app.py

import pickle
import gradio as gr
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression

# import sklearn.preprocessing
# with open("diabetes_classifier.pkl", "rb") as file:
#     loaded_model = pickle.load(file)
loaded_model = pickle.load(open("diabetes_classifier.pkl", "rb"), encoding="bytes")
diabetes_classifier = loaded_model['model']
columns = loaded_model['columns']

def predict_diabetes_func(Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age):
    input_data = [Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age]
    input_df = pd.DataFrame([input_data], columns=columns)
    prediction = diabetes_classifier.predict(input_df)
    # return Pregnancies
    return "Positive" if prediction[0] == 1 else "Negative"

iface = gr.Interface( title = "Mashdemy AI Demo _Diabetes Prediction App",
                     description = "Enter the various parameters and click submit to know if the result is Positive or Negative",
    fn=predict_diabetes_func,  # Updated function name
    inputs=[
        gr.Number(label="Pregnancies"),
        gr.Number(label="Glucose"),
        gr.Number(label="BloodPressure"),
        gr.Number(label="SkinThickness"),
        gr.Number(label="Insulin"),
        gr.Number(label="BMI"),
        gr.Number(label="DiabetesPedigreeFunction"),
        gr.Number(label="Age"),
    ],
    outputs="text",
    live=False,
)

iface.launch(share= True, debug = True)