Update app.py

app.py

@@ -1,67 +1 @@
-# This is a small and fast sklearn model, so the run-gradio script trains a model and deploys it
-
-import pandas as pd
-import numpy as np
-import sklearn
-import gradio as gr
-from sklearn import preprocessing
-from sklearn.model_selection import train_test_split
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.metrics import accuracy_score
-
-data = pd.read_csv('https://raw.githubusercontent.com/gradio-app/titanic/master/train.csv')
-data.head()
-
-def encode_ages(df): # Binning ages 
-    df.Age = df.Age.fillna(-0.5)
-    bins = (-1, 0, 5, 12, 18, 25, 35, 60, 120)
-    categories = pd.cut(df.Age, bins, labels=False)
-    df.Age = categories
-    return df
-
-def encode_fares(df): # Binning fares
-    df.Fare = df.Fare.fillna(-0.5)
-    bins = (-1, 0, 8, 15, 31, 1000)
-    categories = pd.cut(df.Fare, bins, labels=False)
-    df.Fare = categories
-    return df
-
-def encode_sex(df):
-    mapping = {"male": 0, "female": 1}
-    return df.replace({'Sex': mapping})
-
-def transform_features(df):
-    df = encode_ages(df)
-    df = encode_fares(df)
-    df = encode_sex(df)
-    return df
-
-train = data[['PassengerId', 'Fare', 'Age', 'Sex', 'Survived']]
-train = transform_features(train)
-train.head()
-
-
-X_all = train.drop(['Survived', 'PassengerId'], axis=1)
-y_all = train['Survived']
-
-num_test = 0.20
-X_train, X_test, y_train, y_test = train_test_split(X_all, y_all, test_size=num_test, random_state=23)
-
-clf = RandomForestClassifier()
-clf.fit(X_train, y_train)
-predictions = clf.predict(X_test)
-
-def predict_survival(sex, age, fare):
-    df = pd.DataFrame.from_dict({'Sex': [sex], 'Age': [age], 'Fare': [fare]})
-    df = encode_sex(df)
-    df = encode_fares(df)
-    df = encode_ages(df)
-    pred = clf.predict_proba(df)[0]
-    return {'Perishes': float(pred[0]), 'Survives': float(pred[1])}
-    
-sex = gr.inputs.Radio(['female', 'male'], label="Sex")
-age = gr.inputs.Slider(minimum=0, maximum=120, default=22, label="Age")
-fare = gr.inputs.Slider(minimum=0, maximum=200, default=100, label="Fare (british pounds)")
-
-gr.Interface(predict_survival, [sex, age, fare], "label", live=True, thumbnail="https://raw.githubusercontent.com/gradio-app/hub-titanic/master/thumbnail.png", analytics_enabled=False,
-    title="Surviving the Titanic", description="What is the probability that a passenger on the Titanic would survive the famous wreck? It depends on their demographics as this live interface demonstrates.").launch();
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