Update app.py

app.py

@@ -1,13 +1,18 @@
-# app.py — Self-contained (no external 'agents' package needed)
-import os, re, json
+# app.py — Biomedical Device Troubleshooting Assistant (Education-only)
+# Self-contained: no external 'agents' package needed.
+
+import os, io, re, json
 from typing import Any, Callable, Dict, List, Optional
 from dataclasses import dataclass, field
 
 import chainlit as cl
 from dotenv import load_dotenv
 from openai import AsyncOpenAI as _SDKAsyncOpenAI
+from pypdf import PdfReader
 
-# ========= Minimal "agents" shim (inline) =========
+# =========================
+# Minimal "agents" shim
+# =========================
 def set_tracing_disabled(disabled: bool = True):
     return disabled
 
@@ -97,7 +102,7 @@ class Runner:
                     })
                 continue
 
-            # Final answer
+            # Final
             result_obj = type("Result", (), {})()
             result_obj.final_output = msg.content or ""
             result_obj.context = context or {}
@@ -110,7 +115,9 @@ class Runner:
         result_obj.final_output_as = lambda *_: result_obj.final_output
         return result_obj
 
-# ========= Setup: provider auto-detect =========
+# =========================
+# Provider setup (Gemini or OpenAI)
+# =========================
 load_dotenv()
 GEMINI_API_KEY = os.getenv("Gem")
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
@@ -124,145 +131,32 @@ elif OPENAI_API_KEY:
     BASE_URL = None
     MODEL_ID = "gpt-4o-mini"
 else:
-    raise RuntimeError("Missing GEMINI_API_KEY or OPENAI_API_KEY in env/secrets.")
+    raise RuntimeError("Missing GEMINI_API_KEY or OPENAI_API_KEY in your environment.")
 
 set_tracing_disabled(True)
 ext_client = AsyncOpenAI(api_key=API_KEY, base_url=BASE_URL)
 llm_model = OpenAIChatCompletionsModel(model=MODEL_ID, openai_client=ext_client)
 
-# ========= Tools =========
-@function_tool
-def infer_modality_from_filename(filename: str) -> dict:
-    """
-    Guess modality (MRI / X-ray / CT / Ultrasound) from filename hints.
-    Returns: {"modality": "<guess or unknown>"}
-    """
-    f = (filename or "").lower()
-    mapping = {
-        "xray": "X-ray", "x_ray": "X-ray", "xr": "X-ray", "chest": "X-ray",
-        "mri": "MRI", "t1": "MRI", "t2": "MRI", "flair": "MRI", "dwi": "MRI", "adc": "MRI", "swi": "MRI",
-        "ct": "CT", "cta": "CT",
-        "ultrasound": "Ultrasound", "usg": "Ultrasound", "echo": "Ultrasound",
-    }
-    for k, v in mapping.items():
-        if k in f:
-            return {"modality": v}
-    return {"modality": "unknown"}
+# =========================
+# Topic/safety guardrails
+# =========================
+ALLOWED_COMMANDS = ("/help", "/policy", "/manual", "/clear", "/hits")
 
-@function_tool
-def imaging_reference_guide(modality: str) -> dict:
-    """
-    Educational bullets for acquisition, artifacts, preprocessing, and study tips by modality.
-    No diagnosis. Teaching focus only.
-    """
-    mod = (modality or "").strip().lower()
-    if mod in ["xray", "x-ray", "x_ray"]:
-        return {
-            "acquisition": [
-                "Projection radiography with ionizing radiation.",
-                "Views: AP/PA/lateral; tune kVp/mAs and positioning.",
-                "Grids & collimation reduce scatter to improve contrast."
-            ],
-            "artifacts": [
-                "Motion blur; under/overexposure.",
-                "Grid cut-off; foreign objects (jewelry, buttons).",
-                "Magnification/distortion from object-detector distance."
-            ],
-            "preprocessing": [
-                "Denoising (median/NLM); histogram equalization.",
-                "Window/level exploration for bone vs soft tissue.",
-                "Edge enhancement (unsharp) sparingly to avoid halos."
-            ],
-            "study_tips": [
-                "Use ABCDE (for CXR), check markers/labels/devices.",
-                "Compare sides and prior images.",
-                "Practice with checklists for consistency."
-            ],
-        }
-    if mod in ["mri", "mr"]:
-        return {
-            "acquisition": [
-                "MR signal via RF pulses; sequences define contrast.",
-                "Common: T1, T2, FLAIR, DWI/ADC, GRE/SWI.",
-                "TR/TE/flip angle trade off SNR, contrast, scan time."
-            ],
-            "artifacts": [
-                "Motion/ghosting; susceptibility near metal/air.",
-                "Chemical shift; Gibbs ringing.",
-                "B0/B1 inhomogeneity causing intensity bias."
-            ],
-            "preprocessing": [
-                "Bias-field correction (N4).",
-                "Denoising (NLM); registration/normalization.",
-                "Skull stripping (brain); intensity standardization."
-            ],
-            "study_tips": [
-                "Know sequence emphases (T1 anatomy; T2 fluid; FLAIR edema).",
-                "Review diffusion for acute ischemia (check ADC).",
-                "Keep window/level consistent across timepoints."
-            ],
-        }
-    if mod in ["ct"]:
-        return {
-            "acquisition": [
-                "Helical CT; HU reflect attenuation.",
-                "Recon kernels affect sharpness vs noise.",
-                "Contrast timing (arterial/venous) per question."
-            ],
-            "artifacts": [
-                "Beam hardening streaks; partial volume; motion.",
-                "Metal artifacts; MAR/iterative recon help."
-            ],
-            "preprocessing": [
-                "Denoising (bilateral/NLM) with edge preservation.",
-                "Window/level by organ system (lung, mediastinum, bone).",
-                "Metal artifact reduction if available."
-            ],
-            "study_tips": [
-                "Use standard planes; scroll systematically.",
-                "Check multiple windows; annotate size/location; HU if needed.",
-                "Compare with priors when teaching cases."
-            ],
-        }
-    # Fallback (generic)
-    return {
-        "acquisition": [
-            "Acquisition parameters set contrast, resolution, noise.",
-            "Positioning & motion control drive image quality."
-        ],
-        "artifacts": [
-            "Motion blur or ghosting; foreign objects/hardware can streak.",
-            "Under/overexposure or parameter misconfiguration."
-        ],
-        "preprocessing": [
-            "Denoising & contrast normalization aid teaching clarity.",
-            "Registration & standard planes for consistent review."
-        ],
-        "study_tips": [
-            "Adopt a checklist; compare bilaterally or across time.",
-            "Understand modality-specific controls (window/level, sequences)."
-        ],
-    }
-
-@function_tool
-def file_facts(filename: str, size_bytes: str) -> dict:
-    """Return simple file facts (name and size)."""
-    return {"filename": filename, "size_bytes": size_bytes}
-
-# ========= Guardrails =========
-ALLOWED_COMMANDS = ("/help", "/policy")
 TOPIC_KEYWORDS = [
-    "imaging","image","radiology","biomedical","device","equipment","oem","modality",
-    "acquisition","artifact","preprocessing","window","level","sequence","kVp","mAs",
-    "mri","t1","t2","flair","dwi","adc","swi","ct","xray","x-ray","ultrasound","usg","echo"
+    "biomedical","biomed","device","equipment","oem","service manual",
+    "troubleshoot","fault","error","alarm","probe","sensor","lead","cable",
+    "battery","power","calibration","qc","verification","analyzer","self-test",
+    "ventilator","defibrillator","infusion","pump","ecg","oximeter","nibp",
+    "monitor","ultrasound","anesthesia","syringe","suction","spirometer","glucometer"
 ]
+
 RE_FORBIDDEN_CLINICAL = re.compile(r"\b(diagnos(e|is|tic)|prescrib|medicat|treat(ment|ing)?|dose|drug|therapy)\b", re.I)
-RE_INVASIVE_REPAIR    = re.compile(r"\b(open(ing)?\s+(device|casing|cover)|solder|board[- ]level|reflow|replace\s+(capacitor|ic))\b", re.I)
+RE_INVASIVE_REPAIR    = re.compile(r"\b(open(ing)?\s+(device|casing|cover)|solder|reflow|board[- ]level|replace\s+(capacitor|ic))\b", re.I)
 RE_ALARM_BYPASS       = re.compile(r"\b(bypass|disable|silence)\s+(alarm|alert|safety|interlock)\b", re.I)
-RE_FIRMWARE_TAMPER    = re.compile(r"\b(firmware|bootloader|root|jailbreak|unlock\s+(service|engineer)\s*mode|password\s*override)\b", re.I)
+RE_FIRMWARE_TAMPER    = re.compile(r"\b(firmware|bootloader|root|jailbreak|unlock\s+(service|engineer)\s*mode|password\s*override|backdoor)\b", re.I)
 RE_EMAIL = re.compile(r"[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,}", re.I)
 RE_PHONE = re.compile(r"(?:\+\d{1,3}[-\s.]*)?(?:\(?\d{3,4}\)?[-\s.]*)?\d{3}[-\s.]?\d{4}")
-RE_IDHINT = re.compile(r"\b(CNIC|MRN|passport|nid|national\s*id)\b", re.I)
+RE_IDHINT = re.compile(r"\b(CNIC|MRN|passport|nid|national\s*id|social\s*security|aadhaar)\b", re.I)
 
 def on_topic(text: str) -> bool:
     low = (text or "").lower().strip()
@@ -283,124 +177,237 @@ def local_guard(text: str) -> List[str]:
         issues.append("phi_share_or_collect")
     return issues
 
-# ========= Tutor Agent =========
-tutor_instructions = (
-    "You are a Biomedical Imaging **Education** Tutor. Explain how images are acquired, common artifacts, "
-    "and preprocessing for study/teaching. Do NOT diagnose or give clinical advice.\n\n"
-    "Output a concise, structured answer with sections in this order:\n"
-    "1) Acquisition overview\n"
-    "2) Common artifacts\n"
-    "3) Preprocessing methods (education/study only)\n"
-    "4) Study tips\n"
-    "5) Caution (one line: education-only; consult qualified clinicians for clinical questions)\n\n"
-    "Use tools to infer modality (from filename) and fetch a modality-specific reference guide. "
-    "If modality unclear, provide a generic overview and invite the user to specify."
-)
-tutor_agent = Agent(
-    name="Biomedical Imaging Tutor",
-    instructions=tutor_instructions,
-    model=llm_model,
-    tools=[infer_modality_from_filename, imaging_reference_guide, file_facts],
+# =========================
+# Manual handling + search
+# =========================
+def extract_pdf_pages(data: bytes):
+    pages = []
+    reader = PdfReader(io.BytesIO(data))
+    for i, pg in enumerate(reader.pages, start=1):
+        try:
+            txt = pg.extract_text() or ""
+        except Exception:
+            txt = ""
+        pages.append({"page": i, "text": txt})
+    return pages
+
+def extract_txt_pages(data: bytes, chunk_chars: int = 1600):
+    try: txt = data.decode("utf-8", errors="ignore")
+    except Exception: txt = ""
+    return [{"page": i+1, "text": txt[i:i+chunk_chars]} for i in range(0, len(txt), chunk_chars)] or [{"page": 1, "text": ""}]
+
+def manual_hits(pages, query: str, topk: int = 3):
+    if not pages: return []
+    terms = [w for w in re.findall(r"\w+", (query or "").lower()) if len(w) > 2]
+    scored = []
+    for p in pages:
+        low = (p.get("text") or "").lower()
+        score = sum(low.count(t) for t in terms)
+        if score > 0: scored.append((score, p))
+    scored.sort(key=lambda x: x[0], reverse=True)
+    hits = [p for _, p in scored[:topk]] or pages[:1]
+    def excerpt(text: str, window: int = 420):
+        t = text or ""
+        low = t.lower()
+        idxs = [low.find(tk) for tk in terms if tk in low]
+        start = max(0, min([i for i in idxs if i >= 0], default=0) - window)
+        end = min(len(t), start + 2*window)
+        snippet = re.sub(r"\s+", " ", t[start:end]).strip()
+        # basic PHI redaction
+        snippet = RE_EMAIL.sub("[REDACTED_EMAIL]", snippet)
+        snippet = RE_PHONE.sub("[REDACTED_PHONE]", snippet)
+        snippet = RE_IDHINT.sub("[ID]", snippet)
+        return snippet
+    return [f"[p.{h['page']}] {excerpt(h.get('text',''))}" for h in hits]
+
+# =========================
+# Tools (deterministic helpers)
+# =========================
+@function_tool
+def quick_reference(device_desc: str) -> dict:
+    """
+    Deterministic education-only bullets based on free-text device description.
+    """
+    d = (device_desc or "").lower()
+    life_critical = any(k in d for k in ["ventilator","defibrillator","infusion pump"])
+    return {
+        "safety": [
+            "If attached to a patient, ensure backup/alternative monitoring before checks.",
+            "No invasive service; do not open casing; follow OEM and facility policy.",
+            "Do not bypass or silence alarms beyond OEM instructions."
+        ] + (["Life-support device: remove from service and use backup if malfunction suspected."] if life_critical else []),
+        "common_faults": [
+            "Power/battery supply issues or loose connections.",
+            "Damaged/incorrect accessories (leads, probes, tubing).",
+            "Configuration/profile mismatch for the clinical setup.",
+            "Environmental interference (EMI), filters/clogs, or mechanical obstruction."
+        ],
+        "quick_checks": [
+            "Note model/serial/firmware and any error codes.",
+            "Verify power source/battery; try another outlet; reseat user-removable battery.",
+            "Inspect and reseat accessories; swap with a known-good if available.",
+            "Confirm settings match procedure; restore defaults if safe.",
+            "Run device self-test if available and review OEM quick-reference."
+        ],
+        "qc": [
+            "Verify against a reference/simulator where applicable (ECG, SpO2, NIBP, flow, etc.).",
+            "Confirm scheduled electrical safety tests per policy.",
+            "Document results and compare with historical QC."
+        ],
+        "escalate": [
+            "Any failed self-test or QC/safety test.",
+            "Persistent faults after basic checks or physical damage/liquid ingress.",
+            "Life-critical pathway: remove from service immediately and escalate to Biomed/OEM."
+        ]
+    }
+
+# =========================
+# LLM troubleshooting plan
+# =========================
+SYSTEM_PROMPT = (
+    "You are a biomedical device troubleshooting assistant for clinical engineers.\n"
+    "STRICT SCOPE: Education-only device troubleshooting. No diagnosis/treatment, no invasive repair, no alarm bypass, no firmware hacks, "
+    "no collection of personal identifiers. Defer to OEM manuals and local policy if any conflict.\n\n"
+    "Given a device description and symptom, produce concise bullet lists with exactly these sections:\n"
+    "1) Safety First (non-invasive, patient-first)\n"
+    "2) Likely Causes (ranked)\n"
+    "3) Step-by-Step Checks (do-not-open device; do-not-bypass alarms)\n"
+    "4) QC/Calibration (what to verify and with what reference/simulator)\n"
+    "5) Escalate When (clear triggers)\n"
+    "End with a one-line summary.\n"
+    "If the text suggests a life-critical device (ventilator/defibrillator/infusion pump) and patient connected, start with: "
+    "REMOVE FROM SERVICE & USE BACKUP — then proceed with safe checks off-patient."
 )
 
-# ========= UI strings =========
+async def call_llm(user_desc: str, manual_excerpts: str = "") -> str:
+    user_block = f"Device & symptom:\n{user_desc.strip()}"
+    if manual_excerpts:
+        user_block += f"\n\nManual excerpts (for reference; OEM prevails if conflict):\n{manual_excerpts.strip()}"
+    resp = await ext_client.client.chat.completions.create(
+        model=MODEL_ID,
+        messages=[
+            {"role": "system", "content": SYSTEM_PROMPT},
+            {"role": "user", "content": user_block},
+        ],
+    )
+    return resp.choices[0].message.content or ""
+
+# =========================
+# UI strings
+# =========================
 WELCOME = (
-    "🎓 **Multimodal Biomedical Imaging Tutor**\n\n"
-    "Upload an **MRI/X-ray/CT/Ultrasound** image (PNG/JPG), then ask what you’d like to learn.\n"
-    "I’ll explain acquisition, common artifacts, and preprocessing for study.\n\n"
-    "⚠️ Education only — no diagnosis or clinical advice."
+    "🛠️ **Biomedical Device Troubleshooting Assistant**\n"
+    "Education-only. No diagnosis, no invasive service, no alarm bypass. OEM manual & policy rule the day.\n\n"
+    "Type your **device & symptom** (e.g., “Infusion pump occlusion alarm”).\n"
+    "Commands: **/manual** upload PDF/TXT, **/hits** show top manual matches, **/clear** remove manual, **/policy** view rules."
 )
 POLICY = (
     "🛡️ **Safety & Scope Policy**\n"
-    "- Scope: biomedical **imaging education/troubleshooting** only.\n"
+    "- Scope: biomedical **device troubleshooting** (education-only).\n"
     "- No clinical advice (diagnosis/treatment/dosing/medications).\n"
-    "- No invasive repair steps (opening casing, soldering, board-level).\n"
+    "- No invasive repairs (opening casing, soldering, board-level).\n"
     "- No alarm bypass or firmware tampering.\n"
     "- No collecting/sharing personal identifiers.\n"
     "- OEM manuals & local policy take priority."
 )
 REFUSAL = (
     "🚫 I can’t help with diagnosis/treatment, invasive repair, alarm bypass, firmware hacks, or collecting personal data.\n"
-    "I can explain **imaging acquisition, artifacts, and preprocessing** for education."
+    "I can guide **safe, non-invasive troubleshooting** and educational QC steps."
 )
 
-# ========= Chainlit flow =========
+# =========================
+# Chainlit flow
+# =========================
 @cl.on_chat_start
-async def on_chat_start():
+async def start():
+    cl.user_session.set("manual", None)
     await cl.Message(content=WELCOME).send()
-    files = await cl.AskFileMessage(
-        content="Please upload an **MRI/X-ray/CT/Ultrasound** image (PNG/JPG).",
-        accept=["image/png", "image/jpeg"],
-        max_size_mb=15,
-        max_files=1,
-        timeout=180,
-    ).send()
-    if files:
-        f = files[0]
-        cl.user_session.set("last_file_name", f.name)
-        cl.user_session.set("last_file_size", f.size)
-        await cl.Message(content=f"Received **{f.name}** ({f.size} bytes). Now tell me what to explain.").send()
-    else:
-        await cl.Message(content="No file uploaded yet. You can still ask general imaging questions.").send()
 
 @cl.on_message
-async def on_message(message: cl.Message):
+async def main(message: cl.Message):
     text = (message.content or "").strip()
 
     # Commands
-    if text.lower().startswith("/help"):
+    low = text.lower()
+    if low.startswith("/help"):
         await cl.Message(content=WELCOME).send(); return
-    if text.lower().startswith("/policy"):
+    if low.startswith("/policy"):
         await cl.Message(content=POLICY).send(); return
 
-    # Topic & guardrails
+    if low.startswith("/manual"):
+        files = await cl.AskFileMessage(
+            content="Upload the **service manual** (PDF or TXT). Max ~20 MB.",
+            accept=["application/pdf", "text/plain"],
+            max_files=1, max_size_mb=20, timeout=240
+        ).send()
+        if not files:
+            await cl.Message(content="No file received.").send(); return
+        f = files[0]
+        data = getattr(f, "content", None)
+        if data is None and getattr(f, "path", None):
+            with open(f.path, "rb") as fh: data = fh.read()
+        try:
+            pages = extract_pdf_pages(data) if (f.mime == "application/pdf" or f.name.lower().endswith(".pdf")) else extract_txt_pages(data)
+        except Exception as e:
+            await cl.Message(content=f"Couldn't read the manual: {e}").send(); return
+        cl.user_session.set("manual", {"name": f.name, "pages": pages})
+        await cl.Message(content=f"✅ Manual indexed: **{f.name}** — {len(pages)} page-chunks.").send()
+        return
+
+    if low.startswith("/clear"):
+        cl.user_session.set("manual", None)
+        await cl.Message(content="Manual cleared.").send()
+        return
+
+    if low.startswith("/hits"):
+        m = cl.user_session.get("manual")
+        if not m or not m.get("pages"):
+            await cl.Message(content="No manual attached. Use **/manual** to upload one.").send(); return
+        hits = manual_hits(m["pages"], text.replace("/hits", "").strip() or "device fault error alarm", topk=3)
+        if not hits: hits = ["No obvious matches — try different wording."]
+        await cl.Message(content="### 🔎 Manual Matches\n" + "\n\n".join(hits)).send()
+        return
+
+    # Topic & local safety guard
     if not on_topic(text):
         await cl.Message(
-            content="I only discuss **biomedical imaging education** (acquisition, artifacts, preprocessing). "
-                    "Please ask about MRI/X-ray/CT/Ultrasound imaging."
-        ).send(); return
-
+            content="I only support **biomedical device troubleshooting**. "
+                    "Describe the device & symptom (e.g., “ECG noisy baseline”), or upload a manual with **/manual**."
+        ).send()
+        return
     issues = local_guard(text)
     if issues:
-        await cl.Message(content=REFUSAL + "\n\n" + POLICY).send(); return
-
-    # Context from uploaded file
-    file_name = cl.user_session.get("last_file_name")
-    file_size = cl.user_session.get("last_file_size")
-    context_lines = []
-    if file_name: context_lines.append(f"File: {file_name}")
-    if file_size is not None: context_lines.append(f"Size: {file_size} bytes")
-    context_block = "\n".join(context_lines)
-
-    # Compose query for tutor
-    user_query = text if not context_block else f"{text}\n\n[Context]\n{context_block}"
-
-    # Run tutor
+        await cl.Message(content=REFUSAL + "\n\n" + POLICY).send()
+        return
+
+    # Manual excerpts (optional)
+    manual = cl.user_session.get("manual")
+    excerpts = ""
+    if manual and manual.get("pages"):
+        query = text
+        hits = manual_hits(manual["pages"], query, topk=3)
+        excerpts = "\n".join(hits)
+
+    # Deterministic quick reference (for reliability)
+    ref = quick_reference(text)
+    def bullets(arr: List[str]): return "\n".join(f"- {x}" for x in (arr or [])) or "-"
+
+    quick_md = (
+        f"### 📘 Quick Reference\n"
+        f"**Safety**\n{bullets(ref.get('safety'))}\n\n"
+        f"**Common Faults**\n{bullets(ref.get('common_faults'))}\n\n"
+        f"**Quick Checks**\n{bullets(ref.get('quick_checks'))}\n\n"
+        f"**QC / Calibration**\n{bullets(ref.get('qc'))}\n\n"
+        f"**Escalate If**\n{bullets(ref.get('escalate'))}\n\n"
+        f"> ⚠️ Education-only. Refer to OEM manual & policy. No invasive service.\n"
+    )
+
+    # LLM troubleshooting plan
     try:
-        result = await Runner.run(tutor_agent, user_query)
-    except InputGuardrailTripwireTriggered:
-        await cl.Message(content=REFUSAL).send(); return
+        answer = await call_llm(text, excerpts)
     except Exception as e:
-        await cl.Message(content=f"⚠️ Tutor failed: {e}").send(); return
+        await cl.Message(content=f"{quick_md}\n---\n⚠️ LLM call failed: {e}").send()
+        return
 
-    # Deterministic quick reference (tools)
-    try:
-        mod_guess = infer_modality_from_filename(file_name or "")
-        modality = mod_guess.get("modality", "unknown")
-        guide = imaging_reference_guide(modality)
-        def bullets(arr: List[str]): return "\n".join(f"- {x}" for x in (arr or [])) or "- (general)"
-        facts_md = (
-            f"### 📁 File\n- Name: `{file_name or 'unknown'}`\n- Size: `{file_size if file_size is not None else 'unknown'} bytes`\n\n"
-            f"### 🔎 Modality (guess)\n- {modality}\n\n"
-            f"### 📚 Reference Guide\n"
-            f"**Acquisition**\n{bullets(guide.get('acquisition'))}\n\n"
-            f"**Common Artifacts**\n{bullets(guide.get('artifacts'))}\n\n"
-            f"**Preprocessing**\n{bullets(guide.get('preprocessing'))}\n\n"
-            f"**Study Tips**\n{bullets(guide.get('study_tips'))}\n\n"
-            f"> ⚠️ Education only — no diagnosis.\n"
-        )
-    except Exception:
-        facts_md = ""
-
-    answer = result.final_output or "I couldn’t generate an explanation."
-    await cl.Message(content=f"{facts_md}\n---\n{answer}").send()
+    prefix = f"📄 Using manual: **{manual['name']}**\n\n" if (manual and manual.get('name')) else ""
+    await cl.Message(content=f"{quick_md}\n---\n{prefix}{answer or 'I couldn’t generate a plan.'}").send()