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| # 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.Radio(['female', 'male'], label="Sex", value="male") | |
| age = gr.Slider(minimum=0, maximum=120, default=22, label="Age") | |
| fare = gr.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, | |
| theme="soft", 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(); | |