laineeCronch/Colorful

1### ----------------------------- ###2###           libraries           ###3### ----------------------------- ###4 5import gradio as gr6import pandas as pd7import numpy as np8from sklearn.model_selection import train_test_split9from sklearn.linear_model import LogisticRegression10from sklearn import metrics11 12 13### ------------------------------ ###14###       data transformation      ###15### ------------------------------ ###16 17# load dataset18uncleaned_data = pd.read_csv('data.csv')19 20# remove timestamp from dataset (always first column)21uncleaned_data = uncleaned_data.iloc[: , 1:]22data = pd.DataFrame()23 24# keep track of which columns are categorical and what 25# those columns' value mappings are26# structure: {colname1: {...}, colname2: {...} }27cat_value_dicts = {}28final_colname = uncleaned_data.columns[len(uncleaned_data.columns) - 1]29 30# for each column...31for (colname, colval) in uncleaned_data.iteritems():32 33  # check if col is already a number; if so, add col directly34  # to new dataframe and skip to next column35  if isinstance(colval.values[0], (np.integer, float)):36    data[colname] = uncleaned_data[colname].copy()37    continue38 39  # structure: {0: "lilac", 1: "blue", ...}40  new_dict = {}41  val = 0 # first index per column42  transformed_col_vals = [] # new numeric datapoints43 44  # if not, for each item in that column...45  for (row, item) in enumerate(colval.values):46    47    # if item is not in this col's dict...48    if item not in new_dict:49      new_dict[item] = val50      val += 151    52    # then add numerical value to transformed dataframe53    transformed_col_vals.append(new_dict[item])54  55  # reverse dictionary only for final col (0, 1) => (vals)56  if colname == final_colname:57    new_dict = {value : key for (key, value) in new_dict.items()}58 59  cat_value_dicts[colname] = new_dict60  data[colname] = transformed_col_vals61 62 63### -------------------------------- ###64###           model training         ###65### -------------------------------- ###66 67# select features and predicton; automatically selects last column as prediction68cols = len(data.columns)69num_features = cols - 170x = data.iloc[: , :num_features]71y = data.iloc[: , num_features:]72 73# split data into training and testing sets74x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25)75 76# instantiate the model (using default parameters)77model = LogisticRegression()78model.fit(x_train, y_train.values.ravel())79y_pred = model.predict(x_test)80 81 82### -------------------------------- ###83###        article generation        ###84### -------------------------------- ###85# borrow file reading function from reader.py86 87def get_feat():88  feats = [abs(x) for x in model.coef_[0]]89  max_val = max(feats)90  idx = feats.index(max_val)91  return data.columns[idx]92  93acc = str(round(metrics.accuracy_score(y_test, y_pred) * 100, 1)) + "%"94most_imp_feat = get_feat()95# info = get_article(acc, most_imp_feat)96 97 98 99### ------------------------------- ###100###        interface creation       ###101### ------------------------------- ###102 103 104# predictor for generic number of features105def general_predictor(*args):106  features = []107 108  # transform categorical input109  for colname, arg in zip(data.columns, args):110    if (colname in cat_value_dicts):111      features.append(cat_value_dicts[colname][arg])112    else:113      features.append(arg)114 115  # predict single datapoint116  new_input = [features]117  result = model.predict(new_input)118  return cat_value_dicts[final_colname][result[0]]119 120# add data labels to replace those lost via star-args121 122 123block = gr.Blocks()124 125with open('info.md') as f:126  with block:127    gr.Markdown(f.readline())128    gr.Markdown('Take the quiz to get a personalized recommendation using AI.')129    130    with gr.Row():131      with gr.Group():132        inputls = []133        for colname in data.columns:134          # skip last column135          if colname == final_colname:136            continue137          138          # access categories dict if data is categorical139          # otherwise, just use a number input140          if colname in cat_value_dicts:141            radio_options = list(cat_value_dicts[colname].keys())142            inputls.append(gr.Dropdown(radio_options, type="value", label=colname))143          else:144            # add numerical input145            inputls.append(gr.Number(label=colname))146          gr.Markdown("<br />")147        148        submit = gr.Button("Click to see your personalized result!", variant="primary")149        gr.Markdown("<br />")150        output = gr.Textbox(label="Your recommendation:", placeholder="your recommendation will appear here")151        152        submit.click(fn=general_predictor, inputs=inputls, outputs=output)153        gr.Markdown("<br />")154        155        with gr.Row():156          with gr.Group():157            gr.Markdown(f"<h3>Accuracy: </h3>{acc}")158          with gr.Group():159            gr.Markdown(f"<h3>Most important feature: </h3>{most_imp_feat}")160        161        gr.Markdown("<br />")162        163        with gr.Group():164          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>.''')165        166      with gr.Group():167        with open('info.md') as f:168          f.readline()169          gr.Markdown(f.read())170 171# show the interface172block.launch()