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README.md

@@ -6,7 +6,6 @@ colorFrom: indigo
 colorTo: indigo
 sdk: gradio
 sdk_version: 3.4.1
-
-app_file: app.py
+app_file: run.py
 pinned: false
 ---

run.py

@@ -0,0 +1,113 @@
+import os
+
+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+
+import gradio as gr
+
+current_dir = os.path.dirname(os.path.realpath(__file__))
+data = pd.read_csv(os.path.join(current_dir, "files/titanic.csv"))
+
+
+def encode_age(df):
+    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_fare(df):
+    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_df(df):
+    df = encode_age(df)
+    df = encode_fare(df)
+    sex_mapping = {"male": 0, "female": 1}
+    df = df.replace({"Sex": sex_mapping})
+    embark_mapping = {"S": 1, "C": 2, "Q": 3}
+    df = df.replace({"Embarked": embark_mapping})
+    df.Embarked = df.Embarked.fillna(0)
+    df["Company"] = 0
+    df.loc[(df["SibSp"] > 0), "Company"] = 1
+    df.loc[(df["Parch"] > 0), "Company"] = 2
+    df.loc[(df["SibSp"] > 0) & (df["Parch"] > 0), "Company"] = 3
+    df = df[
+        [
+            "PassengerId",
+            "Pclass",
+            "Sex",
+            "Age",
+            "Fare",
+            "Embarked",
+            "Company",
+            "Survived",
+        ]
+    ]
+    return df
+
+
+train = encode_df(data)
+
+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(passenger_class, is_male, age, company, fare, embark_point):
+    if passenger_class is None or embark_point is None:
+        return None
+    df = pd.DataFrame.from_dict(
+        {
+            "Pclass": [passenger_class + 1],
+            "Sex": [0 if is_male else 1],
+            "Age": [age],
+            "Company": [
+                (1 if "Sibling" in company else 0) + (2 if "Child" in company else 0)
+            ],
+            "Fare": [fare],
+            "Embarked": [embark_point + 1],
+        }
+    )
+    df = encode_age(df)
+    df = encode_fare(df)
+    pred = clf.predict_proba(df)[0]
+    return {"Perishes": float(pred[0]), "Survives": float(pred[1])}
+
+
+demo = gr.Interface(
+    predict_survival,
+    [
+        gr.Dropdown(["first", "second", "third"], type="index"),
+        "checkbox",
+        gr.Slider(0, 80, value=25),
+        gr.CheckboxGroup(["Sibling", "Child"], label="Travelling with (select all)"),
+        gr.Number(value=20),
+        gr.Radio(["S", "C", "Q"], type="index"),
+    ],
+    "label",
+    examples=[
+        ["first", True, 30, [], 50, "S"],
+        ["second", False, 40, ["Sibling", "Child"], 10, "Q"],
+        ["third", True, 30, ["Child"], 20, "S"],
+    ],
+    interpretation="default",
+    live=True,
+)
+
+if __name__ == "__main__":
+    demo.launch()