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import gradio as gr
import pandas as pd
from joblib import load
# Load the saved model and encoders
model = load("random_forest_model.pkl")
encoder = load("one_hot_encoder.pkl")
field_encoder = load("label_encoder.pkl")
def preprocess_input(high_school_stream, physics, math, chemistry, biology, economics, geography, history, literature):
"""
Preprocess user input for prediction.
"""
# Create a dictionary for the input data
user_data = {
"High_School_Stream": high_school_stream,
"Physics": physics,
"Math": math,
"Chemistry": chemistry,
"Biology": biology,
"Economics": economics,
"Geography": geography,
"History": history,
"Literature": literature,
}
# Convert to DataFrame
user_df = pd.DataFrame([user_data])
# One-hot encode High_School_Stream
stream_encoded = encoder.transform(user_df[["High_School_Stream"]])
stream_encoded_df = pd.DataFrame(stream_encoded, columns=encoder.get_feature_names_out(["High_School_Stream"]))
# Combine encoded features with subject scores
user_processed = pd.concat([stream_encoded_df, user_df.iloc[:, 1:]], axis=1)
return user_processed
def predict(high_school_stream, physics, math, chemistry, biology, economics, geography, history, literature):
"""
Make a prediction using the trained model.
"""
# Preprocess input
user_processed = preprocess_input(high_school_stream, physics, math, chemistry, biology, economics, geography, history, literature)
# Make prediction
y_pred_encoded = model.predict(user_processed)
y_pred = field_encoder.inverse_transform(y_pred_encoded)
return y_pred[0]
# Define Gradio interface
inputs = [
gr.Dropdown(label="High School Stream", choices=["PCM", "PCB", "MEG", "MPG", "HGL"]),
gr.Number(label="Physics Score (0 if not applicable)"),
gr.Number(label="Math Score (0 if not applicable)"),
gr.Number(label="Chemistry Score (0 if not applicable)"),
gr.Number(label="Biology Score (0 if not applicable)"),
gr.Number(label="Economics Score (0 if not applicable)"),
gr.Number(label="Geography Score (0 if not applicable)"),
gr.Number(label="History Score (0 if not applicable)"),
gr.Number(label="Literature Score (0 if not applicable)"),
]
output = gr.Textbox(label="Predicted Field")
# Create Gradio app
app = gr.Interface(
fn=predict,
inputs=inputs,
outputs=output,
title="Student Field Prediction",
description="Enter your details to predict the recommended field of study.",
)
# Launch the app
app.launch() |