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import gradio as gr
from fastapi import FastAPI, Request
import uvicorn
import spaces
from sentence_transformers import SentenceTransformer
print("Loading embedding model");
Embedder = SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1")
app = FastAPI()
@spaces.GPU
def embed(text):
query_embedding = Embedder.encode(text)
return query_embedding.tolist();
@app.post("/v1/embeddings")
async def openai_embed(req: Request):
body = await req.json();
print(body);
model = body['model'];
text = body['input'];
embeddings = embed(text)
return {
'object': "list"
,'data': [{
'object': "embeddings"
,'embedding': embeddings
,'index':0
}]
,'model': 'mixedbread-ai/mxbai-embed-large-v1'
,'usage':{
'prompt_tokens': 0
,'total_tokens': 0
}
}
with gr.Blocks() as demo:
text = gr.Textbox();
embeddings = gr.Textbox()
text.submit(embed, [text], [embeddings]);
print("Demo run...");
(app2,url,other) = demo.launch(prevent_thread_lock=True, server_name=None, server_port=8000);
GradioApp = gr.mount_gradio_app(app, demo, path="", ssr_mode=False);
demo.close();
if __name__ == '__main__':
print("Running uviconr...");
uvicorn.run(GradioApp, host="0.0.0.0", port=7860) |