app.py

import streamlit as st

from components.sidebar import sidebar

from ui import (
    wrap_doc_in_html,
    is_query_valid,
    is_file_valid,
    is_open_ai_key_valid,
    display_file_read_error,
)

from core.caching import bootstrap_caching

from core.parsing import read_file
from core.chunking import chunk_file
from core.embedding import embed_files
from core.qa import query_folder
from core.utils import get_llm


EMBEDDING = "openai"
VECTOR_STORE = "faiss"
MODEL_LIST = ["gpt-3.5-turbo", "gpt-4"]

# Uncomment to enable debug mode
# MODEL_LIST.insert(0, "debug")

st.set_page_config(page_title="InspectSolv", page_icon="☘️", layout="wide")
st.header("☘️InspectSolv")

# Enable caching for expensive functions
bootstrap_caching()

sidebar()

openai_api_key = st.session_state.get("OPENAI_API_KEY")


if not openai_api_key:
    st.warning(
        "Enter your OpenAI API key in the sidebar. You can get a key at"
        " https://platform.openai.com/account/api-keys."
    )




uploaded_file = st.file_uploader(
    "Upload a pdf, docx, or txt file",
    type=["pdf", "docx", "txt"],
    help="Scanned documents are not supported yet!",
)

# model: str = st.selectbox("Model", options=MODEL_LIST)  # type: ignore

model = MODEL_LIST[0]


from importlib import metadata
with st.expander("Advanced Options"):
    return_all_chunks = st.checkbox("Show all chunks retrieved from vector search")
    show_full_doc = st.checkbox("Show parsed contents of the document")
    st.markdown('# config')
    import sys
    st.markdown(f'Python version: {sys.version}')
    for dist in metadata.distributions():
        st.markdown((f"{dist.name}=={dist.version}"))



if not uploaded_file:
    st.stop()

try:
    file = read_file(uploaded_file)
except Exception as e:
    display_file_read_error(e, file_name=uploaded_file.name)

chunked_file = chunk_file(file, chunk_size=300, chunk_overlap=0)

if not is_file_valid(file):
    st.stop()


if not is_open_ai_key_valid(openai_api_key, model):
    st.stop()


with st.spinner("Indexing document... This may take a while⏳"):
    folder_index = embed_files(
        files=[chunked_file],
        embedding=EMBEDDING if model != "debug" else "debug",
        vector_store=VECTOR_STORE if model != "debug" else "debug",
        openai_api_key=openai_api_key,
    )


with st.form(key="qa_form"):
    options = ['List all pre existing conditions which may affect home insurance', 'Show the problematic components!', 'Show repair needs!']
    query = st.selectbox('Select an option', options)
    submit = st.form_submit_button("Submit")


if show_full_doc:
    with st.expander("Document"):
        # Hack to get around st.markdown rendering LaTeX
        st.markdown(f"<p>{wrap_doc_in_html(file.docs)}</p>", unsafe_allow_html=True)


if submit:
    if not is_query_valid(query):
        st.stop()

    # Output Columns
    answer_col, sources_col = st.columns(2)

    llm = get_llm(model=model, openai_api_key=openai_api_key, temperature=0)
    result = query_folder(
        folder_index=folder_index,
        query=query,
        return_all=return_all_chunks,
        llm=llm,
    )

    with answer_col:
        st.markdown("#### Answer")
        st.markdown(result.answer)

    with sources_col:
        st.markdown("#### Sources")
        for source in result.sources:
            st.markdown(source.page_content)
            st.markdown(source.metadata["source"])
            st.markdown("---")