File size: 1,458 Bytes
b36f5fb d0d0961 b36f5fb d0d0961 b36f5fb d0d0961 b36f5fb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | from metano_inference import load_model_from_hf, predict_neurosymbolic, SymbolicScorer, ModelConfig
def main():
print("Loading local METANO model...")
# Point directly to the local folder containing your weights and config
local_model_path = "banulaperera/metano"
model = load_model_from_hf(local_model_path)
print("Initializing Symbolic Scorer...")
config = ModelConfig()
scorer = SymbolicScorer(metals=config.metal_elements)
# A sample coordination/organometallic InChI from your notebook
test_inchi = "InChI=1/Fe.Na.H2O4S.H2O.H/c;;1-5(2,3)4;;/h;;(H2,1,2,3,4);1H2;/q;+1;;;-1"
print(f"\nRunning prediction for InChI:\n{test_inchi}\n")
out = predict_neurosymbolic(
model=model,
inchi=test_inchi,
scorer=scorer,
num_candidates=5,
repair_num_candidates=5,
max_repair_rounds=1
)
print("=== TEST RESULTS ===")
print(f"Predicted IUPAC: {out['predicted_iupac']}")
print(f"Hard Fail Triggered: {out['hard_fail']}")
print(f"Combined Score: {out['combined_score']:.3f}")
print(f"Symbolic Score: {out['symbolic_score']:.3f}")
print(f"Neural Score: {out['neural_score']:.3f}")
if out['reasons']:
print(f"Penalty Reasons: {out['reasons']}")
print("\nTop Candidates:")
for cand in out["candidates"][1:]:
print(f" [{cand['combined']:.3f}] {cand['text']}")
if __name__ == "__main__":
main() |