app.py

import gradio as gr
import joblib
from transformers import pipeline

# Load model hoax detector
model = joblib.load("model_hoax.pkl")
vectorizer = joblib.load("vectorizer.pkl")

# Load QA dan NER pipeline
qa_pipe = pipeline("question-answering", model="Rifky/IndoBERT-QA")
ner_pipe = pipeline("ner", model="cahya/bert-base-indonesian-NER", aggregation_strategy="simple")

# --- Fungsi ---
def detect_hoax(text):
    vec = vectorizer.transform([text])
    result = model.predict(vec)
    return "HOAX" if result[0] == 1 else "Bukan Hoax"

def qa_chat(message, history, context):
    if not context:
        return "Mohon masukkan teks berita di kolom atas terlebih dahulu."
    result = qa_pipe(question=message, context=context)
    return result['answer']

def ner(text):
    entities = ner_pipe(text)
    return "\n".join([f"{e['word']} ({e['entity_group']})" for e in entities])

# --- UI Gradio ---
with gr.Blocks() as demo:
    gr.Markdown("## Deteksi Hoaks, QA (Chat), dan NER")

    # Shared input
    context_input = gr.Textbox(label="Teks Berita / Konteks", lines=5, placeholder="Masukkan teks berita di sini...")

    with gr.Tab("Deteksi Hoaks"):
        hoax_output = gr.Textbox(label="Output Deteksi")
        hoax_btn = gr.Button("Deteksi")
        hoax_btn.click(fn=detect_hoax, inputs=context_input, outputs=hoax_output)

    with gr.Tab("QA"):
        gr.Markdown("Tanyakan apapun berdasarkan teks berita di atas:")
        qa_chatbot = gr.ChatInterface(
            fn=lambda msg, hist: qa_chat(msg, hist, context_input.value),
            title="Tanya Jawab Berbasis Teks",
        )

    with gr.Tab("NER"):
        ner_output = gr.Textbox(label="Hasil Ekstraksi Entitas", lines=5)
        ner_btn = gr.Button("Ekstrak Entitas")
        ner_btn.click(fn=ner, inputs=context_input, outputs=ner_output)

demo.launch()