app.py

import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
import joblib

# Mock dataset
data = {
    'age': [25, 32, 47, 51, 29, 45, 35, 33, 29, 24],
    'education_level': [16, 18, 20, 21, 16, 18, 17, 16, 16, 15],
    'experience': [1, 6, 20, 25, 3, 15, 8, 4, 2, 1],
    'salary': [30000, 50000, 120000, 140000, 35000, 110000, 60000, 52000, 40000, 32000]
}
df = pd.DataFrame(data)

# Split dataset
X = df[['age', 'education_level', 'experience']]
y = df['salary']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train model
model = LinearRegression()
model.fit(X_train, y_train)

# Evaluate model
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print(f"Model MSE: {mse}")

# Save model
joblib.dump(model, 'salary_model.joblib')


import gradio as gr
import joblib

# Load the trained model
model = joblib.load('salary_model.joblib')

# Define prediction function
def predict_salary(age, education_level, experience):
    input_data = [[age, education_level, experience]]
    prediction = model.predict(input_data)
    return f"Predicted Salary: ${prediction[0]:.2f}"

# Create Gradio interface
demo = gr.Interface(
    fn=predict_salary,
    inputs=[
        gr.Number(label="Age"),
        gr.Number(label="Education Level (years)"),
        gr.Number(label="Experience (years)")
    ],
    outputs="text",
    title="Salary Prediction Model",
    description="Predict salary based on age, education level, and years of experience."
)

# Launch the Gradio app
demo.launch()