Update app.py

app.py

@@ -1,7 +1,7 @@
 import gradio as gr
 import numpy as np
-from PIL import Image
-import requests
+#from PIL import Image
+#import requests
 import random
 import hopsworks
 import joblib
@@ -17,8 +17,32 @@ model = mr.get_model("titanic_modal_RF", version=1)
 model_dir = model.download()
 model = joblib.load(model_dir + "/titanic_model_RF.pkl")
 
+def encode_features(df):
+  from sklearn.preprocessing import OneHotEncoder, LabelEncoder, StandardScaler
+  import pandas as pd
+  non_numeric_features = ['embarked', 'sex', 'deck', 'title', 'family_size_grouped']
+  for feature in non_numeric_features:
+    df[feature] = LabelEncoder().fit_transform(df[feature])
+
+  
+  cat_features = ['pclass', 'sex', 'deck', 'embarked', 'title', 'family_size_grouped']
+  encoded_features = []
+  for feature in cat_features:
+    encoded_feat = OneHotEncoder().fit_transform(df[feature].values.reshape(-1, 1)).toarray()
+    n = df[feature].nunique()
+    cols = ['{}_{}'.format(feature, n) for n in range(1, n + 1)]
+    encoded_df = pd.DataFrame(encoded_feat, columns=cols)
+    encoded_df.index = df.index
+    encoded_features.append(encoded_df)
+  df = pd.concat([df, *encoded_features], axis=1)
+  drop_cols = ['deck', 'embarked', 'family', 'family_size', 'family_size_grouped',
+             'name', 'parch', 'pclass', 'sex', 'sibsp', 'ticket', 'title']
+
+  df.drop(columns=drop_cols, inplace=True)
+  return df #Return a df with encoded labels
+
 
-def titanic(age, deck, embarked, name, family_name, family_size, family_size_grouped, fare, is_married, parch, sibsp, sex, ticket_number, title):
+def titanic(age, deck, embarked, name, family_size, family_size_grouped, fare, is_married, parch, sibsp, sex, ticket_number, title):
 
     pclass = 0
     if deck == "ABC":
@@ -52,34 +76,10 @@ def titanic(age, deck, embarked, name, family_name, family_size, family_size_gro
          })
     return df
 
-def encode_features(df):
-  from sklearn.preprocessing import OneHotEncoder, LabelEncoder, StandardScaler
-  import pandas as pd
-  non_numeric_features = ['embarked', 'sex', 'deck', 'title', 'family_size_grouped']
-  for feature in non_numeric_features:
-    df[feature] = LabelEncoder().fit_transform(df[feature])
-
-  
-  cat_features = ['pclass', 'sex', 'deck', 'embarked', 'title', 'family_size_grouped']
-  encoded_features = []
-  for feature in cat_features:
-    encoded_feat = OneHotEncoder().fit_transform(df[feature].values.reshape(-1, 1)).toarray()
-    n = df[feature].nunique()
-    cols = ['{}_{}'.format(feature, n) for n in range(1, n + 1)]
-    encoded_df = pd.DataFrame(encoded_feat, columns=cols)
-    encoded_df.index = df.index
-    encoded_features.append(encoded_df)
-  df = pd.concat([df, *encoded_features], axis=1)
-  drop_cols = ['deck', 'embarked', 'family', 'family_size', 'family_size_grouped',
-             'name', 'parch', 'pclass', 'sex', 'sibsp', 'ticket', 'title']
-
-  df.drop(columns=drop_cols, inplace=True)
-  return df #Return a df with encoded labels
-
-def predictor(age, deck, embarked, name, family_name, family_size, family_size_grouped, fare, is_married, parch, sibsp, sex, ticket_number, title):
+def predictor(age, deck, embarked, name, family_size, family_size_grouped, fare, is_married, parch, sibsp, sex, ticket_number, title):
     feature_view = fs.get_feature_view(name="titanic_modal_2", version=1)
     batch_data = feature_view.get_batch_data()
-    row = titanic(age, deck, embarked, name, family_name, family_size, family_size_grouped, fare, is_married, parch, sibsp, sex, ticket_number, title)
+    row = titanic(age, deck, embarked, name, family_size, family_size_grouped, fare, is_married, parch, sibsp, sex, ticket_number, title)
     batch_data = batch_data.append(row)
     encoded = encode_features(batch_data)
     y_pred = model.predict(encoded)
@@ -87,7 +87,7 @@ def predictor(age, deck, embarked, name, family_name, family_size, family_size_g
     if prediction == 0:
         survived = "This person survived"
     if prediction == 1:
-        survived = "This person did not survived"
+        survived = "This person did not survive"
     return survived
 
 demo = gr.Interface(
@@ -100,7 +100,7 @@ demo = gr.Interface(
         gr.inputs.Textbox(label="Deck (ABC, DE, FG, or M)"),
         gr.inputs.Textbox(label="Embarked (S, C, or Q)"),
         gr.inputs.Textbox(label="Name of person"),
-        gr.inputs.Textbox(label="Family name"),
+        #gr.inputs.Textbox(label="Family name"),
         gr.inputs.Number(label="Size of family  (integer)"),
         gr.inputs.Textbox(label="Family Size (Alone, Small, Medium or Large)"),
         gr.inputs.Number(label="Fare (Integer)"),