Upload app.py

app.py

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+## ----------------------------- ###
+###           libraries           ###
+### ----------------------------- ###
+import gradio as gr
+import pandas as pd
+import numpy as np
+import os
+import warnings
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn import metrics
+from reader import get_article
+
+warnings.filterwarnings("ignore")
+
+
+### ------------------------------ ###
+###       data transformation      ###
+### ------------------------------ ###
+# load dataset
+uncleaned_data = pd.read_csv('data.csv')
+
+# remove timestamp from dataset (always first column)
+if uncleaned_data.columns[0].lower() == 'timestamp':
+  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 = {}
+  key = 0 # first index per column
+  transformed_col_vals = [] # new numeric datapoints
+  
+  # if not, for each item in that column...
+  for item in colval.values:
+    
+    # if item is not in this col's dict...
+    if item not in new_dict:
+      new_dict[item] = key
+      key += 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
+num_features = len(data.columns) - 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(multi_class='multinomial', penalty='none', solver='newton-cg')
+model.fit(x_train, y_train.values.ravel())
+y_pred = model.predict(x_test)
+
+
+### -------------------------------- ###
+###            file reading          ###
+### -------------------------------- ###
+# borrow file reading function from reader.py
+info = get_article()
+
+ 
+### ------------------------------- ###
+###        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
+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.Radio(choices=radio_options, type="value", label=colname))
+  else:
+    # add numerical input
+    inputls.append(gr.inputs.Number(label=colname))
+  
+# generate gradio interface
+interface = gr.Interface(general_predictor, inputs=inputls, outputs="text", article=info['article'], css=info['css'], theme='huggingface', title=info['title'], allow_flagging=False, description=info['description'])
+
+# show the interface 
+interface.launch(share=True)