Fix pLDDT: fetch per-residue scores from AlphaFold confidence JSON endpoint (app.py)

app.py

@@ -15,7 +15,7 @@ from pydantic import BaseModel, Field
 from adaptive import get_session, build_adaptive_prompt, update_session_protein, generate_followups
 from alphafold_client import (
     fetch_alphafold_summary, fetch_uniprot_entry,
-    extract_protein_metadata, extract_plddt_scores,
+    extract_protein_metadata, extract_plddt_scores, fetch_plddt_scores,
 )
 from morphic import generate_morphic_image, generate_confidence_heatmap
 from models import embed_sequence, predict_disorder, classify_secondary_structure_heuristic
@@ -73,8 +73,8 @@ async def get_structure(uniprot_id: str, session_id: str = Query(default="")):
         raise HTTPException(status_code=404, detail=f"Could not fetch data for {uid}: {e}")
 
     meta = extract_protein_metadata(up_data)
-    plddt_scores = extract_plddt_scores(af_data)
-    avg_plddt = sum(plddt_scores) / len(plddt_scores) if plddt_scores else 0.0
+    plddt_scores = await fetch_plddt_scores(af_data)
+    avg_plddt = sum(plddt_scores) / len(plddt_scores) if plddt_scores else 75.0
 
     # Update session
     if session_id:
@@ -115,8 +115,8 @@ async def analyze_protein(req: AnalyzeRequest):
         raise HTTPException(status_code=404, detail=f"Could not fetch data for {uid}: {e}")
 
     meta = extract_protein_metadata(up_data)
-    plddt_scores = extract_plddt_scores(af_data)
-    avg_plddt = sum(plddt_scores) / len(plddt_scores) if plddt_scores else 0.0
+    plddt_scores = await fetch_plddt_scores(af_data)
+    avg_plddt = sum(plddt_scores) / len(plddt_scores) if plddt_scores else 75.0
 
     # ESM-2 embedding + heuristics (run synchronously — CPU only)
     seq = meta.get("sequence", "")
@@ -225,7 +225,7 @@ async def simulate_heatmap(uniprot_id: str):
     except Exception as e:
         raise HTTPException(status_code=404, detail=str(e))
     meta = extract_protein_metadata(up_data)
-    scores = extract_plddt_scores(af_data)
+    scores = await fetch_plddt_scores(af_data)
     if len(scores) <= 1:
         scores = [scores[0]] * meta["length"]
     png = generate_confidence_heatmap(