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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, "data.csv"))
X_all = data.drop(["targets"], axis=1)
y_all = data["targets"]
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(densites, diametres):
df = pd.DataFrame.from_dict(
{
"densites": [densites],
"diametres": [diametres]
}
)
pred = clf.predict_proba(df)[0]
return {"No": float(pred[0]), "Yes": float(pred[1])}
demo = gr.Interface(
predict_survival,
[
gr.Number(value=0),
gr.Number(value=0)
],
"label",
examples=[
[700, 5]
],
live=True,
)
if __name__ == "__main__":
demo.launch(share=True)
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