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app (4).py

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+import gradio as gr
+from sentence_transformers import SentenceTransformer
+import numpy as np
+
+# Load model once at startup
+st_model = SentenceTransformer("sentence-transformers/all-mpnet-base-v2")
+
+TITLE = "# Text → Vector (all-mpnet-base-v2)"
+DESC = (
+    "Masukkan **kalimat** lalu dapatkan **embedding vector** "
+    "(opsional dinormalisasi L2). Model: `sentence-transformers/all-mpnet-base-v2`."
+)
+
+def embed(text: str, normalize: bool = True):
+    text = (text or "").strip()
+    if not text:
+        return [], 0
+    vec = st_model.encode([text], normalize_embeddings=normalize)[0]
+    return vec.tolist(), int(vec.shape[0])
+
+with gr.Blocks() as demo:
+    gr.Markdown(TITLE)
+    gr.Markdown(DESC)
+
+    with gr.Row():
+        text_in = gr.Textbox(
+            label="Kalimat",
+            placeholder="Tulis kalimat di sini...",
+            lines=3,
+        )
+    normalize = gr.Checkbox(value=True, label="Normalize embedding (L2)")
+    btn = gr.Button("Compute Embedding", variant="primary")
+
+    with gr.Row():
+        vec_out = gr.JSON(label="Vector (list of floats)")
+        dim_out = gr.Number(label="Dimensi vektor", interactive=False)
+
+    gr.Examples(
+        examples=[
+            ["Halo dunia!"],
+            ["Machine learning is fun."],
+            ["Saya sedang membangun demo embedding sederhana."],
+        ],
+        inputs=[text_in],
+        label="Contoh",
+    )
+
+    btn.click(embed, inputs=[text_in, normalize], outputs=[vec_out, dim_out])
+
+# Enable queue for concurrency
+demo.queue()
+
+if __name__ == "__main__":
+    demo.launch()

requirements (4).txt

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+gradio>=4.31.4
+sentence-transformers>=2.6.1
+torch>=2.1.0
+numpy>=1.26