fix(startup): avoid importing inverse_folding at module load; use local esm/source; add scipy

esm/mcp_output/mcp_plugin/mcp_service.py

@@ -5,7 +5,7 @@ source_path = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__fil
 sys.path.insert(0, source_path)
 
 from fastmcp import FastMCP
-from esm import pretrained, data, inverse_folding, model  # type: ignore
+from esm import pretrained, data, model  # type: ignore
 
 mcp = FastMCP("esm_service")
 
@@ -95,6 +95,8 @@ def generate_fixed_backbone(
         dict: success/result/error. result contains { sampled_sequences, recovery (if native available) }
     """
     try:
+        # Lazy import to avoid requiring torch_geometric unless needed
+        from esm import inverse_folding  # type: ignore
         import torch
         model_obj, _alphabet = pretrained.esm_if1_gvp4_t16_142M_UR50()
         model_obj = model_obj.eval()