from transformers import pipeline
import gradio as gr

sentiment_model = pipeline(
    "sentiment-analysis",
    model="w11wo/indonesian-roberta-base-sentiment-classifier"
)

ner_model = pipeline(
    "ner",
    model="cahya/bert-base-indonesian-NER",
    aggregation_strategy="simple"
)

topic_model = pipeline(
    "text-classification",
    model="YagiASAFAS/indonesia-news-classification-bert"
)

def analyze_text(text):
    if not text or not text.strip():
        return {"error": "Teks kosong. Silakan masukkan kalimat Bahasa Indonesia."}

    sentiment = sentiment_model(text)[0]
    sentiment_result = {
        "label": sentiment["label"],
        "score": round(sentiment["score"], 4)
    }

    entities = ner_model(text)
    entity_result = [
        {"entity": e["entity_group"], "word": e["word"], "score": round(e["score"], 4)}
        for e in entities
    ]

    topic = topic_model(text)[0]
    topic_result = {
        "label": topic["label"],
        "score": round(topic["score"], 4)
    }

    return {
        "sentiment": sentiment_result,
        "entities": entity_result,
        "topic": topic_result
    }

demo = gr.Interface(
    fn=analyze_text,
    inputs=gr.Textbox(lines=3, placeholder="Masukkan kalimat Bahasa Indonesia..."),
    outputs=gr.JSON(label="Hasil Analisis"),
    title="Analisis Sentimen, Entitas, & Topik Bahasa Indonesia",
    description="Gunakan AI untuk analisis sentimen, pengenalan entitas, dan deteksi topik otomatis (multilingual)."
)

if __name__ == "__main__":
    demo.launch()