app.py

### ----------------------------- ###
###           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.Box():
        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.inputs.Dropdown(choices=radio_options, type="value", label=colname))
          else:
            # add numerical input
            inputls.append(gr.inputs.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:")
        
        submit.click(fn=general_predictor, inputs=inputls, outputs=output)
        gr.Markdown("<br />")
        
        with gr.Row():
          with gr.Box():
            gr.Markdown(f"<h3>Accuracy: </h3>{acc}")
          with gr.Box():
            gr.Markdown(f"<h3>Most important question: </h3>{most_imp_feat}")
        
        gr.Markdown("<br />")
        
        with gr.Box():
          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 that dataset. Model accuracy and most important feature can be helpful for understanding how the model works, but should not be considered absolute facts about the real world.''')
        
      with gr.Box():
        with open('info.md') as f:
          f.readline()
          gr.Markdown(f.read())

# show the interface
block.launch()