Spaces:
Sleeping
Sleeping
File size: 1,471 Bytes
24dc52a 80d18e0 561ac7c 24dc52a d9ef11d 24dc52a d9ef11d 24dc52a eecb28e d9ef11d eecb28e d9ef11d eecb28e |
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 |
import io
import os
import streamlit as st
import tempfile
from scripts import analyze_metadata, generate_metadata, ingest, MODEL_NAME
st.title('# DocVerifyRAG')
st.write('## Anomaly detection for BIM document metadata')
with st.form('my_form'):
st.write('Enter your file metadata in the following schema:')
text = st.text_input(label='Filename, Description, Discipline',
value="", placeholder=str)
submitted = st.form_submit_button('Submit')
if submitted:
filename, description, discipline = text.split(',')
st.write('## Analyzing with Vectara + together.ai')
analysis = analyze_metadata(filename, description, discipline)
st.write(analysis)
st.write('## Generate metadata?')
uploaded_file = st.file_uploader("Choose a PDF file", type=["pdf","txt"])
if uploaded_file is not None:
with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(uploaded_file.name)[1]) as tmp:
tmp.write(uploaded_file.read())
file_path = tmp.name
st.write(f'Created temporary file {file_path}')
docs = ingest(file_path)
st.write('## Querying Together.ai API')
metadata = generate_metadata(docs)
form = st.form(key='my_form')
form.text_input(label=f'Suggested Metadata Generated by {MODEL_NAME}')
stop_button = form.form_submit_button(label='Submit')
print(metadata)
os.remove(file_path) |