Create app.py

app.py

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+import streamlit as st
+from sentence_transformers import SentenceTransformer
+
+# Load model
+@st.cache_resource
+def load_model():
+    return SentenceTransformer("BAAI/bge-small-en")
+
+model = load_model()
+
+# UI
+st.title("Text Embedder (BAAI/bge-small-en)")
+st.markdown("Enter some text and get the embedding vector.")
+
+# Input
+user_input = st.text_area("Text to Embed", height=150)
+
+if st.button("Generate Embedding"):
+    if user_input.strip():
+        with st.spinner("Embedding..."):
+            embedding = model.encode([user_input])[0]
+        st.success("Embedding generated!")
+        st.json(embedding.tolist())
+    else:
+        st.warning("Please enter some text.")