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import gradio as gr
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import requests
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import pandas as pd
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def clean_data(data):
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job_map = {
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"admin.": 0,
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"blue-collar": 1,
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"entrepreneur": 2,
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"housemaid": 3,
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"management": 4,
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"retired": 5,
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"self-employed": 6,
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"services": 7,
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"student": 8,
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"technician": 9,
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"unemployed": 10,
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"unknown": 11
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}
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marital_map = {
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"divorced": 0,
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"married": 1,
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"single": 2,
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"unknown": 3
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}
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education_map = {
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"primary": 0,
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"secondary": 1,
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"tertiary": 2,
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"unknown": 3
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}
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default_map = {
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"no": 0,
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"yes": 1,
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"unknown": 2
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}
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housing_map = {
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"no": 0,
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"yes": 1,
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"unknown": 2
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}
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loan_map = {
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"no": 0,
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"yes": 1,
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"unknown": 2
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}
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contact_map = {
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"cellular": 0,
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"telephone": 1,
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"unknown": 2
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}
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month_map = {
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"apr": 0,
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"aug": 1,
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"dec": 2,
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"feb": 3,
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"jan": 4,
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"jul": 5,
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"jun": 6,
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"mar": 7,
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"may": 8,
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"nov": 9,
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"oct": 10,
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"sep": 11
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}
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poutcome_map = {
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"failure": 0,
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"nonexistent": 1,
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"success": 2,
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"unknown": 3
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}
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cleaned_data = {}
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cleaned_data["age"] = data[0]
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cleaned_data["job"] = job_map.get(data[1], 11)
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cleaned_data["marital"] = marital_map.get(data[2], 3)
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cleaned_data["education"] = education_map.get(data[3], 3)
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cleaned_data["default"] = default_map.get(data[4], 2)
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cleaned_data["balance"] = data[5] / 1000
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cleaned_data["housing"] = housing_map.get(data[6], 2)
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cleaned_data["loan"] = loan_map.get(data[7], 2)
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cleaned_data["contact"] = contact_map.get(data[8], 2)
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cleaned_data["day"] = data[9]
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cleaned_data["month"] = month_map.get(data[10], 11)
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cleaned_data["duration"] = data[11] / 100
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cleaned_data["campaign"] = data[12]
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cleaned_data["pdays"] = data[13] / 100
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cleaned_data["previous"] = data[14]
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cleaned_data["poutcome"] = poutcome_map.get(data[15], 3)
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print("Cleaned Data:")
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print(cleaned_data)
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return cleaned_data
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def predict(age, job, marital, education, default, balance, housing, loan, contact, day, month, duration, campaign, pdays, previous, poutcome):
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cleaned_data = clean_data([age, job, marital, education, default, balance, housing, loan, contact, day, month, duration, campaign, pdays, previous, poutcome])
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url = "http://localhost:8000/predict/"
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api_data = {"features": list(cleaned_data.values())}
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print("API Request:")
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print(api_data)
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response = requests.post(url, json=api_data)
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prediction = response.json()["prediction"][0]
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return prediction
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demo = gr.Interface(
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fn=predict,
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inputs=[
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gr.Number(label="Age"),
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gr.Text(label="Job"),
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gr.Text(label="Marital"),
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gr.Text(label="Education"),
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gr.Text(label="Default"),
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gr.Number(label="Balance"),
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gr.Text(label="Housing"),
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gr.Text(label="Loan"),
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gr.Text(label="Contact"),
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gr.Number(label="Day"),
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gr.Text(label="Month"),
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gr.Number(label="Duration"),
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gr.Number(label="Campaign"),
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gr.Number(label="Pdays"),
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gr.Number(label="Previous"),
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gr.Text(label="Poutcome"),
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],
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outputs=gr.Text(label="Prediction"),
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title="Bank Marketing Prediction",
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description="This is a demo for bank marketing prediction. Please enter the required information to get the prediction."
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)
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if __name__ == "__main__":
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demo.launch() |
