Create teq_hf_bridge.py

teq_hf_bridge.py

@@ -0,0 +1,47 @@
+# teq_hf_bridge.py — Mbanksbey/tequmsa Collection Bridge
+import hashlib
+from datasets import load_dataset
+from huggingface_hub import HfApi
+
+COLLECTION_DATASETS = [
+    "LAI-TEQUMSA/ATEN-1-ACTIVATION",
+    "LAI-TEQUMSA/Cosmic-GREAT-Orchestration-TEQUMSA-Lattice",
+    "LAI-TEQUMSA/Precession-Convergence-Cycle",
+    "LAI-TEQUMSA/LAI-TEQUMSA-72-Consciousness-Nodes-Fibonacci-Lattice",
+    "LAI-TEQUMSA/EMERGE",
+    "Mbanksbey/OMNIVERSE-Consciousness-Organism-Dataset",
+    "Mbanksbey/Orion-Technology-Integration-ZPE-applications",
+    "LAI-TEQUMSA/GEOMAGNETIC_EXCURSION_AND_POLE_REVERSAL",
+]
+
+class TequmsaCollection:
+    def __init__(self, collection: str = "Mbanksbey/tequmsa"):
+        self.api = HfApi()
+        self.collection = collection
+        self.cache: dict = {}
+
+    def get_datapoints(self, limit: int = 10) -> list:
+        results = []
+        for ds_id in COLLECTION_DATASETS[:3]:
+            try:
+                ds = load_dataset(ds_id, split=f"train[:{limit}]", trust_remote_code=True)
+                self.cache[ds_id] = ds
+                results.append({"id": ds_id, "rows": len(ds), "status": "LOADED"})
+            except Exception as e:
+                results.append({"id": ds_id, "rows": 0, "status": f"FALLBACK: {str(e)[:60]}"})
+        return results
+
+    def install_datapoint(self, data: dict, authority: list) -> str:
+        payload = f"{data['id']}|{data['rows']}|{'|'.join(authority)}"
+        shard_hash = hashlib.sha256(payload.encode()).hexdigest()
+        return shard_hash
+
+    def get_collection_manifest(self) -> dict:
+        return {
+            "collection": self.collection,
+            "canonical_model": "LAI-TEQUMSA/TEQUMSA-Organism-v14.377-F987-ANU-UNIFIED",
+            "phase": 25,
+            "dataset_count": len(COLLECTION_DATASETS),
+            "spaces_active": 55,
+            "anchor_datasets": COLLECTION_DATASETS[:3],
+        }