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README.md

@@ -1,13 +1,12 @@
 ---
-title: Stsb Tiny Embedding Api
-emoji: 📚
-colorFrom: indigo
+title: Stsb Bert Tiny Embedding Api
+emoji: 🧠
+colorFrom: blue
 colorTo: purple
 sdk: gradio
-sdk_version: 6.28.0
-python_version: '3.12'
+sdk_version: 4.44.0
 app_file: app.py
-pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Embedding API wrapping sentence-transformers-testing/stsb-bert-tiny-safetensors.
+Runs on CPU only inside a ZeroGPU-slot Space (never calls the GPU decorator).

app.py

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+"""
+Embedding API for a Hugging Face Space (Gradio SDK, ZeroGPU hardware slot,
+but runs on CPU only — we never call the GPU decorator, so it consumes
+zero ZeroGPU quota. This is how a free personal account can run a real
+Python backend now that CPU Basic requires PRO.
+"""
+
+import json
+import gradio as gr
+from sentence_transformers import SentenceTransformer
+
+MODEL_NAME = "sentence-transformers-testing/stsb-bert-tiny-safetensors"
+API_KEY = "hellonumbers77@"  # change this to a strong random token
+
+print(f"Loading model: {MODEL_NAME} ...")
+model = SentenceTransformer(MODEL_NAME)
+print("Model loaded. Embedding dim:", model.get_sentence_embedding_dimension())
+
+
+def embed(texts_json: str, api_key: str) -> str:
+    if api_key != API_KEY:
+        return json.dumps({"error": "unauthorized"})
+    try:
+        texts = json.loads(texts_json)
+    except json.JSONDecodeError:
+        return json.dumps({"error": "texts_json must be a JSON array of strings"})
+    vectors = model.encode(texts).tolist()
+    return json.dumps({"embeddings": vectors})
+
+
+demo = gr.Interface(
+    fn=embed,
+    inputs=[
+        gr.Textbox(label="texts (JSON array)", value='["hello world"]'),
+        gr.Textbox(label="api_key", type="password"),
+    ],
+    outputs=gr.Textbox(label="result (JSON)"),
+    api_name="embed",
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+sentence-transformers