app.py

import pandas as pd
data =pd.read_csv("diabetes.csv")

data.head()

import seaborn as sns
sns.pairplot(data)




X = data.drop('Outcome', axis=1)
y = data['Outcome']


from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)


from sklearn.linear_model import LogisticRegression

# Create a Logistic Regression model
model = LogisticRegression()

# Train the model
model.fit(X_train, y_train)

# Make predictions on the test set
y_pred = model.predict(X_test)

# accuracy 
from sklearn.metrics import accuracy_score
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)


def predict_diabetes(Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age):


  # Create a list of input features
  input_data = [[Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age]]

  # Make a prediction
  prediction = model.predict(input_data)[0]

  # Return the result
  if prediction == 1:
    return "The person is diabetic."
  else:
    return "The person is healthy."

result = predict_diabetes(6, 148, 72, 35, 0, 33.6, 0.627, 50)
print(result)





import gradio as gr

# Create a Gradio interface
iface = gr.Interface(
    fn=predict_diabetes,
    inputs=[
        gr.Number(label="Pregnancies"), # Changed gr.inputs.Number to gr.Number
        gr.Number(label="Glucose"), # Changed gr.inputs.Number to gr.Number
        gr.Number(label="BloodPressure"), # Changed gr.inputs.Number to gr.Number
        gr.Number(label="SkinThickness"), # Changed gr.inputs.Number to gr.Number
        gr.Number(label="Insulin"), # Changed gr.inputs.Number to gr.Number
        gr.Number(label="BMI"), # Changed gr.inputs.Number to gr.Number
        gr.Number(label="DiabetesPedigreeFunction"), # Changed gr.inputs.Number to gr.Number
        gr.Number(label="Age") # Changed gr.inputs.Number to gr.Number
    ],
    outputs="text",
    title="Diabetes Prediction",
    description="Enter the patient's details to predict diabetes."
)

# Launch the interface
iface.launch(share=True)