import pickle
import gradio as gr
from scipy.sparse import hstack

OPTIONS = ["A","B","C","D","E"]

with open("model.pkl","rb") as f:
    data = pickle.load(f)
tfw = data["tfw"]
tfc = data["tfc"]
clf = data["clf"]

def predict(question, a, b, c, d, e):
    options = [a, b, c, d, e]
    texts   = [question + " [SEP] " + o for o in options]
    X       = hstack([tfw.transform(texts), tfc.transform(texts)])
    probs   = clf.predict_proba(X)[:, 1]
    ranked  = sorted(zip(OPTIONS, options, probs), key=lambda x: x[2], reverse=True)
    out     = "## Top 3 Predictions\n\n"
    medals  = ["🥇", "🥈", "🥉"]
    for i, (letter, text, score) in enumerate(ranked[:3]):
        out += f"{medals[i]} **Option {letter}** — Score: {score:.3f}\n\n{text}\n\n"
    return out

with gr.Blocks(title="Smart MCQ Solver") as demo:
    gr.Markdown("# 🧠 Smart MCQ Solver — 22f3001980")
    gr.Markdown("TF-IDF + Logistic Regression Pairwise Ranker")
    question = gr.Textbox(lines=3, label="Question")
    with gr.Row():
        a = gr.Textbox(label="Option A")
        b = gr.Textbox(label="Option B")
        c = gr.Textbox(label="Option C")
    with gr.Row():
        d = gr.Textbox(label="Option D")
        e = gr.Textbox(label="Option E")
    btn = gr.Button("🔍 Predict Top 3", variant="primary")
    out = gr.Markdown(label="Result")
    btn.click(fn=predict, inputs=[question,a,b,c,d,e], outputs=out)

demo.launch(server_name="0.0.0.0", server_port=7860, block=True)