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b39aff5 45dc55b b39aff5 45dc55b b39aff5 | 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 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 | from __future__ import annotations
import argparse
import json
from pathlib import Path
import gradio as gr
from tools import (
DEFAULT_INPUT_CSV,
HF_MODEL,
OUTPUT_DIR,
compare_with_taxonomy,
consolidate_into_themes,
export_narrative,
generate_comparison_csv,
label_topics_with_llm,
load_scopus_csv,
run_bertopic_discovery,
run_full_pipeline,
)
CUSTOM_CSS = """
.status-ok { color: #1b5e20; font-weight: 600; }
.status-note { color: #37474f; }
"""
def _resolve_output_dir(value: str) -> Path:
return Path(value) if value else OUTPUT_DIR
def ui_load(file_path: str, output_dir: str) -> str:
stats = load_scopus_csv(file_path, _resolve_output_dir(output_dir))
return json.dumps(stats, indent=2)
def ui_discover(text_type: str, output_dir: str) -> str:
payload = run_bertopic_discovery(text_type, _resolve_output_dir(output_dir))
return json.dumps(payload, indent=2)
def ui_label(text_type: str, output_dir: str) -> str:
payload = label_topics_with_llm(text_type, _resolve_output_dir(output_dir))
return json.dumps(payload, indent=2)
def ui_theme(text_type: str, output_dir: str) -> str:
payload = consolidate_into_themes(text_type, 15, _resolve_output_dir(output_dir))
return json.dumps(payload, indent=2)
def ui_taxonomy(text_type: str, output_dir: str) -> str:
payload = compare_with_taxonomy(text_type, _resolve_output_dir(output_dir))
return json.dumps(payload, indent=2)
def ui_compare(output_dir: str) -> str:
payload = generate_comparison_csv(_resolve_output_dir(output_dir))
return json.dumps(payload, indent=2)
def ui_narrative(output_dir: str) -> str:
payload = export_narrative(_resolve_output_dir(output_dir))
return json.dumps(payload, indent=2)
def ui_full_pipeline(file_path: str, output_dir: str) -> str:
payload = run_full_pipeline(file_path=file_path, output_dir=_resolve_output_dir(output_dir))
return json.dumps(payload, indent=2)
def create_interface() -> gr.Blocks:
with gr.Blocks(css=CUSTOM_CSS, title="CHB BERTopic V3") as app:
gr.Markdown(
f"""
# CHB BERTopic V3
### Hugging Face deployment: SPECTER2 + UMAP + HDBSCAN + BERTopic
**Default input:** `{DEFAULT_INPUT_CSV}`
**Default output:** `{OUTPUT_DIR}`
**LLM backend:** Hugging Face Inference `{HF_MODEL}`
**Secret required for LLM phases:** `HF_TOKEN`
"""
)
with gr.Row():
file_path = gr.Textbox(label="Input CSV", value=str(DEFAULT_INPUT_CSV), lines=1)
output_dir = gr.Textbox(label="Output directory", value=str(OUTPUT_DIR), lines=1)
full_run = gr.Button("Run full pipeline", variant="primary")
full_output = gr.Textbox(label="Full pipeline result", lines=18)
full_run.click(ui_full_pipeline, inputs=[file_path, output_dir], outputs=[full_output])
with gr.Tabs():
with gr.Tab("Phase 1"):
load_btn = gr.Button("Load corpus")
load_out = gr.Textbox(lines=16, label="Load output")
load_btn.click(ui_load, inputs=[file_path, output_dir], outputs=[load_out])
with gr.Tab("Phase 2"):
abs_disc_btn = gr.Button("Discover abstract topics")
title_disc_btn = gr.Button("Discover title topics")
abs_disc_out = gr.Textbox(lines=16, label="Abstract discovery")
title_disc_out = gr.Textbox(lines=16, label="Title discovery")
abs_disc_btn.click(ui_discover, inputs=[gr.State("abstract"), output_dir], outputs=[abs_disc_out])
title_disc_btn.click(ui_discover, inputs=[gr.State("title"), output_dir], outputs=[title_disc_out])
with gr.Tab("Phase 3"):
abs_label_btn = gr.Button("Label abstract topics")
title_label_btn = gr.Button("Label title topics")
abs_label_out = gr.Textbox(lines=16, label="Abstract labels")
title_label_out = gr.Textbox(lines=16, label="Title labels")
abs_label_btn.click(ui_label, inputs=[gr.State("abstract"), output_dir], outputs=[abs_label_out])
title_label_btn.click(ui_label, inputs=[gr.State("title"), output_dir], outputs=[title_label_out])
with gr.Tab("Phase 4"):
abs_theme_btn = gr.Button("Consolidate abstract themes")
title_theme_btn = gr.Button("Consolidate title themes")
abs_theme_out = gr.Textbox(lines=16, label="Abstract themes")
title_theme_out = gr.Textbox(lines=16, label="Title themes")
abs_theme_btn.click(ui_theme, inputs=[gr.State("abstract"), output_dir], outputs=[abs_theme_out])
title_theme_btn.click(ui_theme, inputs=[gr.State("title"), output_dir], outputs=[title_theme_out])
with gr.Tab("Phase 5"):
abs_tax_btn = gr.Button("Map abstract themes to PAJAIS")
title_tax_btn = gr.Button("Map title themes to PAJAIS")
abs_tax_out = gr.Textbox(lines=16, label="Abstract taxonomy")
title_tax_out = gr.Textbox(lines=16, label="Title taxonomy")
abs_tax_btn.click(ui_taxonomy, inputs=[gr.State("abstract"), output_dir], outputs=[abs_tax_out])
title_tax_btn.click(ui_taxonomy, inputs=[gr.State("title"), output_dir], outputs=[title_tax_out])
with gr.Tab("Phase 6"):
compare_btn = gr.Button("Generate comparison")
compare_out = gr.Textbox(lines=16, label="Comparison")
compare_btn.click(ui_compare, inputs=[output_dir], outputs=[compare_out])
with gr.Tab("Phase 7"):
narrative_btn = gr.Button("Generate narrative")
narrative_out = gr.Textbox(lines=16, label="Narrative result")
narrative_btn.click(ui_narrative, inputs=[output_dir], outputs=[narrative_out])
return app
def main() -> None:
parser = argparse.ArgumentParser(description="CHB BERTopic Hugging Face app")
parser.add_argument("--mode", choices=["ui", "pipeline"], default="ui")
parser.add_argument("--input", default=str(DEFAULT_INPUT_CSV))
parser.add_argument("--output-dir", default=str(OUTPUT_DIR))
parser.add_argument("--host", default="0.0.0.0")
parser.add_argument("--port", type=int, default=7860)
args = parser.parse_args()
if args.mode == "pipeline":
payload = run_full_pipeline(file_path=args.input, output_dir=Path(args.output_dir))
print(json.dumps(payload, indent=2))
return
app = create_interface()
app.launch(server_name=args.host, server_port=args.port)
if __name__ == "__main__":
main()
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