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e6966a4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | #gradio app
import gradio as gr
import pandas as pd
import pickle
import numpy as np
# 1. Load the Model
with open("student_rf_pipeline.pkl", "rb") as f:
model = pickle.load(f)
# 2. The Logic Function
def predict_gpa(gender, age, address, famsize,
Pstatus, M_Edu, F_Edu, M_Job, F_Job,
relationship, smoker, tuition_fee, time_friends,
ssc_result):
# Pack inputs into a DataFrame
# The column names must match your CSV file exactly
input_df = pd.DataFrame([[
gender, age, address, famsize, Pstatus,
M_Edu, F_Edu, M_Job, F_Job, relationship,
smoker, tuition_fee, time_friends, ssc_result
]],
columns=[
'gender', 'age', 'address', 'famsize', 'Pstatus',
'M_Edu', 'F_Edu', 'M_Job', 'F_Job', 'relationship',
'smoker', 'tuition_fee', 'time_friends', 'ssc_result'
])
# Predict
prediction = model.predict(input_df)[0]
# Return formatted result (Clipped 0-5)
return f"Predicted HSC Result: {np.clip(prediction, 0, 5):.2f}"
# 3. The App Interface
# Defining inputs in a list to keep it clean
inputs = [
gr.Radio(["M", "F"], label="Gender"),
gr.Number(label="Age", value=18),
gr.Radio(["Urban", "Rural"], label="Address"),
gr.Radio(["GT3", "LE3"], label="Family Size"),
gr.Radio(["Together", "Apart"], label="Parent Status"),
gr.Slider(0, 4, step=1, label="Mother's Edu"),
gr.Slider(0, 4, step=1, label="Father's Edu"),
gr.Dropdown(["At_home", "Health", "Other", "Services",
"Teacher"], label="Mother's Job"),
gr.Dropdown(["Teacher", "Other", "Services", "Health",
"Business", "Farmer"], label="Father's Job"),
gr.Radio(["Yes", "No"], label="Relationship"),
gr.Radio(["Yes", "No"], label="Smoker"),
gr.Number(label="Tuition Fee"),
gr.Slider(1, 5, step=1, label="Time with Friends"),
gr.Number(label="SSC Result (GPA)")
]
app = gr.Interface(
fn=predict_gpa,
inputs=inputs,
outputs="text",
title="HSC Predictor")
app.launch(share=True)
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