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45 lines
1.8 KiB
Python
45 lines
1.8 KiB
Python
import streamlit as st
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import os
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from loaders.audio import process_audio
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from loaders.txt import process_txt
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from loaders.csv import process_csv
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from loaders.markdown import process_markdown
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from utils import compute_sha1_from_content
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from loaders.pdf import process_pdf
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def file_uploader(supabase, openai_key, vector_store):
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file_processors = {
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".txt": process_txt,
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".csv": process_csv,
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".md": process_markdown,
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".markdown": process_markdown,
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".m4a": process_audio,
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".mp3": process_audio,
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".webm": process_audio,
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".mp4": process_audio,
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".mpga": process_audio,
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".wav": process_audio,
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".mpeg": process_audio,
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".pdf": process_pdf,
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}
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files = st.file_uploader("Upload a file", accept_multiple_files=True, type=list(file_processors.keys()))
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if st.button("Add to Database"):
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if files is not None:
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for file in files:
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if file_already_exists(supabase, file):
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st.write(f"😎 {file.name} is already in the database.")
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elif file.size < 1:
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st.write(f"💨 {file.name} is empty.")
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else:
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file_extension = os.path.splitext(file.name)[-1]
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if file_extension in file_processors:
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file_processors[file_extension](vector_store, file)
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st.write(f"✅ {file.name} ")
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else:
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st.write(f"❌ {file.name} is not a valid file type.")
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def file_already_exists(supabase, file):
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file_sha1 = compute_sha1_from_content(file.getvalue())
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response = supabase.table("documents").select("id").eq("metadata->>file_sha1", file_sha1).execute()
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return len(response.data) > 0 |