app.py

import gradio as gr
from sentence_transformers import SentenceTransformer
from sklearn.cluster import KMeans
import pandas as pd

# Load SBERT model
model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")

def cluster_questions(questions_text, num_clusters):
    # Pisahkan pertanyaan per baris
    questions = [q.strip() for q in questions_text.split("\n") if q.strip()]
    if len(questions) < num_clusters:
        return "Jumlah pertanyaan harus >= jumlah cluster"

    # Embedding
    embeddings = model.encode(questions)

    # Clustering
    kmeans = KMeans(n_clusters=num_clusters, random_state=42)
    labels = kmeans.fit_predict(embeddings)

    # Buat dataframe hasil
    df = pd.DataFrame({"Question": questions, "Cluster": labels})
    return df

# UI Gradio
with gr.Blocks() as demo:
    gr.Markdown("# 🧠 Question Clustering Demo (SBERT)")
    with gr.Row():
        with gr.Column():
            questions_input = gr.Textbox(
                lines=10,
                placeholder="Tulis pertanyaan per baris...",
                label="Daftar Pertanyaan"
            )
            num_clusters = gr.Slider(2, 10, step=1, value=3, label="Jumlah Cluster")
            btn = gr.Button("Proses Clustering")
        with gr.Column():
            output = gr.Dataframe(headers=["Question", "Cluster"], interactive=False)

    btn.click(cluster_questions, inputs=[questions_input, num_clusters], outputs=output)

demo.launch()