Spaces:

bscordoba
/

Goals

Sleeping

App Files Files Community

Goals / app.py

bscordoba

Upload 3 files

d99cd76 verified 3 months ago

raw history blame contribute delete

No virus

5.48 kB

	### ----------------------------- ###
	### libraries ###
	### ----------------------------- ###

	import gradio as gr
	import pandas as pd
	import numpy as np
	from sklearn.model_selection import train_test_split
	from sklearn.linear_model import LogisticRegression
	from sklearn import metrics


	### ------------------------------ ###
	### data transformation ###
	### ------------------------------ ###

	# load dataset
	uncleaned_data = pd.read_csv('data.csv')

	# remove timestamp from dataset (always first column)
	uncleaned_data = uncleaned_data.iloc[: , 1:]
	data = pd.DataFrame()

	# keep track of which columns are categorical and what
	# those columns' value mappings are
	# structure: {colname1: {...}, colname2: {...} }
	cat_value_dicts = {}
	final_colname = uncleaned_data.columns[len(uncleaned_data.columns) - 1]

	# for each column...
	for (colname, colval) in uncleaned_data.iteritems():

	# check if col is already a number; if so, add col directly
	# to new dataframe and skip to next column
	if isinstance(colval.values[0], (np.integer, float)):
	data[colname] = uncleaned_data[colname].copy()
	continue

	# structure: {0: "lilac", 1: "blue", ...}
	new_dict = {}
	val = 0 # first index per column
	transformed_col_vals = [] # new numeric datapoints

	# if not, for each item in that column...
	for (row, item) in enumerate(colval.values):

	# if item is not in this col's dict...
	if item not in new_dict:
	new_dict[item] = val
	val += 1

	# then add numerical value to transformed dataframe
	transformed_col_vals.append(new_dict[item])

	# reverse dictionary only for final col (0, 1) => (vals)
	if colname == final_colname:
	new_dict = {value : key for (key, value) in new_dict.items()}

	cat_value_dicts[colname] = new_dict
	data[colname] = transformed_col_vals


	### -------------------------------- ###
	### model training ###
	### -------------------------------- ###

	# select features and predicton; automatically selects last column as prediction
	cols = len(data.columns)
	num_features = cols - 1
	x = data.iloc[: , :num_features]
	y = data.iloc[: , num_features:]

	# split data into training and testing sets
	x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25)

	# instantiate the model (using default parameters)
	model = LogisticRegression()
	model.fit(x_train, y_train.values.ravel())
	y_pred = model.predict(x_test)


	### -------------------------------- ###
	### article generation ###
	### -------------------------------- ###
	# borrow file reading function from reader.py

	def get_feat():
	feats = [abs(x) for x in model.coef_[0]]
	max_val = max(feats)
	idx = feats.index(max_val)
	return data.columns[idx]

	acc = str(round(metrics.accuracy_score(y_test, y_pred) * 100, 1)) + "%"
	most_imp_feat = get_feat()
	# info = get_article(acc, most_imp_feat)



	### ------------------------------- ###
	### interface creation ###
	### ------------------------------- ###


	# predictor for generic number of features
	def general_predictor(*args):
	features = []

	# transform categorical input
	for colname, arg in zip(data.columns, args):
	if (colname in cat_value_dicts):
	features.append(cat_value_dicts[colname][arg])
	else:
	features.append(arg)

	# predict single datapoint
	new_input = [features]
	result = model.predict(new_input)
	return cat_value_dicts[final_colname][result[0]]

	# add data labels to replace those lost via star-args


	block = gr.Blocks()

	with open('info.md') as f:
	with block:
	gr.Markdown(f.readline())
	gr.Markdown('Take the quiz to get a personalized recommendation using AI.')

	with gr.Row():
	with gr.Group():
	inputls = []
	for colname in data.columns:
	# skip last column
	if colname == final_colname:
	continue

	# access categories dict if data is categorical
	# otherwise, just use a number input
	if colname in cat_value_dicts:
	radio_options = list(cat_value_dicts[colname].keys())
	inputls.append(gr.Dropdown(radio_options, type="value", label=colname))
	else:
	# add numerical input
	inputls.append(gr.Number(label=colname))
	gr.Markdown("<br />")

	submit = gr.Button("Click to see your personalized result!", variant="primary")
	gr.Markdown("<br />")
	output = gr.Textbox(label="Your recommendation:", placeholder="your recommendation will appear here")

	submit.click(fn=general_predictor, inputs=inputls, outputs=output)
	gr.Markdown("<br />")

	with gr.Row():
	with gr.Group():
	gr.Markdown(f"<h3>Accuracy: </h3>{acc}")
	with gr.Group():
	gr.Markdown(f"<h3>Most important feature: </h3>{most_imp_feat}")

	gr.Markdown("<br />")

	with gr.Group():
	gr.Markdown('''⭐ Note that model accuracy is based on the uploaded data.csv and reflects how well the AI model can give correct recommendations for <em>that dataset</em>. Model accuracy and most important feature can be helpful for understanding how the model works, but <em>should not be considered absolute facts about the real world</em>.''')

	with gr.Group():
	with open('info.md') as f:
	f.readline()
	gr.Markdown(f.read())

	# show the interface
	block.launch()