Duplicate from gradio-tests/titanic-survival

.gitattributes

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+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tar.gz filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+title: Titanic Survival in Real Time
+emoji: 🚀
+colorFrom: blue
+colorTo: green
+sdk: gradio
+sdk_version: 3.23.1b1
+app_file: app.py
+pinned: false
+duplicated_from: gradio-tests/titanic-survival
+---
+
+# Configuration
+
+`title`: _string_  
+Display title for the Space
+
+`emoji`: _string_  
+Space emoji (emoji-only character allowed)
+
+`colorFrom`: _string_  
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`colorTo`: _string_  
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`sdk`: _string_  
+Can be either `gradio` or `streamlit`
+
+`app_file`: _string_  
+Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
+Path is relative to the root of the repository.
+
+`pinned`: _boolean_  
+Whether the Space stays on top of your list.

app.py

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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();

requirements.txt

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+pandas
+numpy
+scikit-learn==1.0.2