Create app.py

app.py

@@ -0,0 +1,167 @@
+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():
+        with open('info.md') as f:
+          f.readline()
+          gr.Markdown(f.read())
+
+# show the interface
+block.launch()