103 lines
4.1 KiB
Python
103 lines
4.1 KiB
Python
import asyncio
|
|
|
|
import streamlit as st
|
|
import streamlit_scrollable_textbox as stx # type: ignore
|
|
from humanize import naturaltime
|
|
|
|
from open_notebook.domain.models import model_manager
|
|
from open_notebook.domain.notebook import Source
|
|
from open_notebook.domain.transformation import Transformation
|
|
from open_notebook.graphs.transformation import graph as transform_graph
|
|
from pages.stream_app.utils import check_models
|
|
|
|
|
|
def source_panel(source_id: str, notebook_id=None, modal=False):
|
|
check_models(only_mandatory=False)
|
|
source: Source = Source.get(source_id)
|
|
if not source:
|
|
raise ValueError(f"Source not found: {source_id}")
|
|
|
|
current_title = source.title if source.title else "No Title"
|
|
source.title = st.text_input("Title", value=current_title)
|
|
if source.title != current_title:
|
|
st.toast("Saved new Title")
|
|
source.save()
|
|
|
|
process_tab, source_tab = st.tabs(["Process", "Source"])
|
|
with process_tab:
|
|
c1, c2 = st.columns([4, 2])
|
|
with c1:
|
|
title = st.empty()
|
|
if source.title:
|
|
title.subheader(source.title)
|
|
if source.asset and source.asset.url:
|
|
from_src = f"from URL: {source.asset.url}"
|
|
elif source.asset and source.asset.file_path:
|
|
from_src = f"from file: {source.asset.file_path}"
|
|
else:
|
|
from_src = "from text"
|
|
st.caption(f"Created {naturaltime(source.created)}, {from_src}")
|
|
for insight in source.insights:
|
|
with st.expander(f"**{insight.insight_type}**"):
|
|
st.markdown(insight.content)
|
|
x1, x2 = st.columns(2)
|
|
if x1.button(
|
|
"Delete", type="primary", key=f"delete_insight_{insight.id}"
|
|
):
|
|
insight.delete()
|
|
st.rerun(scope="fragment" if modal else "app")
|
|
st.toast("Source deleted")
|
|
if notebook_id:
|
|
if x2.button(
|
|
"Save as Note", icon="📝", key=f"save_note_{insight.id}"
|
|
):
|
|
insight.save_as_note(notebook_id)
|
|
st.toast("Saved as Note. Refresh the Notebook to see it.")
|
|
|
|
with c2:
|
|
transformations = Transformation.get_all(order_by="name asc")
|
|
with st.container(border=True):
|
|
transformation = st.selectbox(
|
|
"Run a transformation",
|
|
transformations,
|
|
key=f"transformation_{source.id}",
|
|
format_func=lambda x: x.name,
|
|
)
|
|
st.caption(transformation.description)
|
|
if st.button("Run"):
|
|
asyncio.run(
|
|
transform_graph.ainvoke(
|
|
input=dict(source=source, transformation=transformation)
|
|
)
|
|
)
|
|
st.rerun(scope="fragment" if modal else "app")
|
|
|
|
if not model_manager.embedding_model:
|
|
help = (
|
|
"No embedding model found. Please, select one on the Models page."
|
|
)
|
|
else:
|
|
help = "This will generate your embedding vectors on the database for powerful search capabilities"
|
|
|
|
if source.embedded_chunks == 0 and st.button(
|
|
"Embed vectors",
|
|
icon="🦾",
|
|
help=help,
|
|
disabled=model_manager.embedding_model is None,
|
|
):
|
|
source.vectorize()
|
|
st.success("Embedding complete")
|
|
|
|
with st.container(border=True):
|
|
st.caption(
|
|
"Deleting the source will also delete all its insights and embeddings"
|
|
)
|
|
if st.button(
|
|
"Delete", type="primary", key=f"bt_delete_source_{source.id}"
|
|
):
|
|
source.delete()
|
|
st.rerun()
|
|
|
|
with source_tab:
|
|
st.subheader("Content")
|
|
stx.scrollableTextbox(source.full_text, height=300)
|