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| """Hugging Face Space: live CPU demo for the exact fixed evidence-graph reranker.""" | |
| import json | |
| import gradio as gr | |
| # ZeroGPU requires an actual GPU-marked Gradio event function at startup. | |
| # The reranker remains CPU-only; the decorator is needed for ZeroGPU hosting. | |
| # On ordinary CPU installations the shim leaves the function unchanged. | |
| try: | |
| import spaces | |
| except ImportError: | |
| class _LocalSpaces: | |
| def GPU(*args, **kwargs): | |
| if args and callable(args[0]): | |
| return args[0] | |
| return lambda fn: fn | |
| spaces = _LocalSpaces() | |
| from evidence_graph_reranker import EvidenceGraphReranker | |
| _RERANKERS = {} | |
| EXAMPLE = [ | |
| {"title": "Atlas device", "text": "The Atlas device was launched by Novus Labs in 2025."}, | |
| {"title": "Novus Labs", "text": "Novus Labs was founded by Mira Chen in 2018."}, | |
| {"title": "Orion spacecraft", "text": "The Orion spacecraft is designed for space missions."}, | |
| {"title": "Electronics", "text": "Electronics can be manufactured in many countries."}, | |
| ] | |
| def demo(query, passages_json, budget, backend): | |
| try: | |
| docs = json.loads(passages_json) | |
| if not isinstance(docs, list) or not 1 <= len(docs) <= 100: | |
| raise ValueError("Enter a JSON list containing 1–100 passages.") | |
| if not query.strip(): | |
| raise ValueError("Query cannot be empty.") | |
| if backend not in _RERANKERS: | |
| _RERANKERS[backend] = EvidenceGraphReranker(backend=backend, device="cpu", batch_size=16) | |
| ranked = _RERANKERS[backend].rank(query, docs, k=int(budget)) | |
| table = [ | |
| [i + 1, r["index"], r["title"] or "(untitled)", | |
| round(r["relevance_score"], 4), round(r["step_score"], 4), | |
| round(r["directed_link_bonus"], 2)] | |
| for i, r in enumerate(ranked) | |
| ] | |
| return table, json.dumps(ranked, indent=2, ensure_ascii=False) | |
| except Exception as exc: | |
| return [], json.dumps({"error": str(exc)}) | |
| with gr.Blocks(title="Evidence-Graph Reranker") as app: | |
| gr.Markdown("# Evidence-Graph Reranker\nTraining-free, query-conditioned **joint evidence selection** after neural relevance scoring.\n\nA passage's usefulness can rise when it completes a link from a previously selected passage. This demo uses **one fixed graph rule** for every dataset. Model weights are fetched from their original Hugging Face repositories.") | |
| query = gr.Textbox(label="Query", value="Who founded the company that released the Atlas device?") | |
| passages = gr.Code(label="Candidate passages (JSON array; optional title)", language="json", | |
| value=json.dumps(EXAMPLE, indent=2)) | |
| with gr.Row(): | |
| budget = gr.Slider(minimum=1, maximum=10, step=1, value=3, label="Passage budget (k)") | |
| backend = gr.Dropdown(choices=["cross-encoder", "dense"], value="cross-encoder", | |
| label="Pretrained neural relevance model") | |
| go = gr.Button("Select complementary evidence", variant="primary") | |
| table = gr.Dataframe(headers=["Rank", "Candidate index", "Title", "Neural score", "Selection score", "Link bonus"], | |
| datatype=["number", "number", "str", "number", "number", "number"], | |
| label="Selected passages", interactive=False) | |
| output = gr.Code(label="Full selection JSON", language="json") | |
| go.click(demo, inputs=[query, passages, budget, backend], outputs=[table, output]) | |
| gr.Markdown("**Interpretation:** `selection_score` is a step-dependent heuristic, not a probability. Gold labels are never used by the selector. Reported benchmarks measure complete evidence coverage, *not* LLM-generated answer accuracy. See `MODEL_CARD.md` for protocols and caveats.") | |
| if __name__ == "__main__": | |
| app.launch(ssr_mode=False) | |