import gradio as gr from fastai.tabular.all import * import pandas as pd import torch df_framework = pd.read_csv('/kaggle/input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv') splits = RandomSplitter(seed=42)(df_framework) dls = TabularPandas( df_framework, splits=splits, procs = [Categorify, FillMissing, Normalize], cat_names=["gender","ever_married","work_type","Residence_type", "smoking_status"], cont_names=['age', 'hypertension', 'heart_disease', 'avg_glucose_level', 'bmi'], y_names="stroke", y_block = CategoryBlock(), ).dataloaders(path=".") learn = tabular_learner(dls, metrics=accuracy, layers=[10,10]) learn.fit(16, lr=0.025) def predict(age, hypertension, heart_disease, avg_glucose_level, bmi, gender, married, work_type, residence_type, smoking_status): data = [[gender, age, hypertension, heart_disease, married, work_type, residence_type, avg_glucose_level, bmi, smoking_status]] columns_df = ['gender', 'age', 'hypertension', 'heart_disease', 'ever_married', 'work_type', 'Residence_type', 'avg_glucose_level', 'bmi','smoking_status'] df_row = pd.DataFrame(data,columns=columns_df) dl = learn.dls.test_dl(df_row) preds,_ = learn.get_preds(dl=dl) return "O paciente tem a seguinte possibilidade de infarto: " + str(preds[0]) gr.Interface( fn=predict, title="Stroke predict Model", allow_flagging="never", share=True, inputs=[ gr.inputs.Number(default=30, label="Age"), gr.Dropdown(["Yes", "No"], label="Hypertension"), gr.Dropdown(["Yes", "No"], label="Heart disease"), gr.inputs.Number(default=100, label="Average glucose level"), gr.inputs.Number(default=28.8, label="Body Mass Index (BMI)"), gr.inputs.Radio(choices=["Female", "Male", "Other"], default="Female", label="Gender"), gr.Dropdown(["No", "Yes"], label="Married"), gr.Dropdown(["Governamental", "Never worked", "Private", "Self-employed", "Children"], label="Work type"), gr.Dropdown(["Rural", "Urban"], label="Residence type"), gr.Dropdown(["Unknown", "Formely smoked", "Never smoked", "Smokes"], label="Smoking status"), ], outputs="text").launch()