Create app.py

app.py

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+import gradio as gr
+from sentence_transformers import SentenceTransformer
+
+# Load the multilingual embedding model
+model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
+
+# Define a function to embed text
+def embed(text: str):
+    if not text.strip():
+        return {"error": "Input text is empty."}
+    embedding = model.encode([text])[0]  # Get the embedding vector
+    return {"embedding": embedding.tolist()}
+
+# Launch Gradio interface
+demo = gr.Interface(
+    fn=embed,
+    inputs=gr.Textbox(lines=3, label="Input Text"),
+    outputs="json",
+    title="Multilingual Text Embedder",
+    description="Uses paraphrase-multilingual-MiniLM-L12-v2 to convert text into embeddings"
+)
+
+demo.launch()