File size: 1,660 Bytes
4159dc2 8f10b0b 4159dc2 a0ace0f 8f10b0b 4159dc2 415b5df 4159dc2 8f10b0b a4575d8 8f10b0b 360e8ae 415b5df 6cd48e0 360e8ae 6cd48e0 360e8ae 4159dc2 d80f9fb 9e32d29 d80f9fb 65e404a 4ca165d 17b5440 a4575d8 4ca165d a4575d8 2b25048 a4575d8 4745a50 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
import gradio as gr
from fastapi import FastAPI, Request
import uvicorn
# from sentence_transformers import SentenceTransformer
# from sentence_transformers.util import cos_sim
# from sentence_transformers.quantization import quantize_embeddings
import spaces
app = FastAPI()
@spaces.GPU
def embed(text):
query_embedding = Embedder.encode(text)
return query_embedding.tolist();
#@app.post("/v1/embeddings")
#async def openai_embeddings(request: Request):
# body = await request.json();
# print(body);
#
# model = body['model']
# text = body['input'];
# embeddings = embed(text)
# return {
# 'object': "list"
# ,'data': [{
# 'object': "embeddings"
# ,'embedding': embeddings
# ,'index':0
# }]
# ,'model':model
# ,'usage':{
# 'prompt_tokens': 0
# ,'total_tokens': 0
# }
# }
def fn(text):
embed(text);
with gr.Blocks(fill_height=True) as demo:
text = gr.Textbox();
embeddings = gr.Textbox()
text.submit(fn, [text], [embeddings]);
print("Loading embedding model");
Embedder = None #SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1")
# demo.run_startup_events()
#demo.launch(
# share=False,
# debug=False,
# server_port=7860,
# server_name="0.0.0.0",
# allowed_paths=[]
#)
print("Demo run...");
(app,url,other) = demo.launch(prevent_thread_lock=False);
print("Mounting app...");
GradioApp = gr.mount_gradio_app(app, demo, path="/", ssr_mode=False);
if __name__ == '__main__':
print("Running uviconr...");
uvicorn.run(GradioApp)
|