File size: 3,287 Bytes
80f21d1 | 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 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 | import io
import os
from pathlib import Path
import pandas as pd
import streamlit as st
from business_export_job import JobConfig, run_job
# On HF Spaces /app/output doesn't exist; default to a local output folder.
if "SWISSPARL_OUTPUT_DIR" not in os.environ:
os.environ["SWISSPARL_OUTPUT_DIR"] = str(Path(__file__).parent / "output")
OUTPUT_DIR = Path(os.environ["SWISSPARL_OUTPUT_DIR"])
OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
st.set_page_config(page_title="SwissParl Topic Tagger – Results", layout="wide")
st.title("SwissParl Topic Tagger – Results")
with st.sidebar:
st.header("Actions")
if st.button("Trigger Topic Modelling", help="Run the topic modelling job now."):
with st.spinner("Running topic modelling... this may take a while."):
try:
config = JobConfig.from_env()
# Ensure the filename template includes the timestamp if we want it to be unique
if "{timestamp}" not in config.output_filename_template:
config = JobConfig(
output_dir=config.output_dir,
output_filename_template=config.output_filename_template.replace(".xlsx", "-{timestamp}.xlsx"),
days_past=config.days_past,
start_date=config.start_date,
end_date=config.end_date,
languages=config.languages,
language_priority=config.language_priority,
topic_score_threshold=config.topic_score_threshold,
min_chunk_days=config.min_chunk_days,
excel_sheet_name=config.excel_sheet_name,
)
output_path = run_job(config)
st.success(f"Job completed! Result saved to `{output_path.name}`")
st.rerun()
except Exception as e:
st.error(f"Job failed: {e}")
excel_files = sorted(OUTPUT_DIR.glob("*.xlsx"), reverse=True)
if not excel_files:
st.warning(f"No Excel files found in `{OUTPUT_DIR}`. Use the sidebar button to run the topic modelling job.")
st.stop()
selected = st.selectbox(
"Select a result file",
excel_files,
format_func=lambda p: p.name,
)
@st.cache_data
def load_excel(path: str) -> pd.DataFrame:
return pd.read_excel(path, engine="openpyxl")
df = load_excel(str(selected))
# Download button (above the table so it's always visible)
buf = io.BytesIO()
with pd.ExcelWriter(buf, engine="openpyxl") as writer:
df.to_excel(writer, index=False)
buf.seek(0)
col1, col2 = st.columns([3, 1])
with col1:
st.caption(f"{len(df):,} rows · {len(df.columns)} columns · `{selected.name}`")
with col2:
st.download_button(
label="Download Excel",
data=buf,
file_name=selected.name,
mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
)
# Row filter
search = st.text_input("Filter rows (searches all text columns)", "")
if search:
mask = df.apply(
lambda col: col.astype(str).str.contains(search, case=False, na=False)
).any(axis=1)
df = df[mask]
st.caption(f"{len(df):,} rows match the filter")
st.dataframe(df, use_container_width=True, height=600)
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