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import gradio as gr
import joblib
import json
import lightgbm as lgb
from sentence_transformers import SentenceTransformer
import numpy as np
# Load models from Hugging Face Hub
ridge = joblib.load("Essay/ridge_model.pkl")
lgb_model = lgb.Booster(model_file="Essay/lightgbm_model.txt")
encoder = joblib.load("Essay/scaler_encoder.pkl")
# Load metadata column order
with open("Essay/metadata_columns.json", "r") as f:
metadata_columns = json.load(f)
# Load SBERT model (will download at runtime)
sbert = SentenceTransformer("sentence-transformers/paraphrase-mpnet-base-v2")
def predict_score(essay_text, gender, race_ethnicity, disability, disadvantaged, ell_status):
# Encode essay
essay_embedding = sbert.encode([essay_text])
# Prepare metadata as dict
metadata_input = {
"gender": gender,
"race_ethnicity": race_ethnicity,
"student_disability_status": disability,
"economically_disadvantaged": disadvantaged,
"ell_status": ell_status
}
# Convert to array in correct order
metadata_values = [metadata_input[col] for col in metadata_columns]
metadata_array = encoder.transform([metadata_values]) # shape: (1, n)
# Combine essay embedding + metadata
full_input = np.hstack([essay_embedding, metadata_array])
# Predict from both models
ridge_score = ridge.predict(full_input)[0]
lgb_score = lgb_model.predict(full_input)[0]
final_score = round((0.5 * ridge_score + 0.5 * lgb_score), 2)
return final_score
# Gradio UI
iface = gr.Interface(
fn=predict_score,
inputs=[
gr.Textbox(label="Essay Text", lines=10, placeholder="Paste your essay here..."),
gr.Dropdown(["Male", "Female", "Other"], label="Gender"),
gr.Dropdown(["Asian", "Black", "Hispanic", "White", "Other"], label="Race/Ethnicity"),
gr.Dropdown(["Yes", "No"], label="Student has Disability"),
gr.Dropdown(["Yes", "No"], label="Economically Disadvantaged"),
gr.Dropdown(["Yes", "No"], label="ELL Status")
],
outputs=gr.Number(label="Predicted Essay Score"),
title="Automated Essay Scoring App"
)
iface.launch()
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