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from __future__ import annotations |
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import json |
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import shutil |
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import subprocess |
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import tempfile |
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from datetime import datetime, timedelta |
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from functools import lru_cache |
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from pathlib import Path |
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from huggingface_hub import hf_hub_download |
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import gradio as gr |
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from modular_graph_and_candidates import build_graph_json, generate_html, build_timeline_json, generate_timeline_html |
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def _escape_srcdoc(text: str) -> str: |
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"""Escape for inclusion inside an <iframe srcdoc="…"> attribute.""" |
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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): |
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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}-{threshold:.2f}-m{int(multimodal)}" |
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html_fp = hf_hub_download(repo_id=repo_id, filename=f"{kind}/{key}.html", repo_type="dataset") |
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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_html = Path(html_fp).read_text(encoding="utf-8") |
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json_text = Path(json_fp).read_text(encoding="utf-8") |
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iframe_html = f'<iframe style="width:100%;height:85vh;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_text, encoding="utf-8") |
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return iframe_html, str(tmp) |
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def run_graph(repo_url: str, threshold: float, multimodal: bool, sim_method: str): |
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return _fetch_from_cache_repo("graph", sim_method, threshold, multimodal) |
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def run_timeline(repo_url: str, threshold: float, multimodal: bool, sim_method: str): |
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return _fetch_from_cache_repo("timeline", sim_method, threshold, multimodal) |
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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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with gr.Blocks(css=CUSTOM_CSS) as demo: |
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gr.Markdown("## 🔍 Modular‑candidate explorer for 🤗 Transformers") |
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with gr.Tabs(): |
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with gr.Tab("Dependency Graph"): |
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with gr.Row(): |
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repo_in = gr.Text(value=HF_MAIN_REPO, label="Repo / fork URL") |
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thresh = gr.Slider(0.50, 0.95, value=0.5, step=0.01, label="Similarity ≥") |
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multi_cb = gr.Checkbox(label="Only multimodal models") |
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sim_radio = gr.Radio(["jaccard", "embedding"], value="jaccard", label="Similarity metric") |
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go_btn = gr.Button("Build graph") |
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graph_html_out = gr.HTML(elem_id="graph_html", show_label=False) |
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graph_json_out = gr.File(label="Download graph.json") |
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go_btn.click(run_graph, [repo_in, thresh, multi_cb, sim_radio], [graph_html_out, graph_json_out]) |
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with gr.Tab("Chronological Timeline"): |
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with gr.Row(): |
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timeline_repo_in = gr.Text(value=HF_MAIN_REPO, label="Repo / fork URL") |
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timeline_thresh = gr.Slider(0.50, 0.95, value=0.5, step=0.01, label="Similarity ≥") |
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timeline_multi_cb = gr.Checkbox(label="Only multimodal models") |
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timeline_sim_radio = gr.Radio(["jaccard", "embedding"], value="jaccard", label="Similarity metric") |
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timeline_btn = gr.Button("Build timeline") |
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timeline_html_out = gr.HTML(elem_id="timeline_html", show_label=False) |
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timeline_json_out = gr.File(label="Download timeline.json") |
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timeline_btn.click(run_timeline, [timeline_repo_in, timeline_thresh, timeline_multi_cb, timeline_sim_radio], [timeline_html_out, timeline_json_out]) |
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if __name__ == "__main__": |
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demo.launch(allowed_paths=["static"]) |
