create model

app.py

@@ -1,10 +1,38 @@
 import gradio as gr
+from sentence_transformers import SentenceTransformer
 
-with gr.Blocks(fill_height=True) as demo:
-    with gr.Sidebar():
-        gr.Markdown("# Inference Provider")
-        gr.Markdown("This Space showcases the sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 model, served by the hf-inference API. Sign in with your Hugging Face account to use this API.")
-        button = gr.LoginButton("Sign in")
-    gr.load("models/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2", accept_token=button, provider="hf-inference")
+# Load the model globally to ensure it's only loaded once
+# This is crucial for performance and resource management
+model = SentenceTransformer("sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
+
+def get_embedding(text: str):
+    """
+    Menghasilkan embedding vector dari teks menggunakan model SentenceTransformer.
+    """
+    # Menghindari encoding teks kosong
+    if not text:
+        return []
     
-demo.launch()
+    # Mengonversi array numpy ke list Python agar bisa di-JSON-kan
+    embedding = model.encode(text).tolist()
+    return embedding
+
+# Gradio Interface
+# `fn` adalah fungsi yang akan dieksekusi
+# `inputs` adalah jenis input (dalam hal ini, sebuah teks)
+# `outputs` adalah jenis output (dalam hal ini, sebuah JSON)
+# `api_name` adalah nama endpoint API
+demo = gr.Interface(
+    fn=get_embedding,
+    inputs=gr.Textbox(label="Masukkan teks"),
+    outputs=gr.JSON(label="Embedding Vector"),
+    title="Multilingual Embedding API",
+    description="Masukkan teks untuk mendapatkan vektor embedding menggunakan model paraphrase-multilingual-MiniLM-L12-v2.",
+    examples=["Halo dunia!", "Apa kabar hari ini?", "Selamat pagi!"],
+    allow_flagging="never",
+    api_name="get_embedding"
+)
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()