Delete app.py

app.py

@@ -1,172 +0,0 @@
-### ----------------------------- ###
-###           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:", placeholder="your recommendation will appear here")
-        
-        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 feature: </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 <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.Box():
-        with open('info.md') as f:
-          f.readline()
-          gr.Markdown(f.read())
-
-# show the interface
-block.launch()