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)