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import gradio as gr
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from fastapi import FastAPI, Request
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import uvicorn
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from sentence_transformers import SentenceTransformer
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from sentence_transformers.util import cos_sim
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from sentence_transformers.quantization import quantize_embeddings
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import spaces
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from gradio_client import Client
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import json
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import os
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app = FastAPI()
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@app.post("/v1/embeddings")
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async def openai_embeddings(request: Request):
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body = await request.json();
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token = request.headers.get("authorization");
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apiName = body.get("ApiName");
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print(body);
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BearerToken = None;
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if not token is None:
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parts = token.split(' ');
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BearerToken = parts[1];
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print("Using token...");
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SpacePath = body['model']
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print("Creating client...");
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SpaceClient = Client(SpacePath, hf_token = BearerToken)
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if not apiName:
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apiName = "/embed"
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text = body['input'];
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result = SpaceClient.predict(
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text=text,
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api_name=apiName
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)
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embeddings = json.loads(result);
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return {
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'object': "list"
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,'data': [{
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'object': "embeddings"
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,'embedding': embeddings
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,'index':0
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}]
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,'model': SpacePath
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,'usage':{
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'prompt_tokens': 0
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,'total_tokens': 0
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}
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}
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SpaceHost = os.environ.get("SPACE_HOST");
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if not SpaceHost:
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SpaceHost = "localhost"
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with gr.Blocks() as demo:
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gr.Markdown(f"""
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This space allow you connect SQL Server 2025 with Hugging Face to generate embeddings!
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First, create a ZeroGPU Space that export an endpoint called embed.
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That endpoint must accept a parameter called text.
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Then, create the external model using T-SQL:
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```sql
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CREATE EXTERNAL MODEL HuggingFace
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WITH (
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LOCATION = 'https://{SpaceHost}/v1/embeddings',
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API_FORMAT = 'OpenAI',
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MODEL_TYPE = EMBEDDINGS,
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MODEL = 'user/space'
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);
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```
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If you prefer, just type the space name into field bellow and we generate the right T-SQL command for you!
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""")
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SpaceName = gr.Textbox(label="Space", submit_btn=True)
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EndpointName = gr.Textbox(value="/embed", label = "EndpointName");
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tsqlCommand = gr.Textbox(lines=5);
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def UpdateTsql(space):
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return f"""
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CREATE EXTERNAL MODEL HuggingFace
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WITH (
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LOCATION = 'https://{SpaceHost}/v1/embeddings',
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API_FORMAT = 'OpenAI',
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MODEL_TYPE = EMBEDDINGS,
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MODEL = '{space}'
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)
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"""
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SpaceName.submit(UpdateTsql, [SpaceName], [tsqlCommand])
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print("Mounting app...");
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GradioApp = gr.mount_gradio_app(app, demo, path="", ssr_mode=False);
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if __name__ == '__main__':
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print("Running uviconr...");
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uvicorn.run(GradioApp, host="0.0.0.0", port=7860)
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