| import gradio as gr | |
| import pandas as pd | |
| import pickle | |
| import numpy as np | |
| with open('svm_model.pkl', 'rb') as f: | |
| svm_model_pipeline = pickle.load(f) | |
| def predict_purchase(Gender, Age, EstimatedSalary): | |
| input_df = pd.DataFrame([[ | |
| Gender, Age, EstimatedSalary | |
| ]], | |
| columns = [ | |
| 'Gender', 'Age', 'EstimatedSalary' | |
| ]) | |
| prediction = svm_model_pipeline.predict(input_df)[0] | |
| return "Purchased" if prediction == 1 else "Not Purchased" | |
| inputs = [ | |
| gr.Radio(["Male", "Female"], label="Gender"), | |
| gr.Number(label="Age"), | |
| gr.Number(label="Estimated Salary") | |
| ] | |
| app = gr.Interface( | |
| fn = predict_purchase, | |
| inputs = inputs, | |
| outputs = "text", | |
| title = "Social Network Ads Purchase Prediction" | |
| ) | |
| app.launch(share=True) |