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import gradio as gr
import pandas as pd
import numpy as np
import zipfile
import os
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, roc_curve, auc
# Extract ZIP File
zip_path = "DIABETES_PREDICTION.zip"
extract_path = "diabetes_data"
with zipfile.ZipFile(zip_path, 'r') as zip_ref:
zip_ref.extractall(extract_path)
# Load Dataset
csv_file_path = os.path.join(extract_path, "diabetes_prediction_dataset.csv")
df = pd.read_csv(csv_file_path)
# Data Preprocessing
df['smoking_history'] = df['smoking_history'].map({'never':0, 'No Info':1, 'current':2, 'former':3, 'ever':4, 'not current':5})
df['gender'] = df['gender'].map({'Male': 0, 'Female': 1})
# Feature Selection
target = 'diabetes'
X = df.drop(columns=[target])
y = df[target]
# Normalize Features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Train-Test Split
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)
# Train Model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
# Define Prediction Function
def predict_diabetes(gender, age, hypertension, heart_disease, smoking_history, bmi, HbA1c, blood_glucose):
# Convert categorical inputs to numeric
gender_numeric = 1 if gender == "Male" else 0 # Encode 'Male' as 1, 'Female' as 0
# Prepare the input as a NumPy array
input_data = np.array([[gender_numeric, age, hypertension, heart_disease, smoking_history, bmi, HbA1c, blood_glucose]])
# Scale the input data
input_scaled = scaler.transform(input_data) # Ensure all values are numeric
# Make prediction
prediction = model.predict(input_scaled)
return "Diabetes Positive" if prediction[0] == 1 else "Diabetes Negative"
# Define UI with Gradio
iface = gr.Interface(
fn=predict_diabetes,
inputs=[
gr.Radio(["Male", "Female"], label="Gender"),
gr.Number(label="Age"),
gr.Radio([0, 1], label="Hypertension (0: No, 1: Yes)"),
gr.Radio([0, 1], label="Heart Disease (0: No, 1: Yes)"),
gr.Dropdown([0, 1, 2, 3, 4, 5], label="Smoking History (Encoded)"),
gr.Number(label="BMI"),
gr.Number(label="HbA1c Level"),
gr.Number(label="Blood Glucose Level")
],
outputs=gr.Textbox(label="Diabetes Prediction"),
title="Diabetes Risk Prediction",
description="Enter the patient's details to predict diabetes risk."
)
# Launch App
if __name__ == "__main__":
iface.launch()
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