from fastapi import FastAPI
import uvicorn
import faiss
from sentence_transformers import SentenceTransformer

model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
index = faiss.IndexFlatL2(model.get_sentence_embedding_dimension())   # build the index

index.add(model.encode(['hello']))

app = FastAPI()



@app.post('/tts')
async def transcribe(text: str):
    embeddings = model.encode([text])

    # store the text to a file

    return embeddings[0]


if __name__ == '__main__':
    uvicorn.run('app:app', host='0.0.0.0', port=7860)