app.py

import pandas as pd
import numpy as np
import sklearn as sn
from datasets import Dataset

df=pd.read_csv("https://huggingface.co/spaces/Ralmao/Anemia/raw/main/Flujo_anemia.csv",encoding='latin-1', on_bad_lines='skip')
dataset= Dataset.from_pandas(df)
X = df.drop(['Flujo_Type'], axis = 1)
y = df['Flujo_Type']

from sklearn.model_selection import train_test_split

X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.30,random_state=42)

from sklearn.ensemble import RandomForestClassifier

clf = RandomForestClassifier(n_estimators=10)
clf = clf.fit(X_train, y_train)
clf

import gradio as gr

def predict_Flujotype(Hemoglobina):
    x = np.array([Hemoglobina])
    pred = clf.predict(x.reshape(1, -1))

    if pred == 1:
        return "Paciente con anemia empezar con un flujo de 220 e ir incrementando poco a poco hasta llegar a 300"
    else:
        return "Paciente sin anemia empezar con un flujo de 250 e ir incrementando poco a poco hasta llegar a 300"


Hemoglobina = gr.Number(label='Hemoglobina')
output = gr.Textbox(label='Flujo_Type')
app = gr.Interface(predict_Flujotype,inputs = [Hemoglobina],outputs=output, description= 'This is a Flujo Type Predictor')
app.launch(share=True)