Initial commit for hoax detector

app.py

@@ -0,0 +1,53 @@
+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()