \"Att\", \"Yds\", \"Cmp%\", \"TD\", \"Int\", \"Y/G\", \"Sk\"]"
]
},
{
"cell_type": "code",
"execution_count": 64,
"id": "6e82eaae",
"metadata": {},
"outputs": [],
"source": [
"def predict(data):\n",
" row = df[df[\"Name\"] == data]\n",
" row = row.loc[:, ~df.columns.str.contains('^Unnamed')]\n",
" if not len(row):\n",
" print(\"ERROR: No QB in database with this name\")\n",
" return \n",
" pred_row, clas, probs = learn.predict(row.iloc[0])\n",
" prediction = pred_row.decode()[\"Tier\"].item() \n",
" return row[columns], prediction\n"
]
},
{
"cell_type": "code",
"execution_count": 81,
"id": "b9242a91",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7866/\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7866/\" width=\"900\" height=\"500\" allow=\"autoplay; camera; microphone;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(<gradio.routes.App at 0x26e1100f1c0>, 'http://127.0.0.1:7866/', None)"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demo = gr.Interface(fn=predict, \n",
" inputs=\"text\", \n",
" outputs=[\n",
" gr.Dataframe(row_count=1, col_count=11, headers=columns, label=\"Rookie Year Stats\"), \n",
" gr.Textbox(label=\"Prediction\")\n",
" ],\n",
" title=\"Rookie QB Career Prediction (Name)\",\n",
" description=\"Given Name of QB who has played in the NFL, predict their career tier. Uses data from https:\\/\\/www.pro-football-reference.com. Tiers based on PFR Approximate Value.\",\n",
" article=\"See more details at https://github.com/mhrice/Rookie-QB-Predictions\"\n",
" examples=[\"Tom Brady\", \"Joe Burrow\", \"Trevor Lawrence\"]\n",
" )\n",
"\n",
"demo.launch()"
]
},
{
"cell_type": "code",
"execution_count": 72,
"id": "46d819f7",
"metadata": {},
"outputs": [],
"source": [
"def predict2(data):\n",
" row = data.drop(\"Name\", axis=1).astype(float)\n",
" row[\"Cmp\"] = row[\"Att\"].item() * row[\"Cmp%\"].item()\n",
" pred_row, clas, probs = learn.predict(row.iloc[0])\n",
" prediction = pred_row.decode()[\"Tier\"].item() \n",
" return prediction\n"
]
},
{
"cell_type": "code",
"execution_count": 80,
"id": "dd0aae3a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7865/\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7865/\" width=\"900\" height=\"500\" allow=\"autoplay; camera; microphone;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(<gradio.routes.App at 0x26e10daa310>, 'http://127.0.0.1:7865/', None)"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demo2 = gr.Interface(fn=predict2, \n",
" inputs=gr.Dataframe(row_count=1, col_count=8, headers=[x for x in columns if x not in [\"Cmp\", \"G\", \"GS\"]], label=\"Rookie Year Stats\"), \n",
" outputs=gr.Textbox(label=\"Prediction\"),\n",
" title=\"Rookie QB Career Prediction (Stats)\",\n",
" description=\"Given stats of a presumed rookie QB, predict their career tier. Uses data from https:\\/\\/www.pro-football-reference.com. Tiers based on PFR Approximate Value.\"\n",
" article=\"See more details at https://github.com/mhrice/Rookie-QB-Predictions\"\n",
" )\n",
"\n",
"demo2.launch()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5c7e8cbe",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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