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app.py

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+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()

requirements.txt

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+gradio
+scikit-learn
+joblib

salary_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6c850ebd628e432cac3fb9007130839457d13b5e284c77118ff69d73dd67c95d
+size 928