Delete model.pkl

app.py

@@ -1,78 +0,0 @@
-import gradio as gr
-import dill
-import pandas as pd
-import xgboost as xgb
-import numpy as np
-import pickle
-
-def decode_file(file_path):
-    with open(file_path, 'rb') as file:
-        obj = pickle.load(file)
-    return obj
-
-model = decode_file('model.pkl')
-
-
-def predict(gender, age, hypertension, ever_married, work_type, heart_disease, avg_glucose_level, bmi, smoking_status, Residence_type):
-    # Mapping for categorical variables
-    gender_mapping = {'Male': 1, 'Female': 0}
-    hypertension_mapping = {'Yes': 1, 'No': 0}
-    ever_married_mapping = {'Yes': 1, 'No': 0}
-    work_type_mapping = {'Private': 2, 'Self-employed': 4, 'Govt_job': 3, 'children': 1, 'Never_worked': 0}
-    heart_disease_mapping = {'Yes': 1, 'No': 0}
-    smoking_status_mapping = {'formerly smoked': 3, 'smokes': 1, 'never smoked': 2, 'Unknown': 0}
-    Residence_type_mapping = {'Urban': 1, 'Rural': 0}
-
-    # Map categorical variables to their corresponding numerical values
-    gender = gender_mapping[gender]
-    hypertension = hypertension_mapping[hypertension]
-    ever_married = ever_married_mapping[ever_married]
-    work_type = work_type_mapping[work_type]
-    heart_disease = heart_disease_mapping[heart_disease]
-    smoking_status = smoking_status_mapping[smoking_status]
-    Residence_type = Residence_type_mapping[Residence_type]
-
-    inputs = [gender, age, hypertension, ever_married, work_type, heart_disease, avg_glucose_level, bmi, smoking_status, Residence_type]
-    input_labels = ['gender', 'age', 'hypertension', 'ever_married', 'work_type', 'heart_disease', 'avg_glucose_level', 'bmi', 'smoking_status', 'Residence_type']
-
-    # Convert the input into a pandas DataFrame
-    input_df = pd.DataFrame([inputs], columns=input_labels)
-
-    # Predict the stroke probability
-    prediction = model.predict_proba(input_df)[0][1]
-
-    # Return the prediction
-    result = "The probability of stroke is {:.2f}%".format(prediction * 100)  # to give a percentage
-    return result
-
-
-
-
-
-
-input_labels = [
-    'gender', 'age', 'hypertension', 'ever_married', 'work_type', 
-    'heart_disease', 'avg_glucose_level', 'bmi', 'smoking_status', 'Residence_type'
-]
-# Create the Gradio interface
-iface = gr.Interface(
-    fn=predict,
-    inputs=[
-        gr.components.Radio(choices=['Female', 'Male'], label="Gender"),
-        gr.components.Slider(label="Age"),
-        gr.components.Radio(choices=['Yes', 'No'], label="Hypertension"),
-        gr.components.Radio(choices=['Yes', 'No'], label="Ever Married"),
-        gr.components.Radio(choices=['Private', 'Self-employed', 'Govt_job', 'children', 'Never_worked'], label="Work Type"),
-        gr.components.Radio(choices=['Yes', 'No'], label="Heart Disease"),
-        gr.components.Number(label="Average Glucose Level"),
-        gr.components.Slider(label="BMI"),
-        gr.components.Radio(choices=['formerly smoked', 'never smoked', 'smokes', 'Unknown'], label="Smoking Status"),
-        gr.components.Radio(choices=['Urban', 'Rural'], label="Residence Type")
-    ],
-    outputs='text',
-    title='Stroke Probability Predictor',
-    description='Predicts the probability of having a stroke based on input features.'
-)
-
-
-iface.launch()