# buddy_math_server/orchestrator.py - V273.0 (SMART CLASSIFICATION + FAST PATH)
import json, re, os, prompts, asyncio, time
from typing import List, Dict, Optional, Any
import sympy as sp
import logging, re
import ocr_strip_engine  # V300: Stitch & Strip OCR engine
from utils.safe_json import safe_extract_json           # V1.0: Canonical JSON extractor
from domain.math_validator import MathPolygraph         # V1.0: SymPy Polygraph
from geometric_sanity import run_geometric_sanity        # V1.0: Geometric Sanity Engine
from domain.processing_strategy import ProcessingStrategy
from domain.ontology import get_allowed_concepts, get_pedagogical_tag
from domain.math_normalizer import MathCanonicalizer
from utils.math_utils import sanitize_latex_for_sympy, aggressive_sympy_sanitizer
from domain.curriculum_classifier import CurriculumClassifier
from domain.proposal_engine import ProposalEngine
from domain.risk_engine import CognitiveRiskEngine
from domain.pedagogical_renderer import PedagogicalRenderer
from domain.validator import ConsistencyGate
from smart_solver import sign_step, resolve_ast_target, execute_action
import domain.telemetry as telemetry
from domain.schemas import BuddyEvent, BuddyState # V8.5: Streaming contract
from firebase_manager import firebase_manager
from config import IS_PRODUCTION, ENV, GEMINI_MODEL, CONFIDENCE_THRESHOLD_HIGH, CONFIDENCE_THRESHOLD_MEDIUM
import google.generativeai as genai
from pydantic import BaseModel, Field

# V318.0: Tutor Response Schema for Structured JSON Output
class TutorInternalAnalytics(BaseModel):
    topic: str = Field(description="The mathematical topic being discussed")
    intent: str = Field(description="The student's intent: 'SOLVE', 'CHECK', or 'CHAT'")
    mastery_score: int = Field(description="Estimated mastery score (0-100) based on this interaction")
    error_analysis: Optional[str] = Field(description="Brief analysis of any errors found")

class TutorResponseSchema(BaseModel):
    student_message: str = Field(description="The encouraging pedagogical response for the student")
    internal_analytics: TutorInternalAnalytics = Field(description="Metadata for system analysis")

# V8.6.9: Global Guardrails (Increased for High-Complexity 5-Unit Problems - V317.8)
GLOBAL_TOKEN_LIMIT = 100000
GLOBAL_TIMEOUT_SEC = 300

# ==================== V7.2: TICKET 1 — AST ENRICHMENT HELPERS ====================


def collect_all_steps(data: dict) -> list:
    """
    V1.0 Polygraph Helper: Deep extraction of all step objects from a response dict.
    Future-proofed to handle nested sub_sections and explanation_steps.
    """
    steps = []
    print(f"🔍 [DEBUG] data type: {type(data)}")
    if not isinstance(data, dict):
        print(f"⚠️ [V1.0] collect_all_steps: Expected dict, got {type(data)}")
        return []
    for section in data.get("sections", []):
        steps.extend(section.get("steps", []))
        # Future-proofing: handle nested structures
        for sub in section.get("sub_sections", []):
            steps.extend(sub.get("steps", []))
        steps.extend(section.get("explanation_steps", []))
    return steps


def build_ast_metadata(math_input: str, category: str) -> dict:
    """
    V7.2 Ticket 1: Builds a rich AST metadata object to pass to LLM #1 (Planner).
    The Planner never receives the raw math string — only this structured metadata.
    """
    variables = []
    constraints = []  # V7.3: domain constraints (placeholder)
    detected_operations = [str(category)]
    estimated_complexity = 0.5

    try:
        parts = aggressive_sympy_sanitizer(math_input)
        free_syms = set()
        for clean_part in parts:
            try:
                expr = sp.sympify(clean_part.replace('=', '-'), evaluate=False)
                free_syms.update(expr.free_symbols)
            except Exception as e:
                logging.debug(f"[AST_METADATA] SymPy parse failed for part: {e}")
                pass

        variables = sorted([str(s) for s in free_syms])
        raw_complexity = CurriculumClassifier.estimate_complexity(math_input)
        estimated_complexity = round(raw_complexity / 10.0, 2)

    except Exception as e:
        logging.warning(f"[AST_METADATA] Failed to enrich metadata: {e}")

    # Build node registry for Planner (IDs → expressions)
    ast_registry = {}
    try:
        parts = aggressive_sympy_sanitizer(math_input)
        for i, part in enumerate(parts):
            ast_registry[f"ast_node_{i}"] = part
    except Exception as e:
        logging.debug(f"[AST_METADATA] Registry build failed: {e}")
        ast_registry["ast_node_0"] = str(math_input)

    return {
        "variables": variables,
        "constraints": constraints,
        "detected_operations": detected_operations,
        "estimated_complexity": estimated_complexity,
        "ast_registry": ast_registry  # Shared secretly with solver; NOT sent to LLM
    }


def _abstract_visual_context(data_anchor: dict) -> dict:
    """
    V7.2 Ticket 1: Strips raw OCR/visual payload and converts to abstract metadata.
    The Planner NEVER receives raw image data.
    """
    if not data_anchor:
        return {}

    # Preserve only safe, abstract fields
    abstract = {}

    graph_relations = []
    if "graphs" in data_anchor:
        for i, g in enumerate(data_anchor["graphs"]):
            graph_relations.append({
                "graph": chr(ord("I") + i),  # I, II, III...
                "zeros": g.get("zeros", 0),
                "type": g.get("type", "unknown")
            })

    if graph_relations:
        abstract["graph_relations"] = graph_relations

    return abstract


# ==================== V7.2: TICKET 5 — UI GATE WHITELIST SCAN ====================

def scan_for_math_leakage(rendered_text: str) -> bool:
    """
    V7.2 Ticket 5: Full Whitelist scan (NOT a blacklist).
    After removing {{...}} placeholders, the remaining text may ONLY contain:
    - Hebrew letters (Unicode block)
    - English letters (for regular words)
    - Spaces and basic punctuation (. , ? ! ' " - :)
    
    Any digit-letter combo, math symbols, trig functions, or equals signs → REJECT.
    """
    # Remove all valid placeholders first
    clean_text = re.sub(r'\{\{\w+\}\}', '', rendered_text).strip()

    if not clean_text:
        return True  # Text was purely placeholders — safe

    ALLOWED_PATTERN = r'^[\u0590-\u05FFa-zA-Z\s.,;:!?\-\"\']+$'

    if re.match(ALLOWED_PATTERN, clean_text):
        return True

    # Log the exact leaking characters for forensics
    violations = re.sub(r'[\u0590-\u05FFa-zA-Z\s.,;:!?\-\"\']+', '', clean_text)
    logging.warning(f"[UI_GATE] Math leakage detected! Offending chars: '{violations[:50]}'")
    return False



def extract_and_parse_json(text: str):
    """V5.7.5 → V1.0: Delegates to canonical safe_extract_json."""
    return safe_extract_json(text, caller="ORCHESTRATOR_LEGACY")


def validate_and_sanitize_response(resp_json, category="GENERAL"):
    """V4.2.16: Validator מדויק - חוסם קוד, מאפשר גיאומטריה (ABC)"""
    has_error = False
    forbidden_terms = ["נגזרת", "גזירה", "אסימפטוטה", "נקודת קיצון"]
    
    print(f"🛡️ [BIT-LOG: VALIDATOR] Checking section in category: {category}")
    
    if "sections" in resp_json:
        for section in resp_json["sections"]:
            for step in section.get("steps", []):
                text = step.get("explanation_text", "")
                # 1. חסימת חדו"א באלגברה (Normalize category for Case Sensitivity Fix)
                if category.upper() != "INVESTIGATION" and any(t in text for t in forbidden_terms):
                    print(f"🚨 [BIT-LOG: VALIDATOR] Calculus leak detected!")
                    has_error = True
                # 2. חסימת פונקציות/קוד (f(x) , import) אבל השארת ABC (Relaxed for False Positives)
                if re.search(r'\b(import|def|class|lambda)\b', text):
                    print(f"🚨 [BIT-LOG: VALIDATOR] Code/Math leak in text: '{text[:20]}'")
                    has_error = True
                    step["explanation_text"] = "הסבר לא זמין עקב חריגה מהחוזה הפדגוגי."

    resp_json["logic_error"] = resp_json.get("logic_error", False) or has_error
    
    # V317.5: UI Sanitization Layer
    if not resp_json.get("logic_error"):
        resp_json = sanitize_llm_output(resp_json)
        
    return resp_json

def unify_data_anchor(raw_data):
    """V317.5: Smart Data Anchor Unification (Prevents key overwrite)"""
    if isinstance(raw_data, dict):
        return raw_data
        
    if isinstance(raw_data, list):
        unified = {}
        for item in raw_data:
            if not isinstance(item, dict): continue
            for key, value in item.items():
                if key in unified:
                    # אם המפתח כבר קיים, נהפוך אותו לרשימה ונוסיף אליו
                    if isinstance(unified[key], list):
                        if value not in unified[key]:
                            unified[key].append(value)
                    else:
                        if unified[key] != value:
                            unified[key] = [unified[key], value]
                else:
                    unified[key] = value
        return unified
        
    return {} # Fallback

def sanitize_llm_output(json_response):
    """V317.5: Cleans technical errors (SYMPY_PARSE_ERROR) and Hebrew from LaTeX."""
    if not isinstance(json_response, dict):
        return json_response
        
    if "steps" in json_response:
        for step in json_response["steps"]:
            block_math = step.get("block_math", "")
            if block_math:
                # Mission 2: זיהוי שגיאות של SymPy
                if "SYMPY_PARSE_ERROR" in block_math:
                    step["block_math"] = ""
                    step["content_mixed"] = step.get("content_mixed", "") + "\n(המשוואה הוסתרה עקב קושי בתצוגה)."
                
                # Mission 2: זיהוי אותיות בעברית בתוך ה-LaTeX
                elif re.search(r'[א-ת]', block_math):
                    # מעבירים את התוכן לשדה הטקסט ומוחקים את הבלוק המתמטי
                    clean_math = block_math.replace('\\text{', '').replace('}', '').replace('$', '')
                    step["content_mixed"] = step.get("content_mixed", "") + f"\n[{clean_math}]"
                    step["block_math"] = ""
                    
    # V280.0: Also check final_answer
    if "final_answer" in json_response and "SYMPY_PARSE_ERROR" in str(json_response["final_answer"]):
        json_response["final_answer"] = "התקבלה תשובה מורכבת (ראה שלבים מלאים)."
                
    return json_response

import asyncio

async def safe_llm_call(generator_func, timeout_seconds=45.0):
    try:
        # הגבלת זמן ריצה למניעת תקיעות שרת במקרה של רשת איטית
        raw_output = await asyncio.wait_for(generator_func(), timeout=timeout_seconds)

        # Handle if the function already returned parsed dict/list
        if isinstance(raw_output, (dict, list)):
            return raw_output

        if hasattr(raw_output, 'text'):
            raw_output = raw_output.text

        # V1.0: Use canonical safe_extract_json (logs RAW, fail-closed)
        result = safe_extract_json(raw_output, caller="SAFE_LLM_CALL")
        if isinstance(result, dict) and result.get("logic_error"):
            return build_standard_response(
                logic_error=True,
                error_type="STREAM_OR_CONTRACT_FAILURE",
                final_answer="התשובה לא התקבלה בצורה מלאה. נסו לסרוק שוב 📸",
                sections=[]
            )
        return result

    except Exception as e:
        logger.error(f"🚨 [FINAL_SHIELD] Exception caught: {str(e)}")
        # חזרה בטוחה למבנה שגיאה תקני ל-Flutter
        return build_standard_response(
            logic_error=True,
            error_type="STREAM_OR_CONTRACT_FAILURE",
            final_answer="התשובה לא התקבלה בצורה מלאה. נסו לסרוק שוב 📸",
            sections=[]
        )

def enforce_step_contract(proof_steps: list, llm_output: list):
    # 1. בדיקת כמות צעדים (חובה התאמה מלאה)
    if len(proof_steps) != len(llm_output):
        return False, "PEDAGOGICAL_STEP_MISMATCH"

    # 2. אימות זהות השלבים (Step ID Binding)
    for step_rule, step_llm in zip(proof_steps, llm_output):
        if step_rule.get("step_id") != step_llm.get("step_id"):
            return False, "STEP_ID_VIOLATION"

    return True, None

def build_standard_response(
    sections=None,
    final_answer="",
    teacher_summary="סיימנו את פתרון התרגיל.",
    graph_base64=None,
    audio_base64=None,
    logic_error=False,
    response_type="standard",
    strategy_card=None,
    visual_context=None,
    error_type=None
):
    """
    Standardize the output format for all responses.
    """
    # 🧹 Firewall 3: Sterilization
    if logic_error:
        # If there's an error, final_answer MUST just be the error message.
        # We strip away any sections to prevent hallucinated data from reaching the UI.
        sections = []

    response = {
        "final_answer": final_answer,
        "teacher_summary": teacher_summary,
        "sections": sections or [],
        "graph_base64": graph_base64,
        "audio_base64": audio_base64,
        "logic_error": logic_error,
        "type": response_type,
        "strategy_card": strategy_card,
        "visual_context": visual_context
    }
    
    if error_type:
        response["error_type"] = error_type
    logger.info(f"🏗️ [TRACE] FINAL JSON OUT: {response}") # Changed final_response to response
    return response

def build_structured_projection(llm_commentaries, sympy_steps):
    """ממזג הסברים מילוליים עם הלוח המתמטי ללא מגע יד אדם (V4.2.7)"""
    structured_response = []
    # Ensure we don't exceed the number of available commentaries
    for i, step in enumerate(sympy_steps):
        commentary = llm_commentaries[i] if i < len(llm_commentaries) else "נבצע את השלב הבא."
        
        # Determine artifact type (basic heuristic for now)
        artifact_type = "equation"
        if "table" in str(step.math_content).lower() or "|" in str(step.math_content):
            artifact_type = "table"

        structured_response.append({
            "step_id": i + 1,
            "step_number": i + 1, # Backward compatibility
            "explanation_text": commentary, # הדיבור של המורה
            "content_mixed": commentary,    # Backward compatibility
            "math_artifact": {
                "type": artifact_type,
                "latex": step.math_content,
                "table_data": "" # For future expansion
            },
            "block_math": step.math_content # Backward compatibility
        })
    return structured_response

logger = logging.getLogger(__name__)

def select_best_anchor(candidates: list[str]) -> str:
    """בחירת הגרסה הארוכה ביותר (מניח שלמות מתמטית)"""
    if not candidates: return ""
    return max(candidates, key=len)

def normalize_latex_for_sympy(expr: str) -> str:
    # המרת נגזרות לפורמט ש-SymPy מבין בצורה דינמית
    expr = re.sub(r"([a-zA-Z])'\((.*?)\)", r"Derivative(\1(\2), x)", expr)
    expr = expr.replace("\\", "")
    return expr

def verify_math_consistency(anchor_latex: str, final_result_latex: str):
    """
    V4.7 Hardened: Handles equations with '=' and never fails silently.
    """
    try:
        def clean_and_parse(latex_str):
            # ניקוי בסיסי והמרת נגזרות
            clean_str = normalize_latex_for_sympy(latex_str).replace('{', '(').replace('}', ')')
            # טיפול במשוואות: העברת אגפים
            parts = clean_str.split('=')
            if len(parts) == 2:
                return sp.sympify(parts[0]) - sp.sympify(parts[1])
            return sp.sympify(parts[0])
            
        a = clean_and_parse(anchor_latex)
        b = clean_and_parse(final_result_latex)
        
        is_identical = bool(sp.simplify(sp.expand(a - b)) == 0)
        return is_identical, (1.0 if is_identical else 0.6)
        
    except Exception as e:
        logger.error(f"❌ Verification crashed on input: {e}")
        # BREAKING FIX: חובה להחזיר False במקרה של קריסה!
        return False, 0.5 
from dotenv import load_dotenv
load_dotenv()  # Load .env BEFORE genai.configure()
import google.generativeai as genai
from smart_solver import SmartSolver
from gibberish_detector import validate_and_fix_solution
import gibberish_detector  # For fix_gibberish_smart
import visuals

# V231.12: Import smart architecture modules
from strategy_manager import StrategyManager
from pedagogical_builder import build_pedagogical_response, sanitize_math_text
import cost_tracker  # V231.26: Log usage
from audio_generator import generate_teacher_audio # V261.5: Teacher TTS

# V1.1: Math Safety Lock Modules
import math_intent_detector
import curriculum_engine
import strategy_policy_engine
from proof_graph import ProofGraph, ProofStep, validate_pedagogical_legality
from math_sanitizer import ProductionMathSanitizer
from pedagogical_builder import build_pedagogical_response, sanitize_math_text, merge_and_verify_explanations, LLMSchemaError

# V4.0: Curriculum Oracle
# curriculum_engine is already imported above, no need to re-import
# import curriculum_engine

# V231.14: Import problem understanding
import problem_understanding

try: from json_repair import repair_json
except: repair_json = lambda x: x

def safe_json_loads(raw_text: str) -> dict:
    """V1.0: Delegates to canonical safe_extract_json with LaTeX shield."""
    return safe_extract_json(raw_text, caller="ORCHESTRATOR_SAFE_LOADS")


from dataclasses import dataclass
from smart_solver import ActionContext

@dataclass
class PipelineContext:
    grade: str
    grade_num: int
    topic: str
    math_input: str
    confidence: float
    category: str = "GENERAL"
    original_text: str = ""        # V4.2.15: For intent-based gating
    sub_question_text: str = ""   # V7.3: Per-question routing context

class BuddyOrchestrator:
    def handle_fallback(self, context: PipelineContext):
        """V4.2.3: Safe fallback for solver failures."""
        print(f"🔄 [FALLBACK] Handling solver failure for grade {context.grade_num}")
        from types import SimpleNamespace
        return SimpleNamespace(success=False)
    def __init__(self):
        print("✅ 🟢 [BIT-LOG: המורה למתמטיקה V273.0] - SMART CLASSIFICATION + FAST PATH")

        genai.configure(api_key=os.environ.get("GOOGLE_API_KEY", ""))
        # V8.6.1: Force Strict JSON Output to prevent Markdown/Preamble leakage
        self.model = genai.GenerativeModel(
            model_name=GEMINI_MODEL,
            generation_config={"response_mime_type": "application/json"}
        )
        self.vision_model = genai.GenerativeModel(
            model_name=GEMINI_MODEL,
            generation_config={"response_mime_type": "application/json"}
        )
        self.smart_solver = SmartSolver()  # No model parameter needed
        
        # V231.12: Initialize strategy manager
        self.strategy_manager = StrategyManager(self.model)
        self._last_ocr_confidence = 1.0 # Default confidence (V3.1.2)
        print("🎯 [BIT-LOG] StrategyManager initialized")

    # ===================== V273.0: SMART QUESTION CLASSIFICATION =====================

    def _quick_classify(self, problem_text: str) -> ProcessingStrategy:
        """
        V5.8.0: Deterministic classification returning strict ProcessingStrategy
        """
        # ---------- MULTI PART / COMPLEX STRUCTURE ----------
        if re.search(r'[אבגדהו][\.\)\:\s]', problem_text) or re.search(r'סעיף\s*[אבגדהו]', problem_text):
            return ProcessingStrategy.STRICT_SYMBOLIC

        complex_keywords = [
            'חקור', 'חקירת', 'הוכח', 'הוכיח',
            'מקום גיאומטרי', 'הראה כי', 'הראי כי',
            'נתונה פונקציה', 'נתון משולש'
        ]
        if any(kw in problem_text for kw in complex_keywords):
            return ProcessingStrategy.STRICT_SYMBOLIC

        # ---------- PURE MATH EXPRESSION ----------
        math_only = re.sub(r'[\u0590-\u05FF\s]', '', problem_text)
        if len(problem_text) < 80 and len(math_only) > len(problem_text) * 0.4:
            return ProcessingStrategy.SIMPLE_ARITHMETIC

        # ---------- SHORT CALCULATION ----------
        simple_keywords = [
            'חשב', 'חשבי', 'פשט', 'פשטי',
            'מהו', 'מהי', 'כמה',
            'מצא', 'מצאי', 'פתור', 'פתרי'
        ]

        if len(problem_text) < 150 and any(kw in problem_text for kw in simple_keywords):
            if not any(kw in problem_text for kw in ['ולכן', 'לפיכך', 'הסבר', 'נמק']):
                return ProcessingStrategy.SIMPLE_ARITHMETIC

        # ---------- DEFAULT ----------
        return ProcessingStrategy.HEURISTIC_DEDUCTION

    async def _llm_classify(self, problem_text: str) -> dict:
        """
        V273.0: סיווג עם LLM - למקרים לא ברורים
        """
        prompt = f"""
סווג את השאלה המתמטית הבאה. החזר JSON בלבד.

שאלה:
"{problem_text[:500]}"

קטגוריות:
- SIMPLE = חישוב בודד, תשובה אחת, בלי סעיפים (דוגמה: "חשב 3+5", "פשט את הביטוי x²-4")
- MULTI_PART = יש סעיפים א,ב,ג או מספר שאלות נפרדות
- COMPLEX = חקירת פונקציה, הוכחה, גיאומטריה מורכבת, בעיה עם כמה שלבים

JSON:
{{
    "complexity": "SIMPLE" / "MULTI_PART" / "COMPLEX",
    "num_parts": מספר (1 אם פשוט, 2-6 אם יש סעיפים),
    "reason": "הסבר קצר מאוד"
}}
"""
        
        try:
            res = await asyncio.wait_for(
                self.model.generate_content_async(
                    prompt,
                    generation_config={"temperature": 0.0}
                ),
                timeout=8.0
            )
            cost_tracker.log_api_usage(res.usage_metadata, "CLASSIFY_QUESTION")
            
            match = re.search(r'\{.*\}', res.text, re.DOTALL)
            if match:
                data = safe_json_loads(match.group())
                data["confidence"] = "HIGH"
                data["source"] = "LLM"
                print(f"🏷️ [CLASSIFY] LLM result: {data}")
                return data
        except Exception as e:
            print(f"⚠️ [CLASSIFY] LLM failed: {e}")
        
        # Fallback - assume complex to be safe
        return {"complexity": "COMPLEX", "num_parts": 1, "confidence": "LOW", "source": "FALLBACK"}

    async def _classify_question(self, problem_text: str) -> dict:
        """
        V273.0: סיווג משולב - מהיר קודם, LLM רק אם צריך
        """
        print(f"🏷️ [CLASSIFY] Analyzing: {problem_text[:60]}...")
        
        # Step 1: Quick classification (no LLM)
        quick_result = self._quick_classify(problem_text)
        
        if quick_result["confidence"] == "HIGH":
            print(f"🏷️ [CLASSIFY] Quick result: {quick_result['complexity']} ({quick_result['source']})")
            return quick_result
        
        if quick_result["confidence"] == "MEDIUM":
            # Medium confidence - use it but log
            print(f"🏷️ [CLASSIFY] Medium confidence: {quick_result['complexity']} ({quick_result['source']})")
            return quick_result
        
        # Step 2: Need LLM classification
        print(f"🏷️ [CLASSIFY] Needs LLM classification...")
        return await self._llm_classify(problem_text)

    # ===================== V273.0: FAST PATH FOR SIMPLE QUESTIONS =====================

    async def _quick_solve(
        self,
        problem_text: str,
        grade: str,
        student_name: str,
        image_data: bytes = None,
        ambiguity_warning: bool = False
    ) -> dict:
        """
        V273.0: פתרון מהיר לשאלות פשוטות - קריאה אחת ל-LLM
        """
        print(f"⚡ [FAST PATH] Solving simple question...")
        
        prompt = f"""
אתה "המורה למתמטיקה" - מורה פרטית חמה ומקצועית.

פתור את השאלה הבאה עבור {student_name} (כיתה {grade}):

"{problem_text}"

הנחיות קריטיות:
1. פתור צעד אחר צעד בעזרת האובייקט `ctx` הקיים בלבד.
2. **אסור בשום פנים ואופן לכתוב הגדרות של מחלקות (class), פונקציות (def) או יבואים (import).**
3. השתמש ב- `ctx.explain("הסבר")` לכל שלב מילולי.
4. השתמש ב- `ctx.declare_equation("תיאור", ctx.Eq(x, 5))` למשוואות.
5. סיים ב- `ctx.finish("תשובה סופית ב-LaTeX", "סיכום מורה")`.
6. החזר אך ורק בלוק קוד פייתון נקי בתוך ```python.

דוגמה למבנה הקוד הרצוי:
```python
ctx.explain("ראשית נחבר את המספרים.")
ctx.declare_equation("פעולת החיבור", ctx.Eq(2 + 2, 4))
ctx.finish("$$ 4 $$", "מעולה! הגענו לתוצאה.")
```
"""

        try:
            import asyncio
            if image_data:
                # Use vision model
                res = await asyncio.wait_for(
                    asyncio.to_thread(
                        self.vision_model.generate_content,
                        [
                            prompt,
                            {"mime_type": "image/png", "data": image_data}
                        ]
                    ),
                    timeout=30.0
                )
            else:
                res = await asyncio.wait_for(
                    asyncio.to_thread(self.model.generate_content, prompt),
                    timeout=30.0
                )
            
            cost_tracker.log_api_usage(res.usage_metadata, "FAST_SOLVE")
            
            import math_engine
            python_match = re.search(r'```python(.*?)```', res.text, re.DOTALL)
            if python_match:
                python_code = python_match.group(1).strip()
                result = math_engine.run_llm_code(python_code)
                if result["success"]:
                    print(f"⚡ [FAST PATH] Python Math Engine execution successful!")
                    return await self._format_quick_response(result, student_name)
                else:
                    print(f"❌ [FAST PATH] Math Engine execution error: {result.get('error')}")
                    
        except Exception as e:
            print(f"❌ [FAST PATH] Error: {e}")
        
        # Fallback to full pipeline
        print(f"⚠️ [FAST PATH] Falling back to full pipeline...")
        return None

    async def _format_quick_response(self, data: dict, student_name: str) -> dict:
        """
        V273.0: המרת תשובה מהירה לפורמט הסטנדרטי של האפליקציה
        """
        steps = data.get("steps", [])
        final_answer = data.get("final_answer", "")
        teacher_summary = data.get("teacher_summary", "")
        
        # Build sections format
        formatted_steps = []
        for step in steps:
            formatted_steps.append({
                "step_number": step.get("step_number", len(formatted_steps) + 1),
                "title": f"שלב {step.get('step_number', len(formatted_steps) + 1)}",
                "content_mixed": step.get("content_mixed", ""),
                "block_math": step.get("block_math", "")
            })
        
        sections = [{
            "section_title": "פתרון",
            "steps": formatted_steps,
            "section_result": final_answer
        }]
        
        # Generate audio for summary
        audio_result = None
        if teacher_summary:
            teacher_summary = self._scrub_latex_from_text(teacher_summary)
            teacher_summary = self._sanitize_teacher_response(teacher_summary)
            
            try:
                audio_result = await generate_teacher_audio(teacher_summary)
            except Exception as e:
                print(f"🎙️ [FAST PATH] Audio failed: {e}")
        
        response = {
            "sections": sections,
            "final_answer": final_answer,
            "teacher_closing": f"כל הכבוד {student_name}! 🎉",
            "teacher_summary": teacher_summary
        }
        
        if audio_result:
            if audio_result.startswith("http"):
                response["audio_url"] = audio_result
            else:
                response["audio_base64"] = audio_result
        
        return response

    # ===================== V300: FEATURE-TOGGLED OCR =====================
    # OCR_STRIP_MODE=development → Stitch & Strip (single-pass, HD, structured)
    # OCR_STRIP_MODE=production  → Legacy Triple-Pass (safe, proven)

    def _flatten_ocr_payload(self, ocr_data) -> str:
        """
        V9.0.2: Ensures the OCR data is converted into a single, continuous text string 
        regardless of the API response format (JSON string, dict, or list).
        """
        if not ocr_data:
            return ""

        # 1. If it's a string, it might be a raw string OR a JSON string
        if isinstance(ocr_data, str):
            s = ocr_data.strip()
            if (s.startswith('[') and s.endswith(']')) or (s.startswith('{') and s.endswith('}')):
                try:
                    # Attempt to parse if it's a JSON structured string
                    parsed_data = json.loads(s)
                    ocr_data = parsed_data  # Pass to dict/list handling below
                except json.JSONDecodeError:
                    # It's just a regular raw string
                    return s
            else:
                return s

        # 2. If it's a List (This fixes the V9.0.1 bug!)
        if isinstance(ocr_data, list):
            # Join all elements with a newline/space, ignoring empty items
            # V9.0.2 FIX: Handle both list of strings AND list of dicts (Stitch & Strip)
            parts = []
            for item in ocr_data:
                if isinstance(item, dict):
                    # Handle structured block format: {"content": "...", "type": "..."}
                    content = item.get("content") or item.get("text") or ""
                    if content: parts.append(str(content).strip())
                elif item:
                    parts.append(str(item).strip())
            
            if parts:
                return " \n ".join(parts)
            return ""
        
        # 3. If it's a Dictionary
        elif isinstance(ocr_data, dict):
            # Look for a primary text key, otherwise convert the whole dict to string
            res = ocr_data.get("text") or ocr_data.get("content")
            if res:
                return str(res).strip()
            else:
                return " \n ".join([f"{k}: {v}" for k, v in ocr_data.items()])

        # 4. Ultimate Fallback for any other type
        return str(ocr_data).strip()

    async def transcribe_image(self, image_bytes: bytes) -> str:
        """
        V300: Feature-toggled OCR.
        Returns flat problem_text string. Also stores structured OCR list
        in self._last_ocr_structured for downstream consumers.
        """
        ocr_mode = os.environ.get("OCR_STRIP_MODE", "production").lower()
        print(f"📸 [BIT-LOG] OCR mode: {ocr_mode.upper()}")

        # ── V300 NEW: Stitch & Strip ──────────────────────────────────────
        if ocr_mode == "development":
            debug = True  # Always save strips while in DEV
            print("📸 🔵 [BIT-LOG] Starting OCR (Stitch & Strip V300)...")
            try:
                structured, confidence = await ocr_strip_engine.transcribe(
                    image_bytes=image_bytes,
                    vision_model=self.vision_model,
                    debug_mode=debug,
                )
                # Side-channel: store structured list for future use
                self._last_ocr_structured = structured
                self._last_ocr_confidence = confidence
                flat = ocr_strip_engine.flatten_to_text(structured)
                print(f"📸 🏆 [BIT-LOG] Stitch & Strip OCR complete — {len(structured)} items, Confidence: {confidence:.2f}")
                return flat
            except Exception as e:
                print(f"📸 ❌ [BIT-LOG] Stitch & Strip failed ({e}), falling back to triple-pass")

        # ── LEGACY: Triple-Pass ───────────────────────────────────────────
        print("📸 🔵 [BIT-LOG] Starting OCR (Triple Pass - V231.8)...")
        self._last_ocr_confidence = 0.85 # Default for legacy mode
        prompt = prompts.get_transcription_prompt()

        results = []

        # Pass 1: Original Image
        try:
            res = await asyncio.wait_for(
                self.vision_model.generate_content_async(
                    [prompt, {"mime_type": "image/jpeg", "data": image_bytes}],
                    generation_config={"temperature": 0.0}
                ),
                timeout=18.0
            )
            results.append(res.text.strip())
            cost_tracker.log_api_usage(res.usage_metadata, "OCR_PASS_1")
            print(f"📸 🟢 [BIT-LOG] OCR Pass 1 (Original): {len(results[0])} chars")
        except Exception as e:
            print(f"📸 🟡 [BIT-LOG] OCR Pass 1 failed: {e}")
            results.append("Error")

        # Pass 2: Enhanced Image
        try:
            enhanced_bytes = self._enhance_image_bytes(image_bytes)
            res = await asyncio.wait_for(
                self.vision_model.generate_content_async(
                    [prompt, {"mime_type": "image/jpeg", "data": enhanced_bytes}],
                    generation_config={"temperature": 0.0}
                ),
                timeout=18.0
            )
            results.append(res.text.strip())
            cost_tracker.log_api_usage(res.usage_metadata, "OCR_PASS_2")
            print(f"📸 🟢 [BIT-LOG] OCR Pass 2 (Enhanced): {len(results[1])} chars")
        except Exception as e:
            print(f"📸 🟡 [BIT-LOG] OCR Pass 2 failed: {e}")
            results.append("Error")

        # Pass 3: Retry with reinforced prompt for complex fractions
        try:
            retry_prompt = prompt + "\n\nCRITICAL: Pay special attention to complex fractions with powers in denominators, like (x^2-16)^2."
            res = await asyncio.wait_for(
                self.vision_model.generate_content_async(
                    [retry_prompt, {"mime_type": "image/jpeg", "data": image_bytes}],
                    generation_config={"temperature": 0.0}
                ),
                timeout=18.0
            )
            results.append(res.text.strip())
            cost_tracker.log_api_usage(res.usage_metadata, "OCR_PASS_3")
            print(f"📸 🟢 [BIT-LOG] OCR Pass 3 (Retry): {len(results[2])} chars")
        except Exception as e:
            print(f"📸 🟡 [BIT-LOG] OCR Pass 3 failed: {e}")
            results.append("Error")

        final_text = self._merge_ocr_results(results)
        # V9.0.2: Flatten payload (Robust handling of Union[str, list, dict])
        final_text = self._flatten_ocr_payload(final_text)
        
        # Build minimal structured list for consistency
        self._last_ocr_structured = [{"type": "text", "content": final_text}]
        print(f"📸 🏆 [BIT-LOG] OCR Final (Merged): {len(final_text)} chars")
        return final_text

    def _enhance_image_bytes(self, image_bytes: bytes) -> bytes:
        """V231.4: Attempt high-contrast enhancement. Falls back to original."""
        try:
            from PIL import Image, ImageEnhance
            import io
            img = Image.open(io.BytesIO(image_bytes))
            # High contrast + sharpness for better OCR
            img = ImageEnhance.Contrast(img).enhance(2.0)
            img = ImageEnhance.Sharpness(img).enhance(2.0)
            buf = io.BytesIO()
            img.save(buf, format='PNG')
            return buf.getvalue()
        except Exception as e:
            logging.debug(f"⚠️ [BIT-LOG] Image enhancement failed: {e}")
            # PIL not available or image issue — use original
            return image_bytes

    def _merge_ocr_results(self, results: list) -> str:
        """V231.4: Trust the most mathematically detailed OCR result."""
        def _math_complexity(text: str) -> int:
            """Score how much math content a string has."""
            if not text: return 0
            score = 0
            score += text.count('\\frac') * 3
            score += text.count('^') * 2
            score += text.count('\\sin') + text.count('\\cos') + text.count('\\tan')
            score += text.count('\\sqrt') * 2
            score += text.count('\\int') * 3
            score += text.count('סעיף') * 5  # sub-question markers are very valuable
            score += text.count('שאלה') * 5  # top-level headers are very valuable
            score += len(text) // 50  # length bonus
            return score

        valid = [r for r in results if r and r != "Error"]
        if not valid:
            return "Error"
        
        scored = [(r, _math_complexity(r)) for r in valid]
        scored.sort(key=lambda x: x[1], reverse=True)
        
        winner = scored[0]
        print(f"📸 🏆 [BIT-LOG] OCR Winner: score={winner[1]} (of {len(valid)} candidates)")
        return winner[0]

    async def _extract_key_data(self, problem_text: str, image_data: bytes = None) -> dict:
        """V231.14: Phase 1 - Extract specific values with validation and image support."""
        for attempt in range(1, 3):  # 2 attempts
            try:
                print(f"⚓ [BIT-LOG] Data Anchor Extraction (Attempt {attempt}). Image Data: {type(image_data)} {len(image_data) if image_data else 'None'}")
                prompt = prompts.get_data_extraction_prompt(problem_text)
                if not prompt:
                    prompt = f"Extract math data from this problem: {problem_text}"
                
                content = [prompt]
                if image_data and isinstance(image_data, bytes):
                    # V316.9: Use canonical dict format for maximum SDK compatibility
                    content.append({"mime_type": "image/png", "data": image_data})
                    print(f"📸 [BIT-LOG] Appended image part (size: {len(image_data)})")
                
                res = await asyncio.wait_for(
                    self.model.generate_content_async(
                        content,
                        generation_config={"temperature": 0.0}
                    ),
                    timeout=15.0  # 15s timeout per attempt
                )
                cost_tracker.log_api_usage(res.usage_metadata, "DATA_ANCHOR")
                match = re.search(r'\{.*\}', res.text, re.DOTALL)
                if match:
                    data = safe_json_loads(match.group())
                    
                    # V317.5: Robust JSON Handling - Smart Unification
                    data = unify_data_anchor(data)
                    if isinstance(data, dict):
                        print(f"⚓ [BIT-LOG] Unified Data Anchor: {json.dumps(data, ensure_ascii=False)[:100]}...")

                    # V261.X: Guard against parse-failure sentinel being treated as valid data
                    if data and isinstance(data, dict) and data.get('logic_error') and data.get('error_type') == 'PARSING_FAILURE':
                        print(f"⚠️ [BIT-LOG] Data Anchor JSON parse failed (Attempt {attempt}/2) — skipping sentinel.")
                        continue

                    # ✅ V231.12: Validate extracted data
                    if data and isinstance(data, dict):
                        # Validate function equations - must have '=' sign
                        if 'function_equations' in data:
                            valid_eqs = []
                            for eq in data['function_equations']:
                                # Must have '=' sign and be longer than just "f(x)"
                                if '=' in eq and len(eq) > 5:
                                    valid_eqs.append(eq)
                                else:
                                    print(f"⚠️ [BIT-LOG] Invalid equation (no '=' or too short): '{eq}'")
                            
                            data['function_equations'] = valid_eqs
                            
                            # V1.1: Partial Semantic Recovery logic
                            if not valid_eqs:
                                # Determine if recovery is possible (e.g., if there's a point or a simple equation)
                                has_points = len(data.get('points', [])) > 0
                                has_equations = len(data.get('equations', [])) > 0
                                
                                if has_points or has_equations:
                                    print(f"🔄 [V1.1] Partial Semantic Recovery: No function f(x) but found data. Continuing...")
                                    data['anchor_state'] = "PARTIAL_RECOVERABLE"
                                else:
                                    print(f"⚠️ [V1.1] Incomplete data: No function or equations. Recapture likely needed.")
                                    data['anchor_state'] = "INCOMPLETE"
                            else:
                                data['anchor_state'] = "FULL"

                            if not valid_eqs and 'function_equations' in data:
                                print(f"⚠️ [BIT-LOG] No valid function equations found in attempt {attempt}!")
                        
                        print(f"⚓ [BIT-LOG] Data Anchor (Attempt {attempt}): {json.dumps(data, ensure_ascii=False)}")
                        return data
            except asyncio.TimeoutError:
                print(f"⚠️ [BIT-LOG] Data Anchor timeout (Attempt {attempt}/2)")
            except Exception as e:
                print(f"⚠️ [BIT-LOG] Data Anchor error (Attempt {attempt}/2): {e}")
                import traceback
                traceback.print_exc()

        
        print("🚨 [BIT-LOG] CRITICAL: Data Anchor extraction failed completely!")
        return None

    async def _understand_problem(
        self,
        problem_text: str,
        data_anchor: dict
    ) -> dict:
        """
        V231.14: Understand problem structure BEFORE solving.
        
        Analyzes:
        - What type of problem is this?
        - How many sub-questions (א, ב, ג)?
        - What is each sub-question asking?
        - What are the dependencies?
        
        Returns understanding JSON.
        """
        print("📋 [BIT-LOG] Analyzing problem structure...")
        
        try:
            prompt = problem_understanding.get_problem_understanding_prompt(
                problem_text,
                data_anchor
            )
            
            response = await asyncio.wait_for(
                self.model.generate_content_async(prompt),
                timeout=15.0
            )
            
            understanding = problem_understanding.parse_understanding(response.text)
            
            # Validate
            if not problem_understanding.validate_understanding(understanding):
                print("⚠️ [BIT-LOG] Invalid understanding structure, using fallback")
                return self._create_fallback_understanding(problem_text, data_anchor)
                
            # V260.2: Enforce Hard Rules (Locus)
            understanding = problem_understanding.enforce_locus_rule(understanding, problem_text)
            
            print(f"📋 [UNDERSTANDING] Type: {understanding['problem_type']}")
            print(f"📋 [UNDERSTANDING] Sub-questions: {len(understanding['sub_questions'])}")
            
            for sq in understanding['sub_questions']:
                print(f"   - {sq['id']}: {sq['topic']}")
            
            return understanding
        
        except Exception as e:
            print(f"❌ [BIT-LOG] Understanding failed: {e}")
            return self._create_fallback_understanding(problem_text, data_anchor)
    
    def _create_fallback_understanding(self, problem_text: str, data_anchor: dict) -> dict:
        """Create simple understanding if analysis fails."""
        return {
            "problem_type": "GENERAL",
            "main_question": "Solve the problem",
            "sub_questions": [{
                "id": "main",
                "question": problem_text,
                "topic": "GENERAL",
                "requires": [],
                "expected_output": "solution"
            }],
            "solving_order": ["main"],
            "dependencies": {}
        }

    async def _generate_strategy_card(self, problem_text: str, data_anchor: dict) -> dict:
        """V260.0: Generate high-level strategy card."""
        print("🧭 [BIT-LOG] Generating Strategy Card...")
        try:
            prompt = prompts.get_strategy_card_prompt(problem_text, data_anchor)
            res = await asyncio.wait_for(
                self.model.generate_content_async(prompt),
                timeout=15.0
            )
            cost_tracker.log_api_usage(res.usage_metadata, "STRATEGY_CARD")
            match = re.search(r'\{.*\}', res.text, re.DOTALL)
            if match:
                data = safe_json_loads(match.group())
                print(f"🧭 [BIT-LOG] Strategy generated: {data.get('title')}")
                return self._inject_bidi_markers(data)
        except Exception as e:
            print(f"⚠️ [BIT-LOG] Strategy generation failed: {e}")
        return None

    async def _generate_visual_context(self, problem_text: str, category: str, image_data: bytes) -> dict:
        """V300.3: Generate visual description (Sketch). Supports text-only fallback."""
        print("GENERATE: [BIT-LOG] Generating Visual Context (Smart Trigger)...")
        try:
            prompt = prompts.get_visual_context_prompt(problem_text, category)
            
            # V300.3: Multi-modal Support (Handle both Image and Text-Only)
            if image_data:
                # Use vision model with image
                res = await asyncio.wait_for(
                    self.vision_model.generate_content_async([
                        prompt,
                        {"mime_type": "image/png", "data": image_data}
                    ]),
                    timeout=15.0
                )
            else:
                # V300.3: Text-only schematic generation
                print("INFO: [BIT-LOG] No image provided. Generating sketch from text description.")
                res = await asyncio.wait_for(
                    self.vision_model.generate_content_async(prompt),
                    timeout=15.0
                )
                
            cost_tracker.log_api_usage(res.usage_metadata, "VISUAL_CONTEXT")
            match = re.search(r'\{.*\}', res.text, re.DOTALL)
            if match:
                data = safe_json_loads(match.group())
                print(f"SUCCESS: [BIT-LOG] Visual context generated: {data.get('title')}")
                return self._inject_bidi_markers(data)
        except Exception as e:
            print(f"ERROR: [BIT-LOG] Visual context generation failed: {e}")
        return None

    def _verify_completeness(self, understanding: dict, solutions: list) -> list:
        """V260.0: Check if all sub-questions were solved."""
        required_ids = [sq['id'] for sq in understanding['sub_questions']]
        solved_ids = [sol['sub_question_id'] for sol in solutions]
        
        missing = [rid for rid in required_ids if rid not in solved_ids]
        
        if missing:
            print(f"🕵️‍♂️ [BIT-LOG] Completeness Check: MISSING {missing}")
        else:
            print("🕵️‍♂️ [BIT-LOG] Completeness Check: PASSED")
            
    async def _verify_pedagogical_depth(self, llm_response: dict) -> dict:
        """V260.1: The Strict Teacher - Verify step depth and clarity."""
        print("👩‍🏫 [BIT-LOG] Strict Teacher: Verifying depth...")
        
        # Extract steps content for analysis
        steps_text = "\n".join([f"Step {s.get('step')}: {s.get('content')} | {s.get('block_math')}" 
                              for s in llm_response.get("steps", [])])
        
        prompt = f"""
        YOU ARE A STRICT MATH TEACHER. Review this student's solution.
        
        Student's Solution Steps:
        {steps_text}
        
        CRITERIA ("Atomic Algebra"):
        1. did the student skip algebraic steps? (e.g. going from 2x=10 directly to x=5 is BAD. Must show /2).
        2. Is the Hebrew explanation clear and simple?
        3. Are there at least 3-4 steps for a complex problem/locus?
        4. IS 'block_math' POPULATED? Steps must show the math in LaTeX! (e.g., don't just say "we calculate", SHOW the calculation).
        
        OUTPUT JSON:
        {{
            "approved": true/false,
            "feedback": "Specific feedback if rejected (e.g., 'Skipped division step', 'Missing LaTeX in block_math')"
        }}
        """
        
        try:
            res = await asyncio.wait_for(
                self.model.generate_content_async(prompt),
                timeout=10.0
            )
            cost_tracker.log_api_usage(res.usage_metadata, "STRICT_TEACHER_VERIFY")
            match = re.search(r'\{.*\}', res.text, re.DOTALL)
            if match:
                data = safe_json_loads(match.group())
                print(f"👩‍🏫 [BIT-LOG] Verdict: {'✅ APPROVED' if data.get('approved') else '❌ REJECTED'} ({data.get('feedback')})")
                # V4.2.4: Final Output Sealing (Zero-Leakage Guard)
            import math_intent_detector
            grade_num = math_intent_detector._extract_grade_number(grade)
            data = seal_pedagogical_output(data, grade_num)
            
            return data
        except Exception as e:
            print(f"⚠️ [BIT-LOG] Verification failed: {e}")
        
        # Default to approved if check fails to avoid blocking
        return {"approved": True, "feedback": ""}

    # ===================== V272.0: SMART TEACHER SUMMARY =====================

    def _generate_deterministic_summary(
        self,
        problem_type: str,
        topics_text: str,
        answers_text: str,
        proof_graph = None
    ) -> str:
        """
        V4.2 (Behavioral Firewall): Deterministic Template Renderer.
        Now used ONLY as fallback if LLM summary fails.
        """
        print("🎙️ [V4.2] Generating DETERMINISTIC teacher summary (fallback)...")
        
        topic = topics_text if topics_text != "מתמטיקה כללית" else "התרגיל ששלחת"
        
        methods = []
        if proof_graph:
            for step in proof_graph.steps:
                if step.logic_description:
                    methods.append(step.logic_description)
        
        methods_text = " ו- ".join(list(set(methods))[:2]) if methods else "שיטות פתרון בסיסיות"

        template = (
            f"התרגיל עסק ב: {topic}. "
            f"השתמשנו בשיטות: {methods_text}. "
            f"הגענו לתשובה: {answers_text}. "
            f"הטריק לזכור, תמיד כדאי לעבוד שלב אחר שלב בצורה מסודרת. "
            f"כל הכבוד על ההתמדה!"
        )
        
        return template

    async def _generate_teacher_summary(
        self,
        problem_text: str,
        all_solutions: list,
        understanding: dict,
        proof_graph = None,
        student_name: str = "תלמיד",
        student_gender: str = "M"
    ) -> dict:
        """
        V285.1: LLM-powered pedagogical summary generator.
        Returns a dict with: topic_summary, key_concepts, formulas_to_remember, tts_speech.
        Falls back to deterministic template on error.
        """
        # Extract answers and topics for context
        final_answers = []
        topics_used = []
        for sol in all_solutions:
            if 'response' in sol and sol['response']:
                resp = sol['response']
                if isinstance(resp, dict):
                    if resp.get('final_answer'): final_answers.append(resp['final_answer'])
            if sol.get('topic'): topics_used.append(sol['topic'])
        
        answers_text = ", ".join(final_answers[:3]) if final_answers else "לא נמצאו תשובות"
        topics_text = ", ".join(set(topics_used)) if topics_used else "מתמטיקה כללית"
        problem_type = understanding.get('problem_type', 'GENERAL')

        # Try LLM-powered summary
        try:
            print("🎙️ [V285.1] Generating LLM pedagogical summary...")
            
            summary_prompt = prompts.get_teacher_summary_prompt(
                student_name=student_name,
                student_gender=student_gender
            )
            
            # Build context for the LLM
            context = f"""
            הבעיה: {problem_text[:300]}
            סוג בעיה: {problem_type}
            נושאים: {topics_text}
            תשובות סופיות: {answers_text}
            """
            
            response = await asyncio.wait_for(
                self.model.generate_content_async(summary_prompt + "\n\n" + context),
                timeout=30.0
            )
            
            raw_text = response.text.strip()
            print(f"🎙️ [V285.1] LLM Summary received ({len(raw_text)} chars)")
            logger.info(f"🎙️ [V285.1] RAW: {raw_text[:300]}")
            
            match = re.search(r'\{.*\}', raw_text, re.DOTALL)
            if not match:
                raise ValueError("No JSON found in LLM response for teacher summary")
            summary_data = safe_extract_json(match.group(), caller="TEACHER_SUMMARY")
            
            if summary_data.get("error_type") == "PARSING_FAILURE":
                raise ValueError("JSON parsing failed for teacher summary")
            
            # Build structured result
            result = {
                "topic_summary": summary_data.get("topic_summary", topics_text),
                "key_concepts": summary_data.get("key_concepts", []),
                "formulas_to_remember": summary_data.get("formulas_to_remember", []),
                "tts_speech": summary_data.get("tts_speech", ""),
            }
            
            # Build display text (for teacher_summary field in response)
            display_parts = []
            if result["topic_summary"]:
                display_parts.append(f"📚 נושא: {result['topic_summary']}")
            if result["key_concepts"]:
                concepts = "\n".join(f"• {c}" for c in result["key_concepts"])
                display_parts.append(f"💡 תובנות מפתח:\n{concepts}")
            if result["formulas_to_remember"]:
                formulas = "\n".join(f"$${f}$$" for f in result["formulas_to_remember"])
                display_parts.append(f"📐 נוסחאות לזכור:\n{formulas}")
            
            result["display_text"] = "\n\n".join(display_parts)
            
            print(f"🎙️ ✅ [V285.1] Summary ready: {result['topic_summary']}, {len(result['key_concepts'])} concepts")
            return result
            
        except Exception as e:
            print(f"🎙️ ⚠️ [V285.1] LLM summary failed: {e}. Using deterministic fallback.")
            fallback_text = self._generate_deterministic_summary(problem_type, topics_text, answers_text, proof_graph)
            return {
                "topic_summary": topics_text,
                "key_concepts": [],
                "formulas_to_remember": [],
                "tts_speech": fallback_text,
                "display_text": fallback_text
            }

    def _get_enhanced_fallback_summary(self, problem_type: str, answers: str) -> str:
        """V272.0: Fallback משופר לפי סוג הבעיה"""
        fallbacks = {
            "FUNCTION_ANALYSIS": "עברנו על חקירת פונקציה מלאה! מצאנו נקודות קיצון, תחומי עלייה וירידה, והבנו את התנהגות הפונקציה. הטריק הוא לזכור שנגזרת אפס מסמנת נקודות קיצון. כל הכבוד!",
            "GEOMETRY": "פתרנו שאלה בגיאומטריה אנליטית! השתמשנו בנוסחאות מרחק ומשוואות של צורות גיאומטריות. תמיד כדאי לצייר סקיצה לפני שמתחילים לחשב. מעולה!",
            "CALCULUS": "עבדנו עם נגזרות ואינטגרלים! זכרו את כללי הגזירה הבסיסיים ואת כלל השרשרת. התרגול הוא המפתח להצלחה בחדוא. כל הכבוד על ההתמדה!",
            "ALGEBRA": "פתרנו משוואות בצורה מסודרת! הדרך היא לבודד את הנעלם צעד אחר צעד. תמיד כדאי לבדוק את התשובה על ידי הצבה חזרה. יופי של עבודה!",
            "TRIGONOMETRY": "עבדנו עם טריגונומטריה! השתמשנו בזהויות טריגונומטריות ובקשרים בין הפונקציות. כדאי לזכור את הזהויות הבסיסיות בעל פה. מצוין!",
            "INVESTIGATION": "עברנו על חקירת פונקציה מלאה! מצאנו תחום, נקודות חיתוך עם הצירים, נגזרנו ומצאנו קיצון. הטריק הוא לעבוד בשיטתיות - שלב אחרי שלב. כל הכבוד!",
        }
        
        base = fallbacks.get(problem_type, "פתרנו את התרגיל צעד אחר צעד בצורה מסודרת. כל שלב הוביל לשלב הבא עד שהגענו לתשובה. כל הכבוד על ההתמדה!")
        
        if answers and answers != "לא נמצאו תשובות":
            base += f" הגענו לתשובה: {answers[:50]}."
        
        return base

    # ===================== V285.0: CHECK ME (HOMEWORK VERIFICATION) =====================

    async def _check_student_work(self, image_data_list: List[bytes], grade: str, student_name: str,
                                   student_gender: str = "M", question_id: str = "q_check"):
        """
        V285.0: Dedicated pipeline for the "Check Me" feature.
        Sends the raw image to Gemini Vision with the check-me prompt.
        The LLM acts as a homework checker, NOT a solver.
        """
        print(f"📝 [CHECK-ME] Starting homework verification for {student_name}")

        # Step 1: Emit WORKING state
        yield BuddyEvent(
            question_id=question_id,
            state=BuddyState.SECTION_WORKING,
            current_section_id="CHECK",
            payload={"status": "המורה בודקת את העבודה שלך... 📝"}
        )

        try:
            # V311.0: Data Slicing Guardrail
            # First, transcribe and extract the "Absolute Truth" of the problem from the FIRST image
            image_data = image_data_list[0]
            print("📝 [CHECK-ME] Step 1.5: Extracting Problem Data (Data Slicing from image_00)...")
            problem_text = await self.transcribe_image(image_data)
            data_anchor = await self._extract_key_data(problem_text, image_data=image_data)

            # Step 2: Build check-me prompt and send to Vision LLM
            check_prompt = prompts.get_check_me_prompt(
                grade=grade,
                student_name=student_name,
                student_gender=student_gender,
                data_anchor=data_anchor
            )

            # Prepare images for Gemini Vision
            vision_content = [check_prompt]
            for img_bytes in image_data_list:
                vision_content.append({"mime_type": "image/png", "data": img_bytes})

            print(f"📝 [CHECK-ME] Sending {len(image_data_list)} images + check prompt to Vision LLM...")

            response = await asyncio.wait_for(
                self.vision_model.generate_content_async(vision_content),
                timeout=60.0
            )

            raw_text = response.text.strip()
            print(f"📝 [CHECK-ME] LLM Response received ({len(raw_text)} chars)")
            logger.info(f"📝 [CHECK-ME] RAW LLM: {raw_text[:500]}")

            # Step 3: Parse JSON using the canonical safe_extract_json
            match = re.search(r'\{.*\}', raw_text, re.DOTALL)
            if not match:
                print("📝 ❌ [CHECK-ME] No JSON found in LLM response")
                yield BuddyEvent(
                    question_id=question_id,
                    state=BuddyState.ERROR,
                    payload=build_standard_response(
                        final_answer="מצטערת, לא הצלחתי לפענח את הניתוח. נסו שוב? 🔄",
                        logic_error=True,
                    )
                )
                return
            check_result = safe_extract_json(match.group(), caller="CHECK_ME")

            if check_result.get("error_type") == "PARSING_FAILURE":
                print("📝 ❌ [CHECK-ME] JSON parsing failed, returning error")
                yield BuddyEvent(
                    question_id=question_id,
                    state=BuddyState.ERROR,
                    payload=build_standard_response(
                        final_answer="מצטערת, לא הצלחתי לפענח את הניתוח. נסו שוב? 🔄",
                        logic_error=True,
                    )
                )
                return

            # Step 4: Extract fields from LLM response
            verdict = check_result.get("verdict", "has_errors")
            score = check_result.get("score", 0)
            mistakes = check_result.get("mistakes", [])
            encouragement = check_result.get("encouragement", "")
            problem_identified = check_result.get("problem_identified", "")
            methodology_ok = check_result.get("methodology_ok", True)
            methodology_note = check_result.get("methodology_note", "")
            visual_note = check_result.get("visual_note")
            correct_answer = check_result.get("correct_final_answer", "")
            feedback_steps = check_result.get("feedback_steps", [])

            print(f"📝 ✅ [CHECK-ME] Verdict: {verdict}, Steps: {len(feedback_steps)}")

            # ═══════════════════════════════════════════════════════════
            # Step 5: Emit STRATEGY_READY — Methodology card
            # ═══════════════════════════════════════════════════════════
            strategy_content = f"**תרגיל שזוהה:** ${problem_identified}$\n\n"
            if methodology_ok:
                strategy_content += f"✅ **השיטה נכונה:** {methodology_note}" if methodology_note else "✅ **השיטה שנבחרה נכונה!**"
            else:
                strategy_content += f"❌ **בעיה בשיטה:** {methodology_note}"

            strategy_card = {
                "section_title": "בדיקת שיטת פתרון 🔍",
                "bullets": [strategy_content]
            }

            # Generate TTS audio for encouragement
            audio_url = None
            if encouragement:
                try:
                    tts_text = self._scrub_latex_from_text(encouragement)
                    tts_text = self._sanitize_teacher_response(tts_text)
                    if tts_text:
                        print(f"🎙️ [CHECK-ME] Generating TTS for: {tts_text[:60]}...")
                        audio_url = await generate_teacher_audio(tts_text, output_path=None)
                except Exception as e:
                    print(f"🎙️ ❌ [CHECK-ME] TTS failed: {e}")

            strategy_payload = {"strategy_card": strategy_card, "teacher_summary": encouragement}
            if audio_url:
                if audio_url.startswith("http"):
                    strategy_payload["audio_url"] = audio_url
                else:
                    strategy_payload["audio_base64"] = audio_url

            yield BuddyEvent(
                question_id=question_id,
                state=BuddyState.STRATEGY_READY,
                payload=strategy_payload
            )

            # ═══════════════════════════════════════════════════════════
            # Step 6: Emit SECTION_READY per feedback step
            # ═══════════════════════════════════════════════════════════
            for fb_step in feedback_steps:
                step_id = fb_step.get("step_id", 0)
                student_wrote = fb_step.get("student_wrote", "")
                is_correct = fb_step.get("is_correct", True)
                error_desc = fb_step.get("error_description", "") or ""
                should_be = fb_step.get("should_be", "") or ""

                # Build rich content for this step
                if is_correct:
                    title = f"✅ שלב {step_id} — נכון!"
                    content = f"מה שכתבת: ${student_wrote}$" if student_wrote else "נכון!"
                    if error_desc:
                        content += f"\n\n{error_desc}"
                else:
                    title = f"❌ שלב {step_id} — יש טעות"
                    content = f"**מה שכתבת:** ${student_wrote}$\n\n" if student_wrote else ""
                    content += f"**הטעות:** {error_desc}"
                    if should_be:
                        content += f"\n\n**מה שצריך להיות:** ${should_be}$"

                section_data = {
                    "section_title": title,
                    "steps": [{
                        "step_id": step_id,
                        "content_mixed": content,
                        "block_math": ""
                    }]
                }

                yield BuddyEvent(
                    question_id=question_id,
                    state=BuddyState.SECTION_READY,
                    current_section_id=f"CHECK_{step_id}",
                    payload=section_data
                )

            # ═══════════════════════════════════════════════════════════
            # Step 7: Visual note (if any)
            # ═══════════════════════════════════════════════════════════
            if visual_note:
                yield BuddyEvent(
                    question_id=question_id,
                    state=BuddyState.SECTION_READY,
                    current_section_id="CHECK_VISUAL",
                    payload={
                        "section_title": "הערה על שרטוט 📐",
                        "steps": [{
                            "step_id": 99,
                            "content_mixed": visual_note,
                            "block_math": ""
                        }]
                    }
                )

            # ═══════════════════════════════════════════════════════════
            # Step 8: COMPLETE with final answer & Protocol Alignment
            # ═══════════════════════════════════════════════════════════
            from pedagogical_builder import sanitize_math_text
            
            # V311.0: LaTeX UI Safety
            safe_correct_answer = sanitize_math_text(correct_answer) if correct_answer else ""
            if safe_correct_answer and not safe_correct_answer.startswith("$$") and not safe_correct_answer.startswith("$"):
                safe_correct_answer = f"$${safe_correct_answer}$$"

            if verdict == "correct":
                final_answer_text = f"✅ כל הכבוד! הפתרון נכון! {encouragement}"
            elif verdict == "unreadable":
                final_answer_text = "📸 לא הצלחתי לקרוא את כתב היד. נסו לצלם שוב בצורה ברורה יותר."
            elif verdict == "methodology_error":
                final_answer_text = f"📐 יש בעיה בשיטת הפתרון. {methodology_note}"
            else:
                final_answer_text = f"📝 התשובה הנכונה: {safe_correct_answer}" if safe_correct_answer else encouragement

            yield BuddyEvent(
                question_id=question_id,
                state=BuddyState.COMPLETE,
                payload={
                    "final_answer": final_answer_text,
                    "verdict": verdict,
                    "is_correct": verdict == "correct",
                    "score": score,
                    "mistakes": mistakes,
                    "feedback": encouragement, # Protocol Alignment
                    "correct_answer": safe_correct_answer, # Protocol Alignment
                    "problem_identified": problem_identified
                }
            )

        except asyncio.TimeoutError:
            print("📝 ❌ [CHECK-ME] LLM timeout (60s)")
            yield BuddyEvent(
                question_id=question_id,
                state=BuddyState.ERROR,
                payload=build_standard_response(
                    final_answer="הבדיקה לקחה יותר מדי זמן. נסו שוב? ⏱️",
                    logic_error=True,
                )
            )

        except Exception as e:
            logger.exception("CHECK-ME ERROR")
            print(f"📝 ❌ [CHECK-ME] Error: {e}")
            yield BuddyEvent(
                question_id=question_id,
                state=BuddyState.ERROR,
                payload=build_standard_response(
                    final_answer="אופס! משהו השתבש בבדיקה. נסו שוב? 🔄",
                    logic_error=True,
                )
            )

    # ===================== SMART SOLVE =====================


    async def smart_solve(
        self,
        problem_text: str,
        data_anchor: dict,
        grade: str,
        category: str,
        student_name: str,
        student_gender: str = "M",
        image_data: bytes = None,
        image_data_list: list = None,
        ambiguity_warning: bool = False,
        processing_strategy: ProcessingStrategy = None,
        question_id: str = "q_default",
        **kwargs
    ):
        """
        
        Workflow:
        1. Understand problem structure
        2. Solve each sub-question (WITH IMAGE!)
        3. Build comprehensive response
        """
        print("🎯 [BIT-LOG] Using SMART SOLVE (V231.15)")
        uid = kwargs.get('uid')
        assessment_sent = False
        
        try:
            # Heartbeat: Strategy/Planning
            yield BuddyEvent(
                question_id=question_id,
                state=BuddyState.SECTION_WORKING,
                current_section_id="בניית אסטרטגיה",
                payload={"status": "המורה בונה אסטרטגיה לפתרון..."}
            )

            # Step 1: Understand problem structure
            understanding = await self._understand_problem(problem_text, data_anchor)
            
            # V8.9.6: SLICING FAILSAFE (Token Burner Prevention)
            # If this is a function investigation but the Data Anchor has NO functions,
            # we must NOT slice into 9 sub-questions. We force Single-Shot Fallback.
            is_investigation = understanding.get("problem_type") in ["FUNCTION_ANALYSIS", "INVESTIGATION", "CALCULUS"]
            has_anchor_functions = bool(data_anchor.get("function_equations"))
            
            if is_investigation and not has_anchor_functions:
                print("🛑 [V8.9.6] Slicing Failsafe Triggered: No functions in Anchor for Investigation. Forcing Single-Shot.")
                understanding = self._create_fallback_understanding(problem_text, data_anchor)
            
            # V260.0: Generate Pedagogical Context (Strategy & Visuals)
            strategy_card = await self._generate_strategy_card(problem_text, data_anchor)
            
            # V8.6.9: Wrap in sections list so solution_screen.dart can correctly parse it
            yield BuddyEvent(
                question_id=question_id,
                state=BuddyState.STRATEGY_READY,
                payload={"sections": [strategy_card]} if strategy_card else {}
            )

            # V300.3: Smart Visual Triggers (Product Alignment)
            # The goal: Trigger a sketch if explicitly requested or if it's a visual category without an original image.
            visual_categories = ["GEOMETRY", "GEOMETRY_ANALYTIC", "TRIGONOMETRY", "INVESTIGATION", "FUNCTIONS", "GEOMETRIC_LOCUS", "CALCULUS"]
            problem_type = understanding.get("problem_type", "").upper()
            
            # Explicit drawing keywords in text (Supports Dutch/Hebrew variations)
            explicit_drawing_keywords = ["שרטט", "סרטט", "סקיצה", "גרף", "המחשה", "צייר", "כיוון", "שרטוט", "איזה מן הגרפים", "איזה מהגרפים", "איזה גרף מתאר"]
            is_explicitly_requested = any(keyword in problem_text for keyword in explicit_drawing_keywords)
            
            # V300.3: Also check individual sub-questions for explicit requests
            if not is_explicitly_requested and "sub_questions" in understanding:
                for sub_q in understanding["sub_questions"]:
                    if any(kw in sub_q.get("question", "") for kw in explicit_drawing_keywords):
                        is_explicitly_requested = True
                        break
            
            # Helper sketch: Visual category + no original image provided (or manual request)
            has_original_image = bool(image_data)
            needs_helper_sketch = (problem_type in visual_categories) and (not has_original_image)
            
            visual_context = None
            if is_explicitly_requested or needs_helper_sketch:
                print(f"TRIGGER: [V300.3] Visual Context Triggered: Explicit={is_explicitly_requested}, Helper={needs_helper_sketch}")
                visual_context = await self._generate_visual_context(problem_text, problem_type or category, image_data)
            
            graph_svg = None
            if visual_context and visual_context.get("latex_input"):
                print("📉 [V290.0] Generating Chalkboard SVG...")
                try:
                    # V302.0: HOTFIX - SYMPY_PARSE_ERROR guard
                    # If the input is just the d1=d2 placeholder (from prompt instructions), skip it.
                    if "d_1" in visual_context["latex_input"] and "=" in visual_context["latex_input"]:
                         print("📉 [V302.0] Skipping placeholder d1=d2 equation to avoid SymPy parse error.")
                         graph_svg = None
                    else:
                        graph_svg = visuals.generate_plot(
                            visual_context["latex_input"], 
                            problem_text, 
                            visual_context.get("geometric_entities")
                        )
                except Exception as e:
                    print(f"⚠️ [V302.0] SYMPY_PARSE_ERROR: Graph generation failed: {e}")
                    graph_svg = None
            
            # Step 2: Solve each sub-question
            all_solutions = []
            context = {}  # Store results for dependencies
            total_tokens = 0  # V8.5: Token Firewall Tracking
            
            for sub_q in understanding['sub_questions']:
                print(f"🔄 [SOLVING] Sub-question {sub_q['id']}: {sub_q['topic']}")
                
                # V8.5: Emit SECTION_WORKING
                yield BuddyEvent(
                    question_id=question_id,
                    state=BuddyState.SECTION_WORKING,
                    current_section_id=sub_q['id'],
                    payload={"question": sub_q['question']}
                )
                
                # Solve this sub-question WITH IMAGE
                # V231.22: FIX - Use the detected problem_type from understanding as the category!
                # This fixes the bug where FUNCTION_ANALYSIS was treated as GEOMETRY because we passed the stale 'category' arg.
                effective_category = understanding.get('problem_type', category)
                
                # V4.2 PRE-CONSTRAINT LOGIC (Iron Law #1)
                # 1. Fetch Curriculum Rules FIRST
                grade_num = math_intent_detector._extract_grade_number(grade)
                curriculum_rules = curriculum_engine.get_allowed_math_operators(grade, level=kwargs.get('level', '4'))
                
                # 2. Detect Intent & Lock Strategy (Iron Law #2 - V4.2.8)
                intent = math_intent_detector.detect_intent(sub_q['question'], grade_num)
                strategy_vector = strategy_policy_engine.get_allowed_strategies(intent)
                intent_contract = math_intent_detector.get_intent_contract(intent, grade_num)
                
                # Merge intent_contract with strategy_policy_engine (policy takes precedence)
                if strategy_vector.get("forbidden"):
                    intent_contract["forbidden_strategies"] = list(set(intent_contract.get("forbidden_strategies", []) + strategy_vector["forbidden"]))
                
                # 3. SmartSolver Execution (WITH IMAGE! - Strategy Enforcement)
                ocr_confidence = self._last_ocr_confidence
                
                # 1. יצירת אובייקט ה-Context
                eqs = data_anchor.get("function_equations", [])
                joined_eqs = " , ".join(eqs) if eqs else sub_q['question']
                
                # V6 Polish: P0 Canonicalize Math OCR
                cleaned_math = MathCanonicalizer.preprocess_ocr_string(joined_eqs)
                
                context_obj = PipelineContext(
                    grade=grade,
                    grade_num=math_intent_detector._extract_grade_number(grade),
                    topic="GENERAL",
                    math_input=cleaned_math,
                    confidence=ocr_confidence,
                    category=effective_category,
                    original_text=problem_text,
                    sub_question_text=sub_q.get('question', '')  # V7.3: scope isolation
                )

                # V6.1 Phase 1: Complexity Estimation & Prompt Specialization
                complexity_score = CurriculumClassifier.estimate_complexity(cleaned_math)
                prompt_specialization = CurriculumClassifier.get_prompt_specialization(grade, effective_category)

                # ==================== V7.2 PRE-FLIGHT CHECKS ====================

                # Ticket 1: Build AST metadata (Planner gets this; never gets raw math)
                ast_metadata = build_ast_metadata(cleaned_math, effective_category)
                ast_registry = ast_metadata.pop("ast_registry")  # Server-side only
                visual_context = _abstract_visual_context(data_anchor or {})
                if visual_context:
                    ast_metadata["visual_context"] = visual_context

                # Ticket 1: CRS Pre-Flight Gate (BEFORE any LLM call)
                preflight_risk = CognitiveRiskEngine.calculate_risk_score(
                    cleaned_math, [], retry_count=0, validation_errors=0
                )
                preflight_crs = preflight_risk["risk_score"]
                print(f"🔬 [PREFLIGHT] Pre-LLM CRS: {preflight_crs} (threshold: 0.7)")

                if preflight_crs > 0.7:
                    print(f"🛑 [PREFLIGHT] CRS {preflight_crs} exceeds threshold. Skipping Planner. Entering Hint Mode.")
                    telemetry.emit_crs_block(preflight_crs, data_anchor.get("problem_id", "unknown") if data_anchor else "unknown")
                    telemetry.emit_runtime_outcome("crs_block")
                    yield BuddyEvent(
                        question_id=question_id,
                        state=BuddyState.COMPLETE,
                        payload=build_standard_response(
                            final_answer="אפשר לקבל רמז? 🎓",
                            teacher_summary="זהו תרגיל מאתגר! בוא נפרק אותו לחלקים קטנים יותר או נפשט את הביטוי המרכזי.",
                            logic_error=False
                        )
                    )
                    return

                # ==================== END PRE-FLIGHT ====================

                # ── Guard Clause ─────────────────────────────────────────────
                if not hasattr(context_obj, "math_input"):
                    raise ValueError("PipelineContext contract violation")

                import datetime
                problem_id_tag = data_anchor.get("problem_id", "adhoc_request") if data_anchor else "adhoc_request"

                # ══════════════════════════════════════════════════════════════
                # THE HYBRID ENGINE (LLM NAVIGATES, POLYGRAPH CONTROLS)
                # ══════════════════════════════════════════════════════════════
                print("🧠 [V7.3] Triggering LLM Navigation Mode...")
                
                # ══════════════════════════════════════════════════════════════
                # V8.5: THE MICRO-AGENT SECTION LOOP (RETRY + ESCAPE HATCH)
                # ══════════════════════════════════════════════════════════════
                
                attempts = 0
                max_attempts = 2
                solved_data = None
                last_error_context = ""
                is_degraded = False
                degraded_reason = None
                
                while attempts < max_attempts:
                    attempts += 1
                    
                    # V8.5: PRE-FLIGHT TOKEN FIREWALL
                    if total_tokens > GLOBAL_TOKEN_LIMIT:
                        print(f"🛑 [FIREWALL] Token limit reached ({total_tokens} > {GLOBAL_TOKEN_LIMIT}). Aborting solve.")
                        break

                    print(f"🧠 [V8.5] Solving sub-q {sub_q['id']} (Attempt {attempts}/{max_attempts})")
                    
                    # 1. ה-LLM פותר ומנווט את התשובה בהתבסס על ההקשר (והשגיאות הקודמות אם יש)
                    solve_prompt = sub_q['question']
                    if last_error_context:
                        solve_prompt = f"FIX ERROR: {last_error_context}\n\nORIGINAL QUESTION: {solve_prompt}"

                    # V8.6.9: Reset context on retry to reduce token pressure
                    # V310.0: PHYSICAL DATA ISOLATION (The Pink Elephant Fix)
                    # Instead of just prompting, we physically remove values belonging to other sub-questions.
                    
                    all_sub_q_values = []
                    if "sub_questions" in understanding:
                        for other_q in understanding["sub_questions"]:
                            if other_q.get("specific_values"):
                                all_sub_q_values.extend(other_q["specific_values"])
                    
                    # Values that are unique to specific sections (not truly global)
                    restricted_pool = set(all_sub_q_values)
                    
                    local_anchor = {**data_anchor}
                    global_values = local_anchor.get("specific_values", [])
                    
                    # Truly global = anchor values minus ANY value identified as section-specific
                    truly_global_values = [v for v in global_values if v not in restricted_pool]
                    
                    # Section-specific = the values identified for THIS sub-question
                    my_specific_values = sub_q.get("specific_values", [])
                    
                    print(f"📦 [V310.0] Data Slicing for {sub_q['id']}: Global={len(truly_global_values)}, Local={len(my_specific_values)}")
                    local_anchor["specific_values"] = list(set(truly_global_values + my_specific_values))

                    effective_context = {**local_anchor, **context} if attempts == 1 else {**local_anchor}

                    result = await safe_llm_call(
                        lambda: self.strategy_manager.solve_with_strategy(
                            problem_text=solve_prompt,
                            data_anchor=effective_context,
                            grade=grade,
                            image_data=image_data,
                            image_data_list=image_data_list,
                            parent_category=effective_category,
                            student_name=student_name,
                            student_gender=student_gender
                        ),
                        timeout_seconds=30.0
                    )
                    
                    if isinstance(result, dict) and result.get("logic_error"):
                        # Fatal LLM/JSON error, don't retry same error
                        last_error_context = "LLM_MALFORMED_JSON_OR_TIMEOUT"
                        continue

                    llm_resp = result.get("llm_response", {})
                    
                    # V8.5: Token Accumulation
                    usage = llm_resp.get("usage_metadata")
                    if usage:
                        # V8.5.1: usage is now a dict (serialized in strategy_manager)
                        t_count = usage.get('total_token_count', 0) if isinstance(usage, dict) else getattr(usage, 'total_token_count', 0)
                        total_tokens += t_count
                        print(f"💰 [FIREWALL] Cumulative tokens: {total_tokens}")

                    llm_steps = llm_resp if isinstance(llm_resp, list) else llm_resp.get("steps", [])

                    # 2. השרת שולט: הפעלת ה-Polygraph על הצעדים של ה-LLM 
                    struct_ok, struct_reason = await MathPolygraph.validate_step_sequence(llm_steps, topic=sub_q.get('topic', 'GENERAL'))
                    
                    poly_ok = struct_ok
                    poly_reason = struct_reason
                    
                    if struct_ok:
                        # V1.3: Also verify algebraic consistency (e.g. A + B = C)
                        alg_ok, alg_reason = await MathPolygraph.verify_algebraic_consistency(llm_steps, topic=sub_q.get('topic', 'GENERAL'))
                        if not alg_ok:
                            poly_ok = False
                            poly_reason = alg_reason

                    if poly_ok:
                        print(f"✅ [V8.5] Sub-q {sub_q['id']} Validated Successfully (Structure & Algebra)!")
                        solved_data = llm_resp
                        break
                    else:
                        print(f"🛑 [V8.5] Validation failed on attempt {attempts}/{max_attempts}: {poly_reason}")
                        last_error_context = poly_reason
                        
                        # HOTFIX: לא זורקים תשובה טובה לפח! שומרים את הפתרון של ה-LLM
                        solved_data = llm_resp 
                        
                        # מעקף: אם השגיאה היא רק בעיית קריאה של סימנים (אי-שוויונים/חיצים), סומכים על ה-LLM ויוצאים
                        if "SYMPY_PARSE_ERROR" in str(poly_reason):
                            # V280.0 + V310.0: Smart Retry & Soft Fail with JSON Security check
                            # 1. Logic: Only allow bypass if it's NOT the first attempt OR it's a "Soft Fail" case.
                            # 2. Pedagogical: "אין פתרון" is allowed. "לא ייתכן" remains removed.
                            forbidden_words = ["סתירה בנתונים", "לא הגיוני", "שגיאה בחישוב שלי", "אני מזהה סתירה", "סתירה", "המצאה", "טעות שלי", "מתנצל"]
                            import json
                            response_text = json.dumps(llm_resp, ensure_ascii=False)
                            
                            has_forbidden = any(word in response_text for word in forbidden_words)
                            
                            if has_forbidden:
                                print(f"🛑 [ROBUSTNESS] Forbidden word detected in SYMPY_PARSE_ERROR response. Not Trusting LLM.")
                                is_degraded = True
                                degraded_reason = "polygraph_fail_forbidden_words"
                                # Continue to next attempt
                            else:
                                # V317.8 Soft Fail: Treat SymPy Parse Error as a warning immediately to avoid retries on valid LaTeX
                                print(f"🛡️ [SOFT FAIL] SymPy Parse Error detected (Attempt {attempts}). No forbidden words found. Trusting LLM output for sub-q {sub_q['id']}.")
                                is_degraded = True
                                degraded_reason = "sympy_soft_fail"
                                break # Exit the attempt loop
                        elif attempts == max_attempts:
                            print(f"⚠️ [HOTFIX] Max attempts reached. Forcing LLM response despite Polygraph failure.")
                            is_degraded = True
                            degraded_reason = "max_attempts_polygraph_fail"

                # 3. Escape Hatch Injection (if failed twice)
                if not solved_data:
                    is_degraded = True
                    degraded_reason = "polygraph_fail"
                    print(f"🛡️ [V8.5] ESCAPE HATCH TRIGGERED for sub-q {sub_q['id']}")
                    
                    # V8.5.1: If we have LLM steps but they failed validation, use them anyway 
                    # as a degraded fallback instead of the hardcoded d_1 = d_2.
                    if llm_steps:
                        solved_data = {
                            "steps": llm_steps,
                            "final_answer": llm_resp.get("final_answer") if isinstance(llm_resp, dict) else "בדוק את הצעדים"
                        }
                        # Add a disclaimer to the first step if possible
                        if solved_data["steps"] and isinstance(solved_data["steps"][0], dict):
                            orig_text = solved_data["steps"][0].get("explanation_text", "")
                            solved_data["steps"][0]["explanation_text"] = f"הצגנו את הצעדים העיקריים כדי שתוכל לעקוב אחרי הדרך:\n\n{orig_text}"
                    else:
                        solved_data = {
                            "steps": [
                                {
                                    "step_id": 1,
                                    "explanation_text": "החישוב בסעיף זה הפך למורכב מאוד. הצגנו את העיקרון המנחה:",
                                    "math_latex": "d_1 = d_2"
                                }
                            ],
                            "final_answer": "המשך לפי השלבים"
                        }

                # 4. Packaging & Yielding
                # V8.6.7 FIX / V317.5: Only pass the final answer text forward to prevent massive JSON injection in future prompts
                context[f"result_{sub_q['id']}"] = solved_data.get("final_answer", "No valid answer extracted") if isinstance(solved_data, dict) else "הושלם"
                
                # Check for critical failure (No valid answer extracted)
                # If a section fails completely, we break the loop to avoid cascading failures
                if context[f"result_{sub_q['id']}"] == "No valid answer extracted":
                    print(f"🛑 [ABORT] Section {sub_q['id']} failed completely. Aborting orchestration.")
                    yield BuddyEvent(
                        question_id=question_id,
                        state=BuddyState.ERROR,
                        payload={"error": "Section failure", "message": "חלה שגיאה בעיבוד חלק מהשאלה. עוצרים כדי למנוע טעויות."}
                    )
                    return
                
                # AI Assessment Telemetry Extraction
                if not assessment_sent and isinstance(solved_data, dict) and "assessment" in solved_data:
                    assessment_data = solved_data["assessment"]
                    if uid and assessment_data:
                        try:
                            from analytics import analytics_manager
                            loop = asyncio.get_event_loop()
                            loop.create_task(asyncio.to_thread(analytics_manager.update_weekly_analytics, uid, assessment_data))
                            print(f"📊 [ANALYTICS] Triggered background telemetry for {uid}")
                            assessment_sent = True
                        except Exception as e:
                            print(f"❌ [ANALYTICS] Failed to trigger background task: {e}")
                
                response = build_pedagogical_response(
                    effective_category, 
                    solved_data, 
                    data_anchor, 
                    custom_title=f"סעיף {sub_q['id']}",
                    processing_strategy=ProcessingStrategy.HEURISTIC_DEDUCTION
                )
                
                # V8.5: Inject degradation flags into payload
                if is_degraded:
                    response["is_degraded"] = True
                    response["degraded_reason"] = degraded_reason
                
                # V290.0: Inject graph into section payload if available (Early Projection)
                if graph_svg:
                    response["graph_svg"] = graph_svg
                    response["graph_base64"] = "" # Legacy fallback

                # Emit SECTION_READY
                yield BuddyEvent(
                    question_id=question_id,
                    state=BuddyState.SECTION_READY,
                    current_section_id=sub_q['id'],
                    payload=response
                )
                
                all_solutions.append({
                    "sub_question_id": sub_q['id'],
                    "question": sub_q['question'],
                    "topic": sub_q['topic'],
                    "response": response
                })
            
            # Step 3: Build multi-part response (V4.2.16 Hotfix)
            final_response = self._build_multi_part_response(
                all_solutions,
                strategy_card=strategy_card,
                visual_context=visual_context
            )
            
            # V260.2: FIX MISSING GRAPH (Forensic Finding 2026-02-14)
            if visual_context:
                print("📉 [BIT-LOG] Generating graph from visual context...")
                try:
                    # Note: visuals.generate_plot is synchronous in visuals.py V231.10
                    latex_for_plot = visual_context.get("latex_input", "")
                    if latex_for_plot:
                        # V261.2: Pass Explicit Geometric Entities
                        geometric_entities = visual_context.get("geometric_entities")
                        graph_svg = visuals.generate_plot(latex_for_plot, problem_text, geometric_entities)
                        if graph_svg:
                            final_response["graph_svg"] = graph_svg
                            final_response["graph_base64"] = "" # Legacy fallback
                            print(f"📉 [BIT-LOG] SVG Graph generated! ({len(graph_svg)} chars)")
                        else:
                            print("📉 [BIT-LOG] Graph generation returned None/Empty")
                    else:
                        print("📉 [BIT-LOG] No latex_input in visual context. Skipping graph.")
                except Exception as e:
                    print(f"⚠️ [BIT-LOG] Graph generation failed: {e}")

            # ===================== V285.1: SMART TEACHER SUMMARY + TTS =====================
            print("🎙️ [BIT-LOG] Starting V285.1 Smart Teacher Summary...")
            
            # Generate pedagogical summary using LLM
            summary_result = await self._generate_teacher_summary(
                problem_text,
                all_solutions,
                understanding,
                proof_graph=None,
                student_name=student_name,
                student_gender=student_gender
            )
            
            # Extract TTS text (clean, no LaTeX, no emojis)
            tts_text = summary_result.get("tts_speech", "")
            if tts_text:
                tts_text = self._scrub_latex_from_text(tts_text)
                tts_text = self._sanitize_teacher_response(tts_text)
            
            # Generate Audio from TTS text
            if tts_text:
                print(f"🎙️ [BIT-LOG] Generating Audio for: {tts_text[:60]}...")
                try:
                    audio_result = await generate_teacher_audio(tts_text, output_path=None)
                    
                    if audio_result:
                        if audio_result.startswith("http"):
                            final_response["audio_url"] = audio_result
                            print(f"🎙️ ☁️ [BIT-LOG] Audio uploaded: {audio_result}")
                        else:
                            final_response["audio_base64"] = audio_result
                            print(f"🎙️ 💿 [BIT-LOG] Audio as Base64")
                    else:
                        print("🎙️ ⚠️ [BIT-LOG] Audio generation returned None")
                except Exception as e:
                    print(f"🎙️ ❌ [BIT-LOG] Audio CRASHED: {e}")
            
            # Store the structured summary matching Flutter's _buildSummaryCard format
            # Flutter expects: {audio_pitch, key_concepts, formulas}
            topic_summary = summary_result.get("topic_summary", "")
            key_concepts = summary_result.get("key_concepts", [])
            formulas = summary_result.get("formulas_to_remember", [])
            
            # Build the audio_pitch text: topic + TTS speech
            audio_pitch_parts = []
            if topic_summary:
                audio_pitch_parts.append(f"נושא השיעור: {topic_summary}")
            if tts_text:
                audio_pitch_parts.append(tts_text)
            audio_pitch = " ".join(audio_pitch_parts) if audio_pitch_parts else ""
            
            structured_summary = {
                "audio_pitch": audio_pitch,
                "key_concepts": key_concepts,
                "formulas": formulas,
            }
            
            if audio_pitch or key_concepts or formulas:
                final_response["teacher_summary"] = structured_summary
                print(f"🎙️ ✅ [V285.1] Structured summary set: topic={topic_summary}, concepts={len(key_concepts)}, formulas={len(formulas)}")
            else:
                print("🎙️ [BIT-LOG] No summary data generated")
            # ===================== END V285.1 TTS BLOCK =====================

            # V5.10.0: Save to History if Premium
            tier = kwargs.get('tier', 'student_basic')
            # Variable uid is already defined at start of smart_solve
            print(f"🔍 [DEBUG HISTORY] UID: {uid}, Received Tier: '{tier}', kwargs keys: {list(kwargs.keys())}")
            is_premium = tier in ["premium", "admin", "admin_unlimited"]
            if is_premium and uid:
                try:
                    # V315.0: Explicit scheduling with loop check
                    loop = asyncio.get_event_loop()
                    loop.create_task(self._save_exercise_history(uid, problem_text, all_solutions))
                    print(f"📚 [HISTORY] History saving scheduled for {uid}")
                except Exception as e:
                    print(f"❌ [HISTORY] Failed to schedule history saving: {e}")

            # V277.0: FIXED - Yield final solution as a COMPLETE event instead of using return (which is ignored by async generators)
            yield BuddyEvent(
                question_id=question_id,
                state=BuddyState.COMPLETE,
                payload=final_response
            )
            return
        
        except Exception as e:
            print(f"❌ [BIT-LOG] Smart solve error: {e}")
            import traceback
            traceback.print_exc()
            
            # Logic Error Enforced Fallback
            yield BuddyEvent(
                question_id=question_id,
                state=BuddyState.ERROR,
                payload=build_standard_response(
                    final_answer="הסבר זה הוסר עקב חריגה מהחוזה.",
                    teacher_summary="חלה שגיאה טכנית במערכת המתמטית. אנא נסה לצלם שוב.",
                    logic_error=True
                )
            )
            return
    
    def _build_multi_part_response(self, solutions, strategy_card=None, visual_context=None):
        """V6 Polish: Aggregator Refactoring - Isolate sections with statuses, remove string concatenation."""
        sections = []
        all_answers = []
        summaries = []
        
        print(f"📊 [BIT-LOG: AGGREGATOR] Merging {len(solutions)} sub-questions")
        
        # 1. Strategy & Visual (V260.0 compat)
        if strategy_card:
            strategy_steps = []
            if "steps" in strategy_card and isinstance(strategy_card["steps"], list):
                for i, s_text in enumerate(strategy_card["steps"]):
                    clean_s = sanitize_math_text(s_text)
                    strategy_steps.append({"step_id": i+1, "explanation_text": clean_s, "content_mixed": clean_s, "math_artifact": {"type": "equation", "latex": ""}})
            else:
                clean_s = sanitize_math_text(strategy_card.get("content", ""))
                strategy_steps.append({"step_id": 0, "explanation_text": clean_s, "content_mixed": clean_s, "math_artifact": {"type": "equation", "latex": ""}})
            
            sections.append({
                "section_title": strategy_card.get("title", "איך ניגשים לזה? 🧭"),
                "status": "SUCCESS",
                "steps": strategy_steps
            })
            
        if visual_context:
            clean_v = sanitize_math_text(visual_context.get("description", ""))
            sections.append({
                "section_title": visual_context.get("title", "המחשה חזותית ✏️"),
                "status": "SUCCESS",
                "steps": [{"step_id": 0, "explanation_text": clean_v, "content_mixed": clean_v, "math_artifact": {"type": "equation", "latex": visual_context.get("latex_input", "")}}]
            })

        for sol in solutions:
            res = sol.get("response", {})
            sub_q_status = "SUCCESS"  # Default status
            
            if isinstance(res, dict):
                # V6 Polish: Determine status based on flags in response block
                if res.get("logic_error"):
                    # Check if it was a solver failure (no rule found) or parse error. Default to FAILED_RULE if logic error flagged.
                    sub_q_status = "FAILED_RULE"
                
                if "sections" in res and res["sections"]: 
                    # Flatten sub-question sections and append status
                    for section in res["sections"]:
                         base_title = section.get('section_title', '')
                         if "סעיף" not in base_title:
                             section["section_title"] = f"סעיף {sol.get('sub_question_id', '?')} - {base_title}"
                         section["status"] = sub_q_status
                         sections.append(section)
                else:
                    # If this sub-question failed entirely and has no steps to show
                    sections.append({
                        "section_title": f"סעיף {sol.get('sub_question_id', '?')} - הופסק או נכשל",
                        "status": sub_q_status,
                        "steps": []
                    })
                
                if res.get("teacher_summary") and not res.get("logic_error"): 
                    summaries.append(res["teacher_summary"])
        
        response = build_standard_response(
            sections=sections,
            final_answer="הפתרון לכלל הסעיפים מפורט למטה.",
            teacher_summary=summaries[0] if summaries else "סיימנו את הניתוח.",
            graph_base64=None,
            audio_base64=None,
            logic_error=False,
            response_type="standard",
            strategy_card=strategy_card,
            visual_context=visual_context
        )
        
        print(f"✅ [BIT-LOG: AGGREGATOR] Response built. Sections: {len(sections)}")
        return response
    


    # ===================== CORE SOLVE =====================

    async def solve_problem(self, problem_text, grade, student_name, **kwargs):
        """
        V277.0: Main solve method with BINARY DATA SUPPORT and TUTOR SESSION support.
        """
        uid = kwargs.get('uid')
        session_id = kwargs.get('session_id')

        # ===================== V318.0: TUTOR SESSION MODE =====================
        if session_id and uid:
            print(f"🎓 [TUTOR-MODE] Activating Session-Based Dialogue for session: {session_id}")
            # We yield from the tutor handler
            event = await self._handle_tutor_session(problem_text, student_name, uid, session_id, **kwargs)
            yield event
            return
        # ===================== END TUTOR SESSION MODE =====================
        uid = kwargs.get('uid')
        image_data_list = kwargs.get('image_data_list')
        image_data = kwargs.get('image_data') or kwargs.get('image_bytes')
        
        if image_data and not image_data_list:
            image_data_list = [image_data]
        elif image_data_list and not image_data:
            image_data = image_data_list[0]
            
        # V316.0: CRITICAL - Ensure image_data is explicitly passed in kwargs for the rest of parameters
        kwargs['image_data'] = image_data
        kwargs['image_data_list'] = image_data_list

        question_id = kwargs.get('question_id', f"q_{int(time.time())}")
        start_time = asyncio.get_event_loop().time()
        # GLOBAL_TIMEOUT_SEC = 240 # 4 minutes usually
        
        # Immediate Heartbeat for UX
        yield BuddyEvent(
            question_id=question_id,
            state=BuddyState.SECTION_WORKING,
            current_section_id="ניתוח תמונה",
            payload={"status": "המורה קוראת את השאלה..."}
        )

        # ===================== V285.0: CHECK ME ROUTING =====================
        mode = kwargs.get('mode', 'solve')
        if mode == 'check' and image_data:
            print(f"📝 [V285.0] Mode=CHECK detected. Routing to _check_student_work()...")
            student_gender = kwargs.get('student_gender', 'M')
            async for event in self._check_student_work(
                image_data_list=image_data_list,
                grade=grade,
                student_name=student_name,
                student_gender=student_gender,
                question_id=question_id
            ):
                yield event
            return
        # ===================== END CHECK ME ROUTING =====================
        
        if image_data_list:
            print(f"🔵 [BIT-LOG] Starting OCR Pipeline on {len(image_data_list)} images...")
            ocr_results = []
            for i, img in enumerate(image_data_list):
                print(f"📸 [BIT-LOG] Transcribing image {i}...")
                text = await self.transcribe_image(img)
                if text:
                    ocr_results.append(text)
            
            problem_text = "\n\n".join(ocr_results)
            image_data = image_data_list[0] # Use first image for main processing logic/anchors
            
        logger.info(f"🔎 [TRACE] RAW OCR TEXT: {problem_text}")

        student_gender = kwargs.get('student_gender', 'M')
        
        print(f"🧠 [BIT-LOG] Orchestrating for {student_name} (V277.0 - BINARY DATA SUPPORT)")
        
        # 🧱 Firewall 1: OCR Early Exit
        import re
        ocr_clean = problem_text.strip() if problem_text else ""
        
        # חייב להכיל לפחות אות אנגלית אחת, מספר, או סימן מתמטי
        has_math_anchor = bool(re.search(r'[0-9xyzXYZ=+\-\(\)]', ocr_clean))
        # V5.7.5: Short Math Bypass (Happy Flow for simple equations)
        # Often simple equations like $2+2=?$ yield low OCR confidence but are valid.
        is_short_math = has_math_anchor and len(ocr_clean) < 15 and len(ocr_clean) > 2
        
        # מצב 3: Hard Stop (Low Confidence)
        if self._last_ocr_confidence < CONFIDENCE_THRESHOLD_MEDIUM and not is_short_math:
            print(f"🔴 [V3.1.2] Hard Stop: Confidence {self._last_ocr_confidence:.2f} < {CONFIDENCE_THRESHOLD_MEDIUM}")
            yield BuddyEvent(
                question_id=question_id,
                state=BuddyState.ERROR,
                payload=build_standard_response(
                    final_answer="המערכת לא הצליחה לזהות את כל הנתונים. אנא צלם שוב בצורה ברורה יותר.",
                    logic_error=True,
                    # response_type="error",
                    # error_type="RECAPTURE_REQUIRED"
                )
            )
            return
            
        # מצב 2: Soft Recovery (Medium Confidence)
        ambiguity_warning = self._last_ocr_confidence < CONFIDENCE_THRESHOLD_HIGH
        if ambiguity_warning:
            print(f"🟡 [V3.1.2] Soft Recovery: Confidence {self._last_ocr_confidence:.2f} < {CONFIDENCE_THRESHOLD_HIGH}")
        
        # ===================== V5.8.0: STRATEGY RESOLUTION =====================
        strategy = self._quick_classify(problem_text)
        print(f"🏷️ [BIT-LOG] Strategy: {strategy.value}")

        # V8.5: Initialize Token Tracking
        from cost_tracker import CostTracker
        tokens = CostTracker()

        # ===================== SIMPLE FAST PATH =====================
        if strategy == ProcessingStrategy.SIMPLE_ARITHMETIC:
            print("⚡ [BIT-LOG] Using SIMPLE_ARITHMETIC Fast Path")
            yield BuddyEvent(question_id=question_id, state=BuddyState.SECTION_WORKING, current_section_id="A", payload={"status": "Solving locally..."})

            fast_result = await self._quick_solve(
                problem_text=problem_text, grade=grade, student_name=student_name,
                image_data=image_data, ambiguity_warning=ambiguity_warning
            )

            if fast_result:
                fast_result, _, _ = validate_and_fix_solution(fast_result)
                # Quick Polygraph check
                _poly_steps = collect_all_steps(fast_result)
                _poly_ok, _ = await MathPolygraph.validate_step_sequence(_poly_steps, topic=str(strategy.value))
                
                if _poly_ok:
                    yield BuddyEvent(
                        question_id=question_id,
                        state=BuddyState.SECTION_READY,
                        current_section_id="A",
                        payload=build_standard_response(**fast_result)
                    )
                    yield BuddyEvent(question_id=question_id, state=BuddyState.COMPLETE, payload={})
                    return

        # ===================== FULL STREAMING PIPELINE =====================
        print(f"🎯 [BIT-LOG] Using Streaming Pipeline Strategy: {strategy.value}")

        # V316.0: image_data is already hydrated at the top of solve_problem.
        # This block is now redundant but kept for safety if someone moves things.
        if image_data is None and image_data_list:
            image_data = image_data_list[0]
            print("📸 [BIT-LOG] Using first image from list for Data Anchor phase. (Redundant Check)")

        data_anchor = await self._extract_key_data(problem_text, image_data=image_data) or {}
        
        # V8.9.2: SEPARATE VALIDATOR PASS (Single Source of Truth)
        if image_data and data_anchor:
            print("🛡️ [V8.9.2] Starting Data Anchor Validation Pass...")
            data_anchor = await self._validate_anchor(data_anchor, image_data, problem_text)

        # V1.0: GEOMETRIC SANITY CHECK (Ground Truth Injection)
        # Runs BEFORE the LLM to verify algebraic consistency of the extracted data.
        # Injects 'verified_facts' and 'geometry_warnings' into the anchor.
        try:
            _geo_anchor, _geo_prompt_block = run_geometric_sanity(data_anchor)
            if _geo_anchor.verified_facts or _geo_anchor.warnings:
                data_anchor["_verified_facts"] = _geo_anchor.verified_facts
                data_anchor["_geometry_warnings"] = _geo_anchor.warnings
                data_anchor["_geo_prompt_block"] = _geo_prompt_block
                print(f"🔬 [GEO-SANITY] Injected {len(_geo_anchor.verified_facts)} fact(s), "
                      f"{len(_geo_anchor.warnings)} warning(s) into data_anchor.")
        except Exception as _geo_err:
            logging.warning(f"[GEO-SANITY] Non-fatal error: {_geo_err}")
        
        # Iterate through the streaming smart_solve
        # V5.10.2: Remove keys already passed explicitly to avoid TypeError collision
        for _key in ['student_gender', 'image_data', 'image_data_list', 'image_bytes',
                      'mode', 'question_id', 'user_note']:
            kwargs.pop(_key, None)
        try:
            async for event in self.smart_solve(
                problem_text=problem_text,
                data_anchor=data_anchor,
                grade=grade,
                category="investigation", # simplified
                student_name=student_name,
                student_gender=student_gender,
                processing_strategy=strategy,
                image_data=image_data,
                image_data_list=image_data_list,
                ambiguity_warning=ambiguity_warning,
                question_id=question_id,
                **kwargs
            ):
                # 🛡️ Global Guard 1: Token Controller
                # Check current tokens from global tracking if possible, or per call
                # (For now we rely on the fact that each LLM call logs to CostTracker)
                
                # 🛡️ Global Guard 2: Timeout
                elapsed = asyncio.get_event_loop().time() - start_time
                if elapsed > GLOBAL_TIMEOUT_SEC:
                    print(f"🛑 [V8.5] Global Timeout ({elapsed:.1f}s) reached. Cutting stream.")
                    yield BuddyEvent(
                        question_id=question_id,
                        state=BuddyState.COMPLETE,
                        payload={"warning": "Solving timed out. Partial results shown."}
                    )
                    return

                yield event

            # Final Event - No longer needed as smart_solve now yields the final COMPLETE event
            # yield BuddyEvent(question_id=question_id, state=BuddyState.COMPLETE, payload={})
            pass

        except Exception as e:
            logger.exception("STREAMING ERROR")
            yield BuddyEvent(
                question_id=question_id,
                state=BuddyState.ERROR,
                payload={"error": str(e), "message": "אופס! משהו השתבש בעיבוד השאלה."}
            )
    

    # V260.5: General Q&A ("Ask the Teacher")
    async def ask_question(self, context_data, question, student_name):
        """
        Answers a specific student question based on the problem context.
        context_data: The full solution JSON (or relevant parts).
        question: The student's question.
        """
        # Serialize context for prompt
        context_str = json.dumps(context_data, ensure_ascii=False)
        
        prompt = f"""
        תפקיד: מורה פרטית למתמטיקה (המורה למתמטיקה V260.5).
        התלמיד {student_name} שואל שאלה על הפתרון שקיבל.
        
        הקשר (הנתונים והפתרון שכבר נוצר):
        {context_str}
        
        השאלה של התלמיד:
        "{question}"
        
        הנחיות:
        1. ענה לעניין, בצורה קצרה וממוקדת.
        2. השתמש בטון מעודד וחם (כמו "הנסיך/הנסיכה").
        3. אם השאלה קשורה לנוסחה, כתוב אותה ב-LaTeX תקני (בתוך $...$).
        4. אם התלמיד לא הבין משהו, הסבר לו במילים פשוטות יותר.
        5. קריטי: אתה בממשק צ'אט נטול קבצים מצורפים! לעולם אל תגיד "ההסבר המלא בפתרון המצורף" או לשלוח את התלמיד למקור חישוב חיצוני. עליך לספק את **כל החישוב וההסבר המתמטי המלא והמפורט** ישירות בתוך הטקסט של התשובה שלך (שדה "answer").

        חובה: החזר JSON בלבד!
        {{
            "answer": "התשובה המלאה, כולל כל צעדי החישוב וההסבר המתמטי...",
            "follow_up_suggestion": "שאלה נוספת שהתלמיד יכול לשאול (אופציונלי)"
        }}
        """
        
        try:
            res = await asyncio.wait_for(
                self.model.generate_content_async(prompt),
                timeout=30.0
            )
            
            # Extract JSON
            match = re.search(r'\{.*\}', res.text, re.DOTALL)
            if not match:
                raise ValueError("No JSON found in LLM response for ask_question")
            data = safe_extract_json(match.group(), "ask_question")
            
            # Sanitize math in answer
            if "answer" in data:
                from pedagogical_builder import sanitize_math_text
                data["answer"] = sanitize_math_text(str(data["answer"]))
                    
                return build_standard_response(
                    teacher_summary=data.get("answer", ""),
                    sections=[{
                        "section_title": "תשובה לשאלה",
                        "steps": [{
                            "step_id": 1,
                            "explanation_text": data.get("answer", ""),
                            "math_artifact": {"type": "equation", "latex": "", "table_data": ""}
                        }]
                    }],
                    final_answer=data.get("follow_up_suggestion", "")
                )
            
            error_msg = "לא הצלחתי להבין את השאלה, נסה לנסח שוב? 🤔"
            return build_standard_response(
                teacher_summary=error_msg,
                logic_error=True
            )
            
        except Exception as e:
            print(f"🔥 [BIT-LOG] Ask Question error: {e}")
            return build_standard_response(
                teacher_summary="אופס, נתקלתי בבעיה קטנה. בוא ננסה שוב! 😅",
                logic_error=True
            )
            
    async def explain_specific_step(self, context, step_text, student_name):
        """V231.4: Step explanation with LaTeX shield."""
        prompt = f"""
        תפקיד: מורה פרטית למתמטיקה (המורה למתמטיקה V231.4).
        התלמיד {student_name} ביקש הסבר נוסף על צעד ספציפי.
        
        ההקשר: {context}
        הצעד שצריך הסבר: {step_text}
        
        הסבר את הצעד מאפס, כאילו התלמיד רואה את הנושא בפעם הראשונה.
        השתמש בפורמט: "הסבר בעברית :: $נוסחה$"
        חובה: כל פקודת LaTeX חייבת להתחיל ב-\\ (למשל: \\frac, \\cdot, \\sqrt).
        
        החזר JSON: {{ "explanation": "...", "example": "..." }}
        """
        try:
            res = await asyncio.wait_for(
                self.model.generate_content_async(prompt),
                timeout=30.0
            )
            match = re.search(r'\{.*\}', res.text, re.DOTALL)
            if not match:
                raise ValueError("No JSON found in LLM response for explain_step")
            data = safe_extract_json(match.group(), "explain_step")
            data = self._scrub_placeholders(data)
            data = self._enhance_latex_v2(data)
            if data and ("explanation" in data or "example" in data):
                return data
            return {"explanation": "לא הצלחתי לייצר הסבר.", "example": ""}
        except Exception as e:
            print(f"🔥 [BIT-LOG] Explain error: {e}")
            return {"explanation": "המורה למתמטיקה נתקל בקושי. נסה שוב.", "example": ""}

    async def _handle_tutor_session(self, problem_text, student_name, uid, session_id, **kwargs):
        """
        V318.0: Logic for Phase A - Contextual Memory & Tutor Dialogue.
        """
        question_id = kwargs.get('question_id', f"tutor_{int(time.time())}")
        image_data_list = kwargs.get('image_data_list') or []
        grade = kwargs.get('grade', "כיתה י'")

        # 1. Fetch History (Last 10 messages)
        history_docs = firebase_manager.get_chat_history(uid, session_id, limit=10)
        
        # 2. Map history to Gemini format
        history_contents = []
        for doc in history_docs:
            role = "user" if doc.get('role') == 'user' else "model"
            history_contents.append({
                "role": role,
                "parts": [doc.get('content', '')]
            })

        # 3. System Instruction
        system_instruction = f"""את מורה פרטית למתמטיקה. המטרה שלך היא לנהל דיאלוג למידה עם התלמיד {student_name} (כיתה {grade}).
חוקי השיחה:
1. אם זו תחילת שיחה (היסטוריה ריקה): אל תפתרי כלום. שאלי את התלמיד: 'היי {student_name}! מה אנחנו לומדים היום בכיתה?' וחכי לתשובה.
2. ברגע שזוהה נושא: שמרי אותו בזיכרון השיחה והתייחס אליו בהמשך.
3. בבקשת 'בדקי לי' (כשיש תמונות של פתרון): נתחי את התמונה מול השאלה. אם יש טעות, צייני את השורה ואת סוג הטעות (סימנים, חוקי חזקות וכו'). אל תתקני מיד, תני רמז.
4. השתמש בטון מעודד, חם ובגובה העיניים.
5. כל פלט חייב להיות בפורמט JSON תקין לפי הסכימה המוגדרת.
"""

        # 4. Construct Current Input
        current_input_parts = []
        if problem_text:
            current_input_parts.append(problem_text)
        
        for img_bytes in image_data_list:
            current_input_parts.append({
                "mime_type": "image/jpeg",
                "data": img_bytes
            })

        if not current_input_parts:
            current_input_parts.append("(התלמיד הצטרף לשיחה)")

        # 5. Gemini Call with Structured Output
        try:
            # We use a temporary chat session to include history
            chat = self.model.start_chat(history=history_contents)
            
            # V318: Enforce JSON mode and schema
            generation_config = genai.GenerationConfig(
                response_mime_type="application/json",
                response_schema=TutorResponseSchema,
                temperature=0.7
            )

            # Prepend system instruction as a message if model doesn't support separate system_instruction in start_chat
            # Actually, Gemini 2.0 Flash supports system_instruction in the model constructor or in GenerateContent.
            # Here we'll append it to the prompt for maximum compatibility with existing self.model.
            full_prompt = f"[SYSTEM_INSTRUCTION]\n{system_instruction}\n\n[USER_INPUT]\n"
            
            res = await self.model.generate_content_async(
                contents=history_contents + [{"role": "user", "parts": [full_prompt] + current_input_parts}],
                generation_config=generation_config
            )

            # 6. Parse & Save
            data = json.loads(res.text)
            student_message = data.get("student_message", "")
            analytics = data.get("internal_analytics", {})

            # Determine intent for metadata
            intent = analytics.get("intent", "CHAT")
            mastery_score = analytics.get("mastery_score", 0)
            topic = analytics.get("topic", "General")
            
            # V318.0: TRIGGER CHALLENGE GENERATOR if Mastery > 70
            if mastery_score > 70 and intent in ["SOLVE", "CHECK"]:
                logger.info(f"🏆 [TUTOR-MODE] High Mastery detected ({mastery_score}). Triggering Challenge Generator.")
                from exercise_generator import exercise_generator
                # Fire and forget (Background Task)
                asyncio.create_task(exercise_generator.generate_challenge(problem_text or "(מעבד תמונה)", topic, uid))
                
                # Append the challenge offer to the tutor's message
                offer_text = "\n\nכל הכבוד! הכנתי לך תרגיל אתגר דומה כדי לוודא שזה יושב טוב. רוצה לנסות? 💪"
                student_message += offer_text

            # Save User Message (if there was text/images)
            if problem_text or image_data_list:
                firebase_manager.save_chat_message(
                    uid, session_id, "user", problem_text or "(תמונה)", 
                    metadata={"intent": intent}
                )

            # Save Assistant Message
            firebase_manager.save_chat_message(
                uid, session_id, "assistant", student_message, 
                metadata=analytics
            )

            # 7. Map to BuddyEvent for UI
            return BuddyEvent(
                question_id=question_id,
                state=BuddyState.COMPLETE,
                payload=build_standard_response(
                    teacher_summary=student_message,
                    final_answer=""
                )
            )

        except Exception as e:
            logger.error(f"❌ [TUTOR-SESSION] Error: {e}")
            return BuddyEvent(
                question_id=question_id,
                state=BuddyState.ERROR,
                payload={"error": str(e), "message": "המורה נתקלה בבעיה בזיכרון של השיחה."}
            )

    # ===================== PIPELINE METHODS =====================

    def _get_v231_pedagogic_block(self):
        """V231.4: Pedagogic instructions appended to every prompt."""
        return r"""
        \n🛑 משימה: המורה למתמטיקה (סטנדרט פרימיום V231.4) 🛑
        1. הסבר מאפס: כל שלב מלווה בהסבר מילולי עשיר.
        2. בטיחות לטך: חובה להשתמש ב- \\frac{}{} ולא ב- frac.
        3. ניקיון: אל תשאיר פלייסהולדרים כמו $formula$ או $math$ בטקסט.
        4. כל פקודת LaTeX חייבת להתחיל ב-\\. למשל: \\frac, \\cdot, \\sqrt, \\pi.
        5. Hebrew or GEOMETRIC NOTATION (e.g., \angle, ^\circ, \triangle) inside $$ is FORBIDDEN. It breaks the app. Use inline math in content_mixed for dimensions/angles.
        6. NEVER write \\left or \\right. Use ( ) instead.
        7. Each math expression MUST be on its OWN LINE.
        8. NEVER use \\ne (conflicts with newline). USE \\neq instead.
        9. STRATEGY CARD: The first section "איך ניגשים לזה?" must contain ONLY hints and thinking strategy — NO final answers!
        10. OCR SACRED: You MUST solve the EXACT function from the image. If OCR says x^2/(x^2-4), solve THAT. Do NOT invent a different function.
        11. MATH SEPARATION (CRITICAL): block_math is for PURE ALGEBRA ONLY. NO \angle, \triangle, ^\circ, \text{}, \quad, or 'and' allowed in block_math. Use inline math `$...$` in content_mixed for dimensions, labels, or explanatory math.
        12. PEDAGOGICAL HIGHLIGHTING: When performing a substitution, showing a change in sign, taking a derivative, or highlighting a key transition in a calculation, use `\color{color_name}{...}` (e.g., `\color{red}{x^2}`, `\color{blue}{+4}`) to visually highlight the element that changed or needs the student's focus. Ensure you only wrap valid Math in the color tag.
        """

    def _scrub_placeholders(self, data):
        """✅ V231.0: מסיר פלייסהולדרים ריקים ש-Gemini משאיר בטעות"""
        if isinstance(data, str):
            result = data
            result = re.sub(r'\$\s*formula\d*\s*\$', '', result)
            result = re.sub(r'\$\s*math\d*\s*\$', '', result)
            result = result.replace('[math]', '')
            result = result.replace('[formula]', '')
            result = re.sub(r'::\s*$', '', result, flags=re.MULTILINE)
            return result.strip()
        if isinstance(data, dict):
            return {k: self._scrub_placeholders(v) for k, v in data.items()}
        if isinstance(data, list):
            return [self._scrub_placeholders(i) for i in data]
        return data

    def _enhance_latex_v2(self, data):
        """✅ V231.2: מתקן לוכסנים רק בתוך $...$ — לא נוגע בעברית!"""
        if isinstance(data, str):
            def _fix_content(content):
                content = re.sub(
                    r'(?<!\\)(frac|sqrt|cdot|times|neq|pm|alpha|beta|gamma|Delta|pi|infty|leq|geq|quad)',
                    r'\\\1', content
                )
                content = re.sub(r'(?<!\\)(sin|cos|tan|log|ln)\b', r'\\\1', content)
                return content

            # V8.8: Upgrade standalone inline math ($...$) taking up a full line to block math ($$...$$)
            res = re.sub(r'(?m)^\s*\$(.+?)\$\s*$', r'$$\1$$', data)

            res = re.sub(r'\$\$(.+?)\$\$', lambda m: f'$${_fix_content(m.group(1))}$$', res, flags=re.DOTALL)
            res = re.sub(r'(?<!\$)\$(?!\$)(.+?)(?<!\$)\$(?!\$)', lambda m: f'${_fix_content(m.group(1))}$', res)
            return res
        if isinstance(data, dict):
            return {k: self._enhance_latex_v2(v) for k, v in data.items()}
        if isinstance(data, list):
            return [self._enhance_latex_v2(i) for i in data]
        return data

    def _scrub_strategy_hebrew(self, data):
        """✅ V231.5: Scrubs $ \\ { from Hebrew side of :: separator in strategy cards."""
        if isinstance(data, dict):
            sections = data.get('sections', [])
            if sections and isinstance(sections, list) and len(sections) > 0:
                first_sec = sections[0]
                title = first_sec.get('section_title', '')
                if 'ניגשים' in title or 'אסטרטגיה' in title or '🧭' in title:
                    # This is the strategy card — scrub Hebrew side
                    steps = first_sec.get('steps', [])
                    for step in steps:
                        if 'content_mixed' in step:
                            val = step['content_mixed']
                            if '::' in val and '$' in val:
                                parts = val.split('::', 1)
                                hebrew = re.sub(r'\$[^$]*\$', '', parts[0]).strip()
                                step['content_mixed'] = f"{hebrew} :: {parts[1].strip()}"
                            else:
                                # Pure Hebrew strategy line — strip any accidental LaTeX
                                val = re.sub(r'\\[a-zA-Z]+', '', val)
                                val = val.replace('$', '').replace('{', '').replace('}', '')
                                step['content_mixed'] = re.sub(r'\s+', ' ', val).strip()
            return data
        return data
    
    def _inject_bidi_markers(self, data):
        """✅ V231.11: Smart BiDi with RLM markers (Right-to-Left Mark)."""
        # RLM (\u200F) marks direction without reversing text
        # Unlike RLE (\u202B) which was causing full text reversal
        
        if isinstance(data, str):
            # Check if string contains Hebrew
            has_hebrew = bool(re.search(r'[\u0590-\u05FF]', data))
            
            if has_hebrew:
                return '\u200F' + data
            
            return data
        
        if isinstance(data, dict):
            return {k: self._inject_bidi_markers(v) for k, v in data.items()}
        if isinstance(data, list):
            return [self._inject_bidi_markers(i) for i in data]
        return data
    
    def _purge_double_dollars(self, data):
        """✅ V275.3: Remove orphan double-dollars, fix quadruple dollars, and unwrap Hebrew-in-math."""
        if isinstance(data, str):
            # Fix quadruple dollars $$$$ -> $$ globally before processing
            data = re.sub(r'\${3,}', '$$', data)
            
            # Remove empty math blocks: $$ $$ or $$$$
            data = re.sub(r'\$\$\s*\$\$', '', data)
            
            # V275.4: Unwrap Hebrew paragraphs from $$...$$ blocks (mirror text fix)
            def _unwrap_hebrew(match):
                content = match.group(1).strip()
                hebrew_chars = len(re.findall(r'[\u0590-\u05FF]', content))
                total_chars = len(content.replace(' ', ''))
                if total_chars == 0:
                    return ''
                # Heuristic 1: >25% Hebrew = text paragraph (lowered from 40%)
                if hebrew_chars / total_chars > 0.25 and hebrew_chars > 8:
                    return content
                # Heuristic 2: 3+ consecutive Hebrew words
                if re.search(r'[\u0590-\u05FF]+\s+[\u0590-\u05FF]+\s+[\u0590-\u05FF]+', content):
                    return content
                return match.group(0)
            
            data = re.sub(r'\$\$(.+?)\$\$', _unwrap_hebrew, data, flags=re.DOTALL)
            
            # Preserve display math: $$...$$
            # Remove orphan $$: "text $$" or "$$ text"
            
            # Strategy: Protect display math blocks, purge orphans, restore protected
            protected = []
            
            def protect_display_math(match):
                protected.append(match.group(0))
                return f"__DISPLAY_MATH_{len(protected)-1}__"
            
            # Protect $$...$$ blocks (non-greedy match)
            result = re.sub(r'\$\$[^$]+?\$\$', protect_display_math, data)
            
            # Now purge orphan $$ (multiple consecutive $)
            result = re.sub(r'\$\$+', '$', result)
            
            # Restore protected blocks
            for i, block in enumerate(protected):
                result = result.replace(f"__DISPLAY_MATH_{i}__", block)
            
            if result != data:
                print(f"💲 [BIT-LOG] Purged orphan $$ and fixed quadruple $: '{data[:50]}...' → '{result[:50]}...'")
            return result
        if isinstance(data, dict):
            return {k: self._purge_double_dollars(v) for k, v in data.items()}
        if isinstance(data, list):
            return [self._purge_double_dollars(i) for i in data]
        return data

    def _error_response(self):
        return build_standard_response(
            final_answer="המורה למתמטיקה נתקל בקושי. נסה שוב.",
            teacher_summary="המורה למתמטיקה מתנצל, אך חלה שגיאה לא צפויה.",
            logic_error=True,
            response_type="error"
        )

    def _sanitize_for_sympy(self, expr: str) -> str:
        """✅ V231.4: Robust SymPy sanitizer.
        Converts \\frac{4}{3}x -> (4)/(3)*x, handles all implicit multiplication."""
        s = str(expr)
        
        # Step 0: Pre-algebraic cleanup (Remove UI/Geometric/Text notation)
        s = re.sub(r'\\color\{.*?\}(?:\{.*?\})?', '', s) # Aggressively strip color tags
        s = re.sub(r'\\text\{[^{}]*\}', '', s)
        s = re.sub(r'\^(\{\\circ\}|\\circ)', '', s)
        s = re.sub(r'\\(angle|triangle|quad|qquad)', '', s)
        s = re.sub(r'\band\b', '', s)
        s = re.sub(r'\s*,\s*', ' ', s)  # Remove commas with surrounding space
        
        # Step 1: Convert LaTeX fractions PROPERLY: \frac{a}{b} -> (a)/(b)
        while '\\frac' in s:
            s = re.sub(r'\\frac\s*\{([^{}]*)\}\{([^{}]*)\}', r'(\1)/(\2)', s)
            if '\\frac' in s and '{' not in s:
                s = s.replace('\\frac', '')
        
        # Step 2: Convert other LaTeX commands
        s = s.replace('\\cdot', '*').replace('\\times', '*')
        s = s.replace('\\pi', 'pi').replace('\\sqrt', 'sqrt')
        s = s.replace('\\sin', 'sin').replace('\\cos', 'cos').replace('\\tan', 'tan')
        s = s.replace('\\ln', 'ln').replace('\\log', 'log')
        s = s.replace('\\left', '').replace('\\right', '')
        
        # Step 3: Convert remaining braces to parens
        s = s.replace('{', '(').replace('}', ')')
        
        # Step 4: Convert ^ to **
        s = s.replace('^', '**')
        
        # Step 5: Implicit multiplication (run multiple passes)
        for _ in range(3):  # Multiple passes catch nested cases
            s = re.sub(r'(\d)([a-zA-Z(])', r'\1*\2', s)         # 4x → 4*x, 2( → 2*(
            s = re.sub(r'\)([a-zA-Z0-9(])', r')*\1', s)          # )x → )*x, )( → )*(
            s = re.sub(r'([a-zA-Z])(?<![a-zA-Z])\(', r'\1*(', s) # x( → x*( but not sin(
        
        # Step 6: Fix function-call implicit mul that shouldn't be there
        for func in ['sin', 'cos', 'tan', 'sqrt', 'log', 'ln', 'exp', 'Abs', 'abs', 'pi']:
            s = s.replace(f'{func}*(', f'{func}(')
        
        # Step 7: Clean whitespace — only strip truly invalid chars
        s = re.sub(r'[^\S\r\n]+', ' ', s)
        s = re.sub(r'[\u0590-\u05FF\u200B-\u200D\uFEFF\'"`;@#%&!?]', '', s)
        s = re.sub(r'\\[a-zA-Z]+', '', s)
        
        # Step 8: Remove leading = or trailing junk
        s = s.lstrip('=').rstrip('.,;')
        
        print(f"📈 🔵 [BIT-LOG] SymPy Sanitized: '{expr}' → '{s}'")
        return s

    def _deep_sanitize_math(self, text: str) -> str:
        """V281.1: Aggressively strips non-printable characters from math blocks."""
        if not text: return ""
        # Remove Tabs, Newlines, and multiple spaces which break KaTeX
        s = text.replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
        s = re.sub(r'\s+', ' ', s)
        return s.strip()

    async def _validate_anchor(self, data_anchor: dict, image_data: bytes, problem_text: str = "") -> dict:
        """V8.9.2: Single Source of Truth Validator pass."""
        try:
            from prompts import get_anchor_validation_prompt
            from utils.safe_json import safe_extract_json
            
            prompt = get_anchor_validation_prompt(data_anchor)
            
            # Using current model which supports Vision
            response = await self.model.generate_content_async(
                [
                    {"mime_type": "image/jpeg", "data": image_data},
                    prompt
                ]
            )
            
            match = re.search(r'\{.*\}', response.text, re.DOTALL)
            if match:
                clean_anchor = safe_extract_json(match.group(), "anchor_validator")
                if clean_anchor:
                    print(f"🛡️ ✅ [V8.9.2] Anchor Validated: {len(clean_anchor.get('function_equations', []))} equations found.")
                    return clean_anchor
            
            # V8.9.6: If validator returns garbage, Fallback to raw OCR
            print("⚠️ [V8.9.6] Validator returned invalid JSON. Falling back to raw OCR.")
            return {"raw_ocr_text": problem_text}
        except Exception as e:
            print(f"⚠️ [V8.9.2] Anchor Validation failed: {e}. Falling back to raw OCR.")
            return {"raw_ocr_text": problem_text}

    async def _save_exercise_history(self, uid: str, question: str, solutions: list):
        """V317.0: Saves sanitized exercise history with clean titles."""
        try:
            db = firebase_manager.get_db()
            if not db or not uid: return

            def generate_clean_title(ocr_raw_text):
                try:
                    # מנקה JSON אם קיים
                    if isinstance(ocr_raw_text, str) and ocr_raw_text.strip().startswith('{'):
                        match = re.search(r'\{.*\}', ocr_raw_text, re.DOTALL)
                        if match:
                            data = safe_json_loads(match.group())
                            text = data.get('text', '')
                        else:
                            text = str(ocr_raw_text)
                    else:
                        text = str(ocr_raw_text)
                    
                    # ניקוי LaTeX וסימנים טכניים מתקדם
                    # 1. הסרת בלוקים של מתמטיקה $...$
                    text = re.sub(r'\$.*?\$', '', text)
                    # 2. הסרת פקודות LaTeX נפוצות (למשל \frac{...}{...})
                    text = re.sub(r'\\[a-zA-Z]+', '', text)
                    # 3. הסרת סוגריים מסולסלים ומרובעים
                    text = re.sub(r'[\{\}\[\]]', '', text)
                    # 4. ניקוי סימנים מתמטיים שאריתיים
                    text = re.sub(r'[\^_*=+\-/|]', '', text)
                    
                    # חיתוך ל-6 מילים ראשונות
                    words = text.split()
                    if not words: return "תרגיל במתמטיקה"
                    title = " ".join(words[:6])
                    if len(words) > 6: title += "..."
                    return title.replace('\n', ' ').strip()
                except Exception as e:
                    logging.debug(f"⚠️ [BIT-LOG] Title generation failed: {e}")
                    return "תרגיל במתמטיקה"
            
            # Flatten solution steps into a single string
            solution_text_parts = []
            for sol in solutions:
                res = sol.get("response", {})
                if "sections" in res:
                    for section in res["sections"]:
                        title = section.get("section_title", "")
                        solution_text_parts.append(f"### {title}")
                        for step in section.get("steps", []):
                            exp = step.get("explanation_text", "") or ""
                            math = step.get("math_artifact", {}).get("latex", "") or ""
                            if not math: math = step.get("block_math", "") or ""
                            
                            solution_text_parts.append(str(exp))
                            if math:
                                math = self._deep_sanitize_math(str(math))
                                solution_text_parts.append(f"$${math}$$")
                solution_text_parts.append("---")
            
            full_solution = "\n\n".join(solution_text_parts)
            
            from firebase_admin import firestore
            import datetime
            # Save to history collection with clean title
            history_ref = db.collection('users').document(uid).collection('history').document()
            history_ref.set({
                "original_question_text": question,
                "display_title": generate_clean_title(question),
                "solution_steps_text": full_solution,
                "timestamp": firestore.SERVER_TIMESTAMP
            })
            print(f"✅ [HISTORY] Successfully saved exercise to DB: users/{uid}/history")
        except Exception as e:
            print(f"❌ [HISTORY] Error saving history: {e}")
            import traceback
            traceback.print_exc()

    # ===================== EXISTING PIPELINE METHODS =====================

    def _smart_minify(self, data):
        """V226.1+ Line-based minifier that preserves newlines around Math blocks."""
        if isinstance(data, str):
            clean = data.replace('\r\n', '\n').replace('\r', '\n')
            token = "###NEWLINE_TOKEN###"
            clean = re.sub(r'\n\s*\n', token, clean)
            lines = clean.split('\n')
            result = []
            for i, line in enumerate(lines):
                stripped = line.strip()
                if not stripped: continue
                should_keep = False
                if stripped.endswith('$$') or stripped.startswith('$$'): should_keep = True
                if i + 1 < len(lines):
                    next_stripped = lines[i+1].strip()
                    if next_stripped.startswith('$$') or next_stripped.startswith('*') or next_stripped.startswith('-'): should_keep = True
                    if next_stripped.startswith('**'): should_keep = True
                result.append(stripped)
                if should_keep: result.append('\n')
                else: result.append(' ')
            final_text = "".join(result).replace(token, '\n\n')
            final_text = re.sub(r' +', ' ', final_text)
            return final_text.strip()
        elif isinstance(data, dict):
            return {k: self._smart_minify(v) for k, v in data.items() if v is not None}
        elif isinstance(data, list):
            return [self._smart_minify(i) for i in data if i is not None]
        return data

    def _sanitize_teacher_response(self, text: str) -> str:
        """
        V275.2: ANTI-CHATTER GUARD 🛡️
        Removes ENTIRE SENTENCES containing apologetic/self-correction language.
        V261.7: PIPELINE SYNC - Now also runs sanitize_math_text!
        """
        if not text: return ""
        
        # V275.4: Expanded chatter phrases - catches self-correction monologue from LLM
        chatter_words = (
            r"(oops|sorry|mistake|apologize|let me correct|my bad"
            r"|אופס|סליחה|טעות|מתנצל|תיקון|שגיתי|טעיתי|מצטער"
            r"|בוא נתקן|רגע|שים לב לטעות|הייתה טעות|נפלה טעות|תיקון טעות|בחישוב הקודם"
            # V275.4: New patterns from investigation question logs
            r"|טעות חשיבה|בוא ננסה שוב|סוף סוף הבנתי|על הבלבול"
            r"|זה לא עובד|מה עושים|זה לא טוב|זה לא נכון|מה קורה פה"
            r"|בוא נחשוב על זה|שלי הייתה|בוא נחזור אחורה|בוא נתחיל מהתחלה"
            r"|אני קצת מבולבל|על כל הטעויות|נוספת|קונספטואלית|בשאלה"
            r"|wait|רגע|let me think|hold on)"
        )
        
        # Regex to match the ENTIRE SENTENCE containing one of the chatter phrases
        # Matches from previous period/newline to the next period/newline
        chatter_sentence_pattern = r"(?i)\s*[^.!?\n]*?(?:" + chatter_words + r")[^.!?\n]*?[.!?]?"
        
        cleaned = text
        cleaned = re.sub(chatter_sentence_pattern, "", cleaned)
            
        # V261.7: Global Math Sanitization (Fixes mangled ( x )^2 etc.)
        cleaned = sanitize_math_text(cleaned)
            
        return cleaned.strip()

    def _scrub_latex_from_text(self, data):
        """✅ V261.3: Aggressively scrub LaTeX commands from Hebrew text."""
        if isinstance(data, str):
            # 1. Protect math blocks $...$ and $$...$$
            protected = []
            def protect(match):
                protected.append(match.group(0))
                return f"__MATH_BLOCK_{len(protected)-1}__"
            
            temp = re.sub(r'\$\$[^$]+?\$\$', protect, data)
            temp = re.sub(r'(?<!\$)\$(?!\$)[^$]+?(?<!\$)\$(?!\$)', protect, temp)
            
            # 2. Scrub LaTeX commands from the TEXT parts
            # Remove \cdot, \quad, \text{...}, etc.
            temp = temp.replace(r'\cdot', '')
            temp = temp.replace(r'\quad', ' ')
            temp = temp.replace(r'\text', '')
            # Remove any other backslash commands in text
            temp = re.sub(r'\\[a-zA-Z]+', '', temp)
            # Remove orphan braces { } often left behind by \text{}
            temp = temp.replace('{', '').replace('}', '')
            
            # 3. Restore math blocks
            for i, block in enumerate(protected):
                temp = temp.replace(f"__MATH_BLOCK_{i}__", block)
            
            return temp
            
        elif isinstance(data, dict):
            return {k: self._scrub_latex_from_text(v) for k, v in data.items()}
        elif isinstance(data, list):
            return [self._scrub_latex_from_text(i) for i in data]
        return data

    def _sanitize_and_split(self, data):
        """V230.4: Mixed-to-Inline Fix. Modified to allow equations to stay as block math."""
        if isinstance(data, str):
            text_outside_math = re.sub(r'\$\$.*?\$\$', '', data, flags=re.DOTALL)
            has_hebrew_outside = bool(re.search(r'[\u0590-\u05FF]', text_outside_math))
            
            def splitter(match):
                content = match.group(1)
                # If there's Hebrew *inside* the math block, it's probably mixed text, so inline it.
                if re.search(r'[\u0590-\u05FF]', content):
                    return f" ${content}$ "
                
                # V8.8: No longer eagerly downgrading pure math to inline just because there's
                # Hebrew *outside* the math block. We want calculations on their own line.
                # Just keep it as block math.
                return f"$${content}$$"
            
            return re.sub(r'\$\$(.*?)\$\$', splitter, data, flags=re.DOTALL)
        elif isinstance(data, dict):
            return {k: self._sanitize_and_split(v) for k, v in data.items()}
        elif isinstance(data, list):
            return [self._sanitize_and_split(i) for i in data]
        return data

orchestrator = BuddyOrchestrator()