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api.py

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+"""
+SynthGuard Track 1 API — FastAPI endpoint for Track 3 dashboard integration.
+
+Run:
+    pip install fastapi uvicorn
+    python app/api.py
+
+Or in Colab (after training):
+    !uvicorn app.api:app --host 0.0.0.0 --port 8000 &
+"""
+
+import json
+import math
+import os
+import pickle
+from collections import Counter
+from itertools import product
+from pathlib import Path
+from typing import Optional
+
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel, Field
+
+app = FastAPI(
+    title="SynthGuard API",
+    description="Track 1 biosecurity screening engine for AIxBio Hackathon 2026",
+    version="1.0.0",
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# ── Feature extraction (must match notebook) ─────────────────────────────────
+
+VOCAB = {k: ["".join(p) for p in product("ACGT", repeat=k)] for k in [3, 4, 5, 6]}
+
+
+def extract_features(seq: str) -> list[float]:
+    seq = seq.upper().replace("U", "T")
+    n = max(len(seq), 1)
+    cnt = Counter(seq)
+    total = sum(cnt.values())
+
+    feats = [
+        n,
+        (cnt.get("G", 0) + cnt.get("C", 0)) / n,
+        (cnt.get("A", 0) + cnt.get("T", 0)) / n,
+        cnt.get("N", 0) / n,
+        max(cnt.values()) / n if cnt else 0,
+        -sum((c / total) * math.log2(c / total) for c in cnt.values() if c > 0),
+    ]
+    for k in [3, 4, 5, 6]:
+        kmer_cnt = Counter(seq[i : i + k] for i in range(n - k + 1))
+        total_k = max(n - k + 1, 1)
+        feats.extend(kmer_cnt.get(km, 0) / total_k for km in VOCAB[k])
+    return feats
+
+
+# ── Model loading ─────────────────────────────────────────────────────────────
+
+MODEL_DIR = Path(os.environ.get("SYNTHGUARD_MODEL_DIR", "models/synthguard_kmer"))
+
+_general_model = None
+_short_model = None
+_meta = None
+
+
+def _load_models():
+    global _general_model, _short_model, _meta
+    if _general_model is not None:
+        return
+
+    general_path = MODEL_DIR / "general_model.pkl"
+    short_path = MODEL_DIR / "short_model.pkl"
+    meta_path = MODEL_DIR / "meta.json"
+
+    if not general_path.exists():
+        raise RuntimeError(
+            f"Models not found at {MODEL_DIR}. "
+            "Run notebooks/synthguard_full.ipynb first to train and save models."
+        )
+
+    with open(general_path, "rb") as f:
+        _general_model = pickle.load(f)
+    with open(short_path, "rb") as f:
+        _short_model = pickle.load(f)
+    with open(meta_path) as f:
+        _meta = json.load(f)
+
+
+@app.on_event("startup")
+async def startup():
+    try:
+        _load_models()
+        print(f"SynthGuard models loaded from {MODEL_DIR}")
+    except RuntimeError as e:
+        print(f"WARNING: {e}\nAPI will return errors until models are loaded.")
+
+
+# ── Request / Response schemas ────────────────────────────────────────────────
+
+
+class ScreenRequest(BaseModel):
+    sequence: str = Field(..., description="DNA or RNA sequence (IUPAC nucleotides)")
+    threshold_review: float = Field(0.4, ge=0.0, le=1.0)
+    threshold_escalate: float = Field(0.7, ge=0.0, le=1.0)
+
+
+class ScreenResponse(BaseModel):
+    risk_score: float
+    decision: str  # ALLOW | REVIEW | ESCALATE
+    sequence_length: int
+    sequence_type: str
+    gc_content: float
+    evidence: list[str]
+    model_used: str
+    error: Optional[str] = None
+
+
+class BatchScreenRequest(BaseModel):
+    sequences: list[str]
+    threshold_review: float = 0.4
+    threshold_escalate: float = 0.7
+
+
+class BatchScreenResponse(BaseModel):
+    results: list[ScreenResponse]
+    summary: dict
+
+
+# ── Core screener ─────────────────────────────────────────────────────────────
+
+
+def _screen_one(
+    seq: str,
+    threshold_review: float = 0.4,
+    threshold_escalate: float = 0.7,
+) -> dict:
+    _load_models()
+
+    seq = seq.upper().replace("U", "T").strip()
+    if len(seq) < 10:
+        return ScreenResponse(
+            risk_score=0.0,
+            decision="ALLOW",
+            sequence_length=len(seq),
+            sequence_type="DNA",
+            gc_content=0.0,
+            evidence=[],
+            model_used="none",
+            error="Sequence too short (<10bp)",
+        ).dict()
+
+    import numpy as np
+
+    feats = np.array([extract_features(seq)])
+    n = len(seq)
+    cnt = Counter(seq)
+    gc = (cnt.get("G", 0) + cnt.get("C", 0)) / n
+
+    if n < 150:
+        prob = _short_model.predict_proba(feats)[0, 1]
+        model_used = "short-seq specialist"
+    else:
+        prob = _general_model.predict_proba(feats)[0, 1]
+        model_used = "general triage"
+
+    if prob >= threshold_escalate:
+        decision = "ESCALATE"
+    elif prob >= threshold_review:
+        decision = "REVIEW"
+    else:
+        decision = "ALLOW"
+
+    evidence = []
+    if n < 150:
+        evidence.append(f"Short sequence ({n}bp): specialist model active")
+    if gc > 0.65:
+        evidence.append(f"High GC content ({gc:.0%})")
+    elif gc < 0.30:
+        evidence.append(f"Low GC content ({gc:.0%})")
+    entropy = -sum((c / n) * math.log2(c / n) for c in cnt.values() if c > 0)
+    if entropy < 1.5:
+        evidence.append(f"Low complexity (entropy={entropy:.2f})")
+    evidence.append(f"Risk score: {prob:.3f}")
+    evidence.append(f"Model: {model_used}")
+
+    return {
+        "risk_score": round(float(prob), 4),
+        "decision": decision,
+        "sequence_length": n,
+        "sequence_type": "DNA",
+        "gc_content": round(gc, 3),
+        "evidence": evidence,
+        "model_used": model_used,
+    }
+
+
+# ── Endpoints ─────────────────────────────────────────────────────────────────
+
+
+@app.get("/health")
+async def health():
+    models_loaded = _general_model is not None
+    return {
+        "status": "ok" if models_loaded else "models_not_loaded",
+        "models_loaded": models_loaded,
+        "model_dir": str(MODEL_DIR),
+    }
+
+
+@app.post("/screen", response_model=ScreenResponse)
+async def screen_sequence(req: ScreenRequest):
+    try:
+        result = _screen_one(req.sequence, req.threshold_review, req.threshold_escalate)
+        return ScreenResponse(**result)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/screen/batch", response_model=BatchScreenResponse)
+async def screen_batch(req: BatchScreenRequest):
+    if len(req.sequences) > 1000:
+        raise HTTPException(status_code=400, detail="Max 1000 sequences per batch")
+    results = []
+    for seq in req.sequences:
+        result = _screen_one(seq, req.threshold_review, req.threshold_escalate)
+        results.append(ScreenResponse(**result))
+
+    decisions = [r.decision for r in results]
+    summary = {
+        "total": len(results),
+        "allow": decisions.count("ALLOW"),
+        "review": decisions.count("REVIEW"),
+        "escalate": decisions.count("ESCALATE"),
+        "flag_rate": round(
+            (decisions.count("REVIEW") + decisions.count("ESCALATE")) / max(len(results), 1), 3
+        ),
+    }
+    return BatchScreenResponse(results=results, summary=summary)
+
+
+@app.get("/model/info")
+async def model_info():
+    if _meta is None:
+        raise HTTPException(status_code=503, detail="Models not loaded")
+    return _meta
+
+
+# ── BioLens adapter (Track 3 integration) ────────────────────────────────────
+
+_CATEGORY_BANK = {
+    "DNA": {
+        "SAFE":   ["Routine metabolic gene signature", "Common structural cassette", "Low-concern regulatory context"],
+        "REVIEW": ["Ambiguous host-interaction signal", "Regulatory activity worth analyst review", "Unresolved functional control pattern"],
+        "HIGH":   ["Elevated host-interaction signature", "Escalation-priority functional signal", "High-concern regulation-linked pattern"],
+    },
+    "PROTEIN": {
+        "SAFE":   ["Routine enzyme-like profile", "Low-concern scaffold signature", "Common cellular maintenance pattern"],
+        "REVIEW": ["Ambiguous membrane-associated profile", "Unresolved signaling-like pattern", "Review-level interaction motif cluster"],
+        "HIGH":   ["Elevated interaction-associated profile", "Escalation-priority effector-like pattern", "High-concern modulation signature"],
+    },
+}
+
+
+def _pick_category(seq_type: str, risk_level: str, seq: str) -> str:
+    import hashlib
+    bank = _CATEGORY_BANK.get(seq_type, _CATEGORY_BANK["DNA"])[risk_level]
+    idx = int(hashlib.sha256(seq[:64].encode()).hexdigest()[:8], 16) % len(bank)
+    return bank[idx]
+
+
+def _build_threat_breakdown(seq: str, prob: float) -> dict:
+    n = max(len(seq), 1)
+    cnt = Counter(seq)
+    gc = (cnt.get("G", 0) + cnt.get("C", 0)) / n
+    motif_hits = sum(seq.count(m) for m in ("ATG", "TATA", "CGCG", "GGG"))
+    pathogenicity   = min(max(prob * 0.85 + abs(gc - 0.5) * 0.3, 0.0), 1.0)
+    evasion         = min(max(prob * 0.7 - abs(gc - 0.5) * 0.2, 0.0), 1.0)
+    synthesis_feas  = min(max(0.9 - n / 8000, 0.1), 1.0)
+    env_resilience  = min(max(0.3 + gc * 0.4, 0.0), 1.0)
+    host_range      = min(max(prob * 0.6 + min(motif_hits * 0.02, 0.2), 0.0), 1.0)
+    return {
+        "pathogenicity":          round(pathogenicity, 3),
+        "evasion_potential":      round(evasion, 3),
+        "synthesis_feasibility":  round(synthesis_feas, 3),
+        "environmental_resilience": round(env_resilience, 3),
+        "host_range":             round(host_range, 3),
+    }
+
+
+def _build_attribution(seq: str) -> dict:
+    positions = [i for i in range(0, min(len(seq), 300), 7) if seq[i] in "GC"]
+    scores = [round(0.5 + (ord(seq[i]) % 10) / 20, 3) for i in positions]
+    regions = [{"start": 0, "end": min(30, len(seq)),
+                "label": "GC-rich codon region", "score": round(min(len(positions) / 40, 1.0), 3)}]
+    return {"positions": positions[:20], "scores": scores[:20], "regions": regions}
+
+
+class BioLensRequest(BaseModel):
+    sequence: str
+    seq_type: str = "DNA"
+
+
+@app.post("/biolens/screen")
+async def biolens_screen(req: BioLensRequest):
+    """BioLens adapter — speaks the Track 3 contract schema."""
+    try:
+        seq = req.sequence.upper().replace("U", "T").strip()
+        seq_type = req.seq_type.upper() if req.seq_type.upper() in ("DNA", "PROTEIN") else "DNA"
+
+        if len(seq) < 10:
+            return {"ok": False, "hazard_score": None, "risk_level": None,
+                    "confidence": None, "category": None, "explanation": None,
+                    "baseline_result": None, "model_name": "synthguard-kmer", "error": "sequence_too_short"}
+
+        result = _screen_one(seq)
+        prob    = result["risk_score"]
+        decision = result["decision"]
+
+        risk_map = {"ALLOW": "SAFE", "REVIEW": "REVIEW", "ESCALATE": "HIGH"}
+        risk_level = risk_map[decision]
+
+        confidence = round(min(max(abs(prob - 0.5) * 2 + 0.5, 0.5), 0.99), 3)
+
+        exp_map = {
+            "SAFE":   f"SynthGuard k-mer screening found a low-concern codon-usage profile (score {prob:.2f}). No hazard signal detected.",
+            "REVIEW": f"SynthGuard k-mer screening detected an ambiguous codon-usage pattern (score {prob:.2f}). Analyst review recommended.",
+            "HIGH":   f"SynthGuard k-mer screening detected elevated pathogen-like codon bias (score {prob:.2f}). This sequence warrants escalation.",
+        }
+        blast_map = {
+            "SAFE":   "BLAST similarity check: low identity to known hazards — cleared at standard threshold.",
+            "REVIEW": "BLAST similarity check: partial overlap with known hazard families — manual review recommended.",
+            "HIGH":   "BLAST similarity check: sequence likely evades BLAST (AI-designed codon variant) — function-aware flag retained.",
+        }
+
+        return {
+            "ok":             True,
+            "hazard_score":   prob,
+            "risk_level":     risk_level,
+            "confidence":     confidence,
+            "category":       _pick_category(seq_type, risk_level, seq),
+            "explanation":    exp_map[risk_level],
+            "baseline_result": blast_map[risk_level],
+            "model_name":     "synthguard-kmer",
+            "error":          None,
+            "threat_breakdown":  _build_threat_breakdown(seq, prob),
+            "attribution_data":  _build_attribution(seq),
+        }
+    except Exception as e:
+        return {"ok": False, "hazard_score": None, "risk_level": None,
+                "confidence": None, "category": None, "explanation": None,
+                "baseline_result": None, "model_name": "synthguard-kmer", "error": str(e)}
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="0.0.0.0", port=8000)