Upload app.py

app.py

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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:
+        @staticmethod
+        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."},
+]
+
+
+@spaces.GPU(duration=120)
+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)