app.py

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')

def suggest_metadata(file_upload):
    extension = uploaded_file.name.split('.')[-1]

    with tempfile.NamedTemporaryFile(delete=False) as tmp:
        tmp.write(uploaded_file.read())
        st.write(f'Created temporary file {tmp.name}')

    st.write('## Ingesting Unstructured file')
    
    docs = ingest(tmp.name)
    print(f'Ingested {tmp.name}')

    metadata = generate_metadata(docs)
    st.write('## Querying Together.ai API')
    st.write(f'### Suggested Metadata Generated by {MODEL_NAME}')    
    st.write(f'#### {metadata}')   

with st.form('analyze_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)
        submitted = None

st.write('## Generate metadata?')
uploaded_file = st.file_uploader("Choose a PDF file", type="pdf")

if uploaded_file is not None:

    query_api = st.button('Query API')
    if query_api:
        suggest_metadata(uploaded_file)    
        query_api = None