mirror of
https://github.com/StanGirard/quivr.git
synced 2024-11-30 21:28:56 +03:00
e53bc6807d
* 🗃️ add new tables for multiple brains * 🗑️ remove date input from fetch_user_id_from_credentials * ✨ new /brain endpoints * ♻️ refactor backend utils by splitting it into files * 💡 comments for next actions to update /upload
76 lines
3.1 KiB
Python
76 lines
3.1 KiB
Python
import os
|
|
import tempfile
|
|
import time
|
|
from io import BytesIO
|
|
from tempfile import NamedTemporaryFile
|
|
|
|
import openai
|
|
from fastapi import UploadFile
|
|
from langchain.document_loaders import TextLoader
|
|
from langchain.embeddings.openai import OpenAIEmbeddings
|
|
from langchain.schema import Document
|
|
from langchain.text_splitter import RecursiveCharacterTextSplitter
|
|
from utils.common import documents_vector_store
|
|
from utils.file import compute_sha1_from_content
|
|
|
|
# # Create a function to transcribe audio using Whisper
|
|
# def _transcribe_audio(api_key, audio_file, stats_db):
|
|
# openai.api_key = api_key
|
|
# transcript = ""
|
|
|
|
# with BytesIO(audio_file.read()) as audio_bytes:
|
|
# # Get the extension of the uploaded file
|
|
# file_extension = os.path.splitext(audio_file.name)[-1]
|
|
|
|
# # Create a temporary file with the uploaded audio data and the correct extension
|
|
# with tempfile.NamedTemporaryFile(delete=True, suffix=file_extension) as temp_audio_file:
|
|
# temp_audio_file.write(audio_bytes.read())
|
|
# temp_audio_file.seek(0) # Move the file pointer to the beginning of the file
|
|
|
|
# transcript = openai.Audio.translate("whisper-1", temp_audio_file)
|
|
|
|
# return transcript
|
|
|
|
# async def process_audio(upload_file: UploadFile, stats_db):
|
|
async def process_audio(upload_file: UploadFile, enable_summarization: bool, user, user_openai_api_key):
|
|
|
|
file_sha = ""
|
|
dateshort = time.strftime("%Y%m%d-%H%M%S")
|
|
file_meta_name = f"audiotranscript_{dateshort}.txt"
|
|
# uploaded file to file object
|
|
|
|
openai_api_key = os.environ.get("OPENAI_API_KEY")
|
|
if user_openai_api_key:
|
|
openai_api_key = user_openai_api_key
|
|
|
|
# Here, we're writing the uploaded file to a temporary file, so we can use it with your existing code.
|
|
with tempfile.NamedTemporaryFile(delete=False, suffix=upload_file.filename) as tmp_file:
|
|
await upload_file.seek(0)
|
|
content = await upload_file.read()
|
|
tmp_file.write(content)
|
|
tmp_file.flush()
|
|
tmp_file.close()
|
|
|
|
with open(tmp_file.name, "rb") as audio_file:
|
|
transcript = openai.Audio.transcribe("whisper-1", audio_file)
|
|
|
|
file_sha = compute_sha1_from_content(transcript.text.encode("utf-8"))
|
|
file_size = len(transcript.text.encode("utf-8"))
|
|
|
|
# Load chunk size and overlap from sidebar
|
|
chunk_size = 250
|
|
chunk_overlap = 0
|
|
|
|
text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
|
|
chunk_size=chunk_size, chunk_overlap=chunk_overlap)
|
|
texts = text_splitter.split_text(transcript)
|
|
|
|
docs_with_metadata = [Document(page_content=text, metadata={"file_sha1": file_sha, "file_size": file_size, "file_name": file_meta_name,
|
|
"chunk_size": chunk_size, "chunk_overlap": chunk_overlap, "date": dateshort}) for text in texts]
|
|
|
|
# if st.secrets.self_hosted == "false":
|
|
# add_usage(stats_db, "embedding", "audio", metadata={"file_name": file_meta_name,"file_type": ".txt", "chunk_size": chunk_size, "chunk_overlap": chunk_overlap})
|
|
documents_vector_store.add_documents(docs_with_metadata)
|
|
|
|
return documents_vector_store
|