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from __future__ import annotations |
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import json |
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import tempfile |
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from pathlib import Path |
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import gradio as gr |
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from huggingface_hub import hf_hub_download |
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from modular_graph_and_candidates import ( |
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build_graph_json, |
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generate_html, |
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build_timeline_json, |
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generate_timeline_html, |
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filter_graph_by_threshold, |
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) |
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def _escape_srcdoc(text: str) -> str: |
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return ( |
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text.replace("&", "&") |
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.replace("\"", """) |
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.replace("'", "'") |
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.replace("<", "<") |
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.replace(">", ">") |
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) |
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HF_MAIN_REPO = "https://github.com/huggingface/transformers" |
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CACHE_REPO = "Molbap/hf_cached_embeds_log" |
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def _fetch_from_cache_repo(kind: str, sim_method: str, threshold: float, multimodal: bool, *, height_vh: int = 85): |
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repo_id = CACHE_REPO |
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latest_fp = hf_hub_download(repo_id=repo_id, filename="latest.json", repo_type="dataset") |
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info = json.loads(Path(latest_fp).read_text(encoding="utf-8")) |
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sha = info.get("sha") |
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key = f"{sha}/{sim_method}-m{int(multimodal)}" |
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json_fp = hf_hub_download(repo_id=repo_id, filename=f"{kind}/{key}.json", repo_type="dataset") |
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raw_data = json.loads(Path(json_fp).read_text(encoding="utf-8")) |
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filtered_data = filter_graph_by_threshold(raw_data, threshold) |
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if kind == "timeline": |
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raw_html = generate_timeline_html(filtered_data) |
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else: |
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raw_html = generate_html(filtered_data) |
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iframe_html = f'<iframe style="width:100%;height:{height_vh}vh;border:none;" srcdoc="{_escape_srcdoc(raw_html)}"></iframe>' |
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tmp = Path(tempfile.mkstemp(suffix=("_timeline.json" if kind == "timeline" else ".json"))[1]) |
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tmp.write_text(json.dumps(filtered_data), encoding="utf-8") |
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return iframe_html, str(tmp) |
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def run_loc(sim_method: str, multimodal: bool, *, height_vh: int = 85): |
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latest_fp = hf_hub_download(repo_id=CACHE_REPO, filename="latest.json", repo_type="dataset") |
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info = json.loads(Path(latest_fp).read_text(encoding="utf-8")) |
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sha = info["sha"] |
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key = f"{sha}/{sim_method}-m{int(multimodal)}" |
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html_fp = hf_hub_download(repo_id=CACHE_REPO, filename=f"loc/{key}.html", repo_type="dataset") |
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raw_html = Path(html_fp).read_text(encoding="utf-8") |
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iframe_html = f'<iframe style="width:100%;height:{height_vh}vh;border:none;" srcdoc="{_escape_srcdoc(raw_html)}"></iframe>' |
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return iframe_html |
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def run_graph(repo_url: str, threshold: float, multimodal: bool, sim_method: str, *, height_vh: int = 85): |
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return _fetch_from_cache_repo("graph", sim_method, threshold, multimodal, height_vh=height_vh) |
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def run_timeline(repo_url: str, threshold: float, multimodal: bool, sim_method: str, *, height_vh: int = 85): |
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return _fetch_from_cache_repo("timeline", sim_method, threshold, multimodal, height_vh=height_vh) |
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CUSTOM_CSS = """ |
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#graph_html iframe, #timeline_html iframe {height:85vh !important; width:100% !important; border:none;} |
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""" |
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TAB_INDEX = {"timeline": 0, "loc": 1, "graph": 2} |
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with gr.Blocks() as demo: |
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html = gr.HTML() |
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def _load(): |
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return run_loc(sim_method="jaccard", multimodal=False) |
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demo.load(_load, outputs=[html]) |
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if __name__ == "__main__": |
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demo.launch() |
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