"""FastAPI web service wrapper for SPARC patent analysis.

Provides REST API endpoints for analyzing company patent portfolios.
"""

from contextlib import asynccontextmanager
from datetime import datetime
from typing import Annotated, List

from fastapi import BackgroundTasks, Depends, FastAPI, HTTPException, Query, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse, StreamingResponse
from pydantic import BaseModel, EmailStr, Field
from slowapi import Limiter
from slowapi.errors import RateLimitExceeded
from slowapi.util import get_remote_address

from SPARC import config
from SPARC.analyzer import CompanyAnalyzer
from SPARC.auth import (
    TokenResponse,
    UserResponse,
    check_jwt_secret,
    close_db_client,
    create_tokens,
    decode_token,
    get_current_admin,
    get_current_user,
    get_db_client,
    init_db_client,
)
from SPARC.types import BatchAnalysisResult, CompanyAnalysisResult


# Pydantic models for API
class CompanyAnalysisResponse(BaseModel):
    """Response model for single company analysis."""

    company_name: str
    analysis: str
    patent_count: int
    success: bool
    error: str | None = None
    model: str | None = None
    timestamp: datetime


class BatchAnalysisResponse(BaseModel):
    """Response model for batch company analysis."""

    results: list[CompanyAnalysisResponse]
    total_companies: int
    successful: int
    failed: int
    timestamp: datetime


class CompanyAnalysisRequest(BaseModel):
    """Request model for single company analysis with optional model selection."""

    model: str | None = Field(
        default=None,
        description="LLM model to use (e.g. 'anthropic/claude-3.5-sonnet', 'openai/gpt-4o'). Defaults to server config.",
    )


class BatchAnalysisRequest(BaseModel):
    """Request model for batch company analysis."""

    companies: list[str] = Field(
        ..., min_length=1, max_length=20, description="List of company names to analyze"
    )
    max_workers: int = Field(
        default=3, ge=1, le=5, description="Max concurrent analyses"
    )
    model: str | None = Field(
        default=None,
        description="LLM model to use for all analyses in this batch. Defaults to server config.",
    )


class JobStatus(BaseModel):
    """Status of a background analysis job."""

    job_id: str
    status: str  # "pending", "running", "completed", "failed"
    progress: int  # 0-100
    total_companies: int
    completed_companies: int
    result: BatchAnalysisResponse | None = None
    error: str | None = None


class PaginatedJobsResponse(BaseModel):
    """Paginated response for job listings."""

    items: list["JobStatus"]
    next_cursor: str | None = None


class HealthResponse(BaseModel):
    """Health check response."""

    status: str
    version: str
    timestamp: datetime


# Auth request/response models
class RegisterRequest(BaseModel):
    """User registration request."""

    email: EmailStr
    password: str = Field(..., min_length=8, description="Password (min 8 characters)")


class LoginRequest(BaseModel):
    """User login request."""

    email: EmailStr
    password: str


class RefreshRequest(BaseModel):
    """Token refresh request."""

    refresh_token: str


class UpdateRoleRequest(BaseModel):
    """Update user role request."""

    role: str = Field(..., pattern="^(admin|user)$")


class AnalyticsResponse(BaseModel):
    """Analytics response model."""

    total_messages: int
    by_company: List[dict]
    by_type: List[dict]
    period_days: int


# Job counter for generating unique IDs (the actual state is in PostgreSQL)
_job_counter = 0


def _convert_result(result: CompanyAnalysisResult) -> CompanyAnalysisResponse:
    """Convert internal result to API response model."""
    return CompanyAnalysisResponse(
        company_name=result.company_name,
        analysis=result.analysis,
        patent_count=result.patent_count,
        success=result.success,
        error=result.error,
        model=result.model,
        timestamp=result.timestamp,
    )


def _convert_batch_result(result: BatchAnalysisResult) -> BatchAnalysisResponse:
    """Convert internal batch result to API response model."""
    return BatchAnalysisResponse(
        results=[_convert_result(r) for r in result.results],
        total_companies=result.total_companies,
        successful=result.successful,
        failed=result.failed,
        timestamp=result.timestamp,
    )


# Global analyzer instance
_analyzer: CompanyAnalyzer | None = None


@asynccontextmanager
async def lifespan(app: FastAPI):
    """Initialize resources on startup, clean up on shutdown."""
    global _analyzer
    check_jwt_secret()
    init_db_client()
    _analyzer = CompanyAnalyzer()
    # Mark any jobs that were running/pending before the restart as failed
    from SPARC.database import DatabaseClient
    _db = DatabaseClient(config.database_url)
    _db.connect()
    _db.initialize_schema()
    stale = _db.mark_stale_jobs_failed()
    if stale:
        import logging
        logging.getLogger(__name__).warning("Marked %d stale jobs as failed on startup", stale)
    _db.close()
    # Start scheduled analysis if tracked companies are configured
    from SPARC.scheduler import start_scheduler
    start_scheduler()
    yield
    # Cleanup
    _analyzer = None
    close_db_client()


app = FastAPI(
    title="SPARC API",
    description="Semiconductor Patent & Analytics Report Core - Patent portfolio analysis using AI",
    version="1.0.0",
    lifespan=lifespan,
    root_path=config.root_path,
)

# Rate limiter (in-memory storage, suitable for single-instance deployments)
limiter = Limiter(key_func=get_remote_address)
app.state.limiter = limiter


@app.exception_handler(RateLimitExceeded)
async def rate_limit_handler(request: Request, exc: RateLimitExceeded):
    """Return 429 with Retry-After header when rate limit is exceeded."""
    retry_after = getattr(exc, "retry_after", 60)
    return JSONResponse(
        status_code=429,
        content={"detail": "Rate limit exceeded. Please try again later."},
        headers={"Retry-After": str(retry_after)},
    )


# Add CORS middleware for React frontend
app.add_middleware(
    CORSMiddleware,
    allow_origins=config.cors_origins,
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


# ============== Auth Endpoints ==============


@app.post("/auth/register", response_model=UserResponse, tags=["Auth"])
@limiter.limit("5/minute")
async def register(request: Request, body: RegisterRequest):
    """Register a new user.

    The first registered user automatically becomes an admin.
    """
    db = get_db_client()

    # First user becomes admin
    user_count = db.get_user_count()
    role = "admin" if user_count == 0 else "user"

    user = db.create_user(
        email=body.email,
        password=body.password,
        role=role,
    )

    if not user:
        raise HTTPException(
            status_code=400,
            detail="Email already registered",
        )

    return UserResponse(
        id=user["id"],
        email=user["email"],
        role=user["role"],
        created_at=user["created_at"],
    )


@app.post("/auth/login", response_model=TokenResponse, tags=["Auth"])
@limiter.limit("10/minute")
async def login(request: Request, body: LoginRequest):
    """Authenticate user and return JWT tokens."""
    db = get_db_client()

    user = db.authenticate_user(body.email, body.password)

    if not user:
        raise HTTPException(
            status_code=401,
            detail="Invalid email or password",
        )

    return create_tokens(user["id"], user["email"], user["role"])


@app.post("/auth/refresh", response_model=TokenResponse, tags=["Auth"])
async def refresh_token(request: RefreshRequest):
    """Refresh access token using refresh token."""
    payload = decode_token(request.refresh_token)

    if not payload or payload.type != "refresh":
        raise HTTPException(
            status_code=401,
            detail="Invalid refresh token",
        )

    db = get_db_client()
    user = db.get_user_by_id(payload.user_id)

    if not user:
        raise HTTPException(
            status_code=401,
            detail="User not found",
        )

    return create_tokens(user["id"], user["email"], user["role"])


@app.get("/auth/me", response_model=UserResponse, tags=["Auth"])
async def get_me(current_user: UserResponse = Depends(get_current_user)):
    """Get current authenticated user."""
    return current_user


# ============== Admin Endpoints ==============


@app.get("/admin/users", response_model=List[UserResponse], tags=["Admin"])
async def list_users(
    limit: int = Query(default=100, ge=1, le=1000),
    offset: int = Query(default=0, ge=0),
    _: UserResponse = Depends(get_current_admin),
):
    """List all users (admin only)."""
    db = get_db_client()
    users = db.get_all_users(limit=limit, offset=offset)

    return [
        UserResponse(
            id=u["id"],
            email=u["email"],
            role=u["role"],
            created_at=u["created_at"],
        )
        for u in users
    ]


@app.patch("/admin/users/{user_id}/role", response_model=UserResponse, tags=["Admin"])
async def update_user_role(
    user_id: int,
    request: UpdateRoleRequest,
    current_admin: UserResponse = Depends(get_current_admin),
):
    """Update a user's role (admin only)."""
    if user_id == current_admin.id:
        raise HTTPException(
            status_code=400,
            detail="Cannot change your own role",
        )

    db = get_db_client()
    user = db.update_user_role(user_id, request.role)

    if not user:
        raise HTTPException(
            status_code=404,
            detail="User not found",
        )

    return UserResponse(
        id=user["id"],
        email=user["email"],
        role=user["role"],
        created_at=user["created_at"],
    )


@app.delete("/admin/users/{user_id}", tags=["Admin"])
async def delete_user(
    user_id: int,
    current_admin: UserResponse = Depends(get_current_admin),
):
    """Delete a user (admin only)."""
    if user_id == current_admin.id:
        raise HTTPException(
            status_code=400,
            detail="Cannot delete yourself",
        )

    db = get_db_client()
    deleted = db.delete_user(user_id)

    if not deleted:
        raise HTTPException(
            status_code=404,
            detail="User not found",
        )

    return {"message": "User deleted"}


# ============== Tracked Companies Endpoints ==============


class TrackCompanyRequest(BaseModel):
    """Request to add a company to tracking."""

    company_name: str = Field(..., min_length=1, max_length=255)


@app.get("/admin/tracked", tags=["Admin"])
async def list_tracked_companies(
    _: UserResponse = Depends(get_current_admin),
):
    """List all tracked companies (admin only)."""
    db = get_db_client()
    return db.list_tracked_companies()


@app.post("/admin/tracked", tags=["Admin"])
async def add_tracked_company(
    request: TrackCompanyRequest,
    _: UserResponse = Depends(get_current_admin),
):
    """Add a company to the tracked list (admin only)."""
    db = get_db_client()
    result = db.add_tracked_company(request.company_name)
    if not result:
        raise HTTPException(status_code=409, detail="Company already tracked")
    return result


@app.delete("/admin/tracked/{company_name}", tags=["Admin"])
async def remove_tracked_company(
    company_name: str,
    _: UserResponse = Depends(get_current_admin),
):
    """Remove a company from the tracked list (admin only)."""
    db = get_db_client()
    removed = db.remove_tracked_company(company_name)
    if not removed:
        raise HTTPException(status_code=404, detail="Company not found in tracking list")
    return {"message": f"Stopped tracking {company_name}"}


@app.get("/admin/alerts", tags=["Admin"])
async def list_alerts(
    limit: int = Query(default=50, ge=1, le=200),
    _: UserResponse = Depends(get_current_admin),
):
    """List recent alerts from scheduled analysis (admin only)."""
    db = get_db_client()
    return db.list_alerts(limit=limit)


# ============== Analytics Endpoint ==============


@app.get("/analytics", response_model=AnalyticsResponse, tags=["Analytics"])
async def get_analytics(
    days: int = Query(default=30, ge=1, le=365),
    _: UserResponse = Depends(get_current_user),
):
    """Get analytics data (authenticated users only)."""
    db = get_db_client()
    analytics = db.get_analytics(days=days)

    return AnalyticsResponse(
        total_messages=analytics["total_messages"],
        by_company=analytics["by_company"],
        by_type=analytics["by_type"],
        period_days=analytics["period_days"],
    )


# ============== Model Selection Endpoints ==============

# Supported models via OpenRouter
SUPPORTED_MODELS = [
    {"id": "anthropic/claude-3.5-sonnet", "name": "Claude 3.5 Sonnet", "provider": "Anthropic"},
    {"id": "openai/gpt-4o", "name": "GPT-4o", "provider": "OpenAI"},
    {"id": "openai/gpt-4o-mini", "name": "GPT-4o Mini", "provider": "OpenAI"},
    {"id": "google/gemini-pro-1.5", "name": "Gemini Pro 1.5", "provider": "Google"},
    {"id": "meta-llama/llama-3.1-70b-instruct", "name": "Llama 3.1 70B", "provider": "Meta"},
]


@app.get("/models", tags=["System"])
async def list_models():
    """List supported LLM models for analysis.

    Returns the available models that can be passed as the `model` field
    in analysis requests. The default model is determined by the `MODEL`
    environment variable on the server.
    """
    return {
        "models": SUPPORTED_MODELS,
        "default": config.model,
    }


@app.get("/analytics/trends", tags=["Analytics"])
async def get_analytics_trends(
    days: int = Query(default=90, ge=7, le=365),
    _: UserResponse = Depends(get_current_user),
):
    """Get trend data for patent analysis over time.

    Returns two datasets:
    - ``by_month``: analysis count per company per month
    - ``by_type_over_time``: analysis type distribution per month

    Args:
        days: Number of days to look back (default 90)

    Returns:
        Trend data suitable for time-series and distribution charts
    """
    db = get_db_client()

    with db.get_conn() as conn:
        with conn.cursor() as cur:
            # Analyses per company per month
            cur.execute(
                """
                SELECT
                    TO_CHAR(timestamp, 'YYYY-MM') AS month,
                    company_name,
                    COUNT(*) AS count
                FROM llm_messages
                WHERE timestamp >= NOW() - INTERVAL '%s days'
                    AND is_cached = FALSE
                    AND company_name IS NOT NULL
                GROUP BY month, company_name
                ORDER BY month
                """,
                (days,),
            )
            by_month_rows = cur.fetchall()

            # Analysis type distribution per month
            cur.execute(
                """
                SELECT
                    TO_CHAR(timestamp, 'YYYY-MM') AS month,
                    analysis_type,
                    COUNT(*) AS count
                FROM llm_messages
                WHERE timestamp >= NOW() - INTERVAL '%s days'
                    AND is_cached = FALSE
                GROUP BY month, analysis_type
                ORDER BY month
                """,
                (days,),
            )
            by_type_rows = cur.fetchall()

    by_month = [
        {"month": row[0], "company_name": row[1], "count": row[2]}
        for row in by_month_rows
    ]
    by_type_over_time = [
        {"month": row[0], "analysis_type": row[1], "count": row[2]}
        for row in by_type_rows
    ]

    return {
        "by_month": by_month,
        "by_type_over_time": by_type_over_time,
        "period_days": days,
    }


# ============== Export Endpoints ==============


@app.get("/export/{company_name}", tags=["Export"])
async def export_company_csv(
    company_name: str,
    _: UserResponse = Depends(get_current_user),
):
    """Export analysis results for a company as a CSV file.

    Returns all stored analysis records for the given company, including
    analysis type, model used, response text, and timestamp.

    Args:
        company_name: Company name to export results for

    Returns:
        CSV file download
    """
    import csv
    import io

    db = get_db_client()
    # Query all non-cached analysis results for this company
    with db.get_conn() as conn:
        with conn.cursor() as cur:
            cur.execute(
                """
                SELECT company_name, analysis_type, model, response, timestamp
                FROM llm_messages
                WHERE LOWER(company_name) = LOWER(%s) AND is_cached = FALSE
                ORDER BY timestamp DESC
                """,
                (company_name,),
            )
            rows = cur.fetchall()

    if not rows:
        raise HTTPException(status_code=404, detail=f"No analysis results found for '{company_name}'")

    output = io.StringIO()
    writer = csv.writer(output)
    writer.writerow(["company_name", "analysis_type", "model", "analysis", "timestamp"])
    for row in rows:
        writer.writerow(row)

    output.seek(0)
    safe_name = company_name.replace(" ", "_").lower()
    return StreamingResponse(
        iter([output.getvalue()]),
        media_type="text/csv",
        headers={"Content-Disposition": f'attachment; filename="sparc_{safe_name}_export.csv"'},
    )


@app.get("/export/{company_name}/pdf", tags=["Export"])
async def export_company_pdf(
    company_name: str,
    _: UserResponse = Depends(get_current_user),
):
    """Export analysis results for a company as a formatted PDF report.

    Returns all stored analysis records for the given company, including
    analysis type, model used, response text, and timestamp, formatted
    as a downloadable PDF document.

    Args:
        company_name: Company name to export results for

    Returns:
        PDF file download
    """
    import io
    import textwrap

    from reportlab.lib import colors
    from reportlab.lib.pagesizes import letter
    from reportlab.lib.styles import ParagraphStyle, getSampleStyleSheet
    from reportlab.lib.units import inch
    from reportlab.platypus import (
        Paragraph,
        SimpleDocTemplate,
        Spacer,
        Table,
        TableStyle,
    )

    db = get_db_client()
    with db.get_conn() as conn:
        with conn.cursor() as cur:
            cur.execute(
                """
                SELECT company_name, analysis_type, model, response, timestamp
                FROM llm_messages
                WHERE LOWER(company_name) = LOWER(%s) AND is_cached = FALSE
                ORDER BY timestamp DESC
                """,
                (company_name,),
            )
            rows = cur.fetchall()

    if not rows:
        raise HTTPException(status_code=404, detail=f"No analysis results found for '{company_name}'")

    buffer = io.BytesIO()
    doc = SimpleDocTemplate(
        buffer,
        pagesize=letter,
        rightMargin=0.75 * inch,
        leftMargin=0.75 * inch,
        topMargin=0.75 * inch,
        bottomMargin=0.75 * inch,
    )

    styles = getSampleStyleSheet()
    title_style = ParagraphStyle(
        "CustomTitle",
        parent=styles["Title"],
        fontSize=20,
        spaceAfter=6,
    )
    subtitle_style = ParagraphStyle(
        "Subtitle",
        parent=styles["Normal"],
        fontSize=11,
        textColor=colors.grey,
        spaceAfter=20,
    )
    heading_style = ParagraphStyle(
        "SectionHeading",
        parent=styles["Heading2"],
        fontSize=13,
        spaceBefore=16,
        spaceAfter=8,
        textColor=colors.HexColor("#1a1a2e"),
    )
    body_style = ParagraphStyle(
        "BodyText",
        parent=styles["Normal"],
        fontSize=9,
        leading=13,
        spaceAfter=10,
    )

    elements = []

    # Title and date
    display_name = rows[0][0]  # Use the casing from the database
    analysis_date = datetime.now().strftime("%Y-%m-%d")
    elements.append(Paragraph(f"SPARC Analysis Report: {display_name}", title_style))
    elements.append(Paragraph(f"Generated on {analysis_date}", subtitle_style))

    # Summary table
    summary_data = [
        ["Total Analyses", str(len(rows))],
        ["Analysis Types", ", ".join(sorted(set(r[1] for r in rows)))],
        ["Models Used", ", ".join(sorted(set(r[2] for r in rows)))],
    ]
    summary_table = Table(summary_data, colWidths=[2 * inch, 4.5 * inch])
    summary_table.setStyle(
        TableStyle(
            [
                ("BACKGROUND", (0, 0), (0, -1), colors.HexColor("#f0f0f5")),
                ("FONTNAME", (0, 0), (0, -1), "Helvetica-Bold"),
                ("FONTSIZE", (0, 0), (-1, -1), 9),
                ("PADDING", (0, 0), (-1, -1), 6),
                ("GRID", (0, 0), (-1, -1), 0.5, colors.HexColor("#cccccc")),
                ("VALIGN", (0, 0), (-1, -1), "TOP"),
            ]
        )
    )
    elements.append(summary_table)
    elements.append(Spacer(1, 16))

    # Individual analysis sections
    for i, row in enumerate(rows, 1):
        _, analysis_type, model, response, timestamp = row
        ts_str = timestamp.strftime("%Y-%m-%d %H:%M:%S") if hasattr(timestamp, "strftime") else str(timestamp)

        elements.append(
            Paragraph(f"Analysis {i}: {analysis_type} (via {model})", heading_style)
        )
        elements.append(
            Paragraph(f"<i>Performed: {ts_str}</i>", body_style)
        )

        # Wrap long response text into paragraphs, escaping XML special chars
        safe_response = (
            response.replace("&", "&amp;")
            .replace("<", "&lt;")
            .replace(">", "&gt;")
        )
        # Split into manageable paragraphs to avoid overflow
        for line in safe_response.split("\n"):
            if line.strip():
                elements.append(Paragraph(line, body_style))
            else:
                elements.append(Spacer(1, 4))

        elements.append(Spacer(1, 10))

    doc.build(elements)
    buffer.seek(0)

    safe_name = company_name.replace(" ", "_").lower()
    filename = f"{safe_name}-analysis-{analysis_date}.pdf"
    return StreamingResponse(
        iter([buffer.getvalue()]),
        media_type="application/pdf",
        headers={"Content-Disposition": f'attachment; filename="{filename}"'},
    )


# ============== System Endpoints ==============


@app.get("/health", response_model=HealthResponse, tags=["System"])
async def health_check():
    """Check API health status."""
    return HealthResponse(
        status="healthy",
        version="1.0.0",
        timestamp=datetime.now(),
    )


@app.get(
    "/analyze/{company_name}",
    response_model=CompanyAnalysisResponse,
    tags=["Analysis"],
)
async def analyze_company(
    company_name: str,
    model: str | None = Query(default=None, description="LLM model to use (e.g. 'openai/gpt-4o'). Defaults to server config."),
    _: UserResponse = Depends(get_current_user),
):
    """Analyze a single company's patent portfolio.

    This endpoint retrieves recent patents for the specified company,
    parses them, and uses AI to generate a comprehensive analysis.

    Args:
        company_name: Name of the company to analyze (e.g., "nvidia", "intel")
        model: Optional LLM model override

    Returns:
        Analysis results including patent count, AI insights, and success status
    """
    if not _analyzer:
        raise HTTPException(status_code=503, detail="Analyzer not initialized")

    result = _analyzer._analyze_company_safe(company_name, model=model)
    return _convert_result(result)


@app.get(
    "/analyze/patent/{patent_id}",
    tags=["Analysis"],
)
async def analyze_single_patent(
    patent_id: str,
    company_name: str = Query(description="Company name for analysis context"),
    _: UserResponse = Depends(get_current_user),
):
    """Analyze a single patent by its publication ID.

    If the patent PDF is not already cached locally, the system will attempt
    to download it automatically from a previously cached link. If no link
    is available, a 404 error is returned.

    Args:
        patent_id: Patent publication ID (e.g. "US-11234567-B2")
        company_name: Company name for analysis context

    Returns:
        Analysis text for the patent
    """
    if not _analyzer:
        raise HTTPException(status_code=503, detail="Analyzer not initialized")

    try:
        analysis = _analyzer.analyze_single_patent(patent_id, company_name)
        return {"patent_id": patent_id, "company_name": company_name, "analysis": analysis}
    except FileNotFoundError as e:
        raise HTTPException(status_code=404, detail=str(e))


@app.post(
    "/analyze/batch",
    response_model=BatchAnalysisResponse,
    tags=["Analysis"],
)
async def analyze_companies_batch(
    request: BatchAnalysisRequest,
    _: UserResponse = Depends(get_current_user),
):
    """Analyze multiple companies' patent portfolios.

    Processes companies concurrently for improved performance.
    Limited to 20 companies per request.

    Args:
        request: List of company names and optional worker count

    Returns:
        Batch results with individual company analyses and summary statistics
    """
    if not _analyzer:
        raise HTTPException(status_code=503, detail="Analyzer not initialized")

    result = _analyzer.analyze_companies(
        companies=request.companies,
        max_workers=request.max_workers,
        model=request.model,
    )
    return _convert_batch_result(result)


def _get_job_db() -> "DatabaseClient":
    """Get a DatabaseClient for job persistence."""
    from SPARC.database import DatabaseClient
    db = DatabaseClient(config.database_url)
    return db


def _job_row_to_status(row: dict) -> JobStatus:
    """Convert a database job row to a JobStatus model."""
    import json as _json
    result = None
    if row.get("result_json"):
        result_data = row["result_json"]
        if isinstance(result_data, str):
            result_data = _json.loads(result_data)
        result = BatchAnalysisResponse(**result_data)
    return JobStatus(
        job_id=row["job_id"],
        status=row["status"],
        progress=row["progress"],
        total_companies=row["total_companies"],
        completed_companies=row["completed_companies"],
        result=result,
        error=row.get("error"),
    )


def _run_batch_job(job_id: str, companies: list[str], max_workers: int, model: str | None = None):
    """Background task for batch analysis."""
    import json as _json
    global _analyzer

    db = _get_job_db()

    if not _analyzer:
        db.update_job(job_id, status="failed", error="Analyzer not initialized")
        return

    db.update_job(job_id, status="running")

    def progress_callback(company: str, completed: int, total: int):
        db.update_job(
            job_id,
            completed_companies=completed,
            progress=int((completed / total) * 100),
        )

    try:
        result = _analyzer.analyze_companies(
            companies=companies,
            max_workers=max_workers,
            progress_callback=progress_callback,
            model=model,
        )
        batch_response = _convert_batch_result(result)
        db.update_job(
            job_id,
            status="completed",
            progress=100,
            result_json=_json.dumps(batch_response.model_dump(), default=str),
        )
        # Fire webhook notification
        from SPARC.webhooks import notify_job_completed
        notify_job_completed(
            job_id=job_id,
            status="completed",
            total_companies=result.total_companies,
            successful=result.successful,
            failed=result.failed,
        )
    except Exception as e:
        db.update_job(job_id, status="failed", error=str(e))
        from SPARC.webhooks import notify_job_completed
        notify_job_completed(
            job_id=job_id,
            status="failed",
            total_companies=len(companies),
            successful=0,
            failed=len(companies),
        )


@app.post("/analyze/batch/async", response_model=JobStatus, tags=["Analysis"])
async def analyze_companies_async(
    request: BatchAnalysisRequest,
    background_tasks: BackgroundTasks,
    _: UserResponse = Depends(get_current_user),
):
    """Start an asynchronous batch analysis job.

    Returns immediately with a job ID that can be used to poll for status.
    Useful for large batch analyses that may take a long time.

    Args:
        request: List of company names and optional worker count

    Returns:
        Job status with job_id for polling
    """
    global _job_counter

    _job_counter += 1
    job_id = f"job_{_job_counter}_{datetime.now().strftime('%Y%m%d%H%M%S')}"

    db = _get_job_db()
    job_row = db.create_job(job_id=job_id, total_companies=len(request.companies))

    background_tasks.add_task(
        _run_batch_job, job_id, request.companies, request.max_workers, request.model
    )

    return _job_row_to_status(job_row)


@app.get("/jobs/{job_id}", response_model=JobStatus, tags=["Jobs"])
async def get_job_status(
    job_id: str,
    _: UserResponse = Depends(get_current_user),
):
    """Get the status of a background analysis job.

    Args:
        job_id: The job ID returned from the async batch endpoint

    Returns:
        Current job status including progress and results when complete
    """
    db = _get_job_db()
    job_row = db.get_job(job_id)

    if not job_row:
        raise HTTPException(status_code=404, detail=f"Job {job_id} not found")

    return _job_row_to_status(job_row)


@app.get("/jobs", response_model=PaginatedJobsResponse, tags=["Jobs"])
async def list_jobs(
    status: Annotated[
        str | None,
        Query(description="Filter by status: pending, running, completed, failed"),
    ] = None,
    limit: Annotated[int, Query(ge=1, le=100)] = 10,
    cursor: Annotated[
        str | None,
        Query(description="Opaque cursor from a previous response's next_cursor field"),
    ] = None,
    _: UserResponse = Depends(get_current_user),
):
    """List analysis jobs with cursor-based pagination.

    Pass ``limit`` to control page size. The response includes a ``next_cursor``
    field; pass it back as the ``cursor`` query parameter to fetch the next page.
    When ``next_cursor`` is ``null``, there are no more results.

    Existing clients that use only ``limit`` (without ``cursor``) continue to
    work without modification.

    Args:
        status: Optional filter by job status
        limit: Maximum number of jobs to return (default 10, max 100)
        cursor: Opaque pagination cursor from a previous response

    Returns:
        Paginated list of job statuses
    """
    db = _get_job_db()
    # Fetch one extra to determine if there is a next page
    job_rows = db.list_jobs(status=status, limit=limit + 1, cursor=cursor)

    has_next = len(job_rows) > limit
    if has_next:
        job_rows = job_rows[:limit]

    items = [_job_row_to_status(row) for row in job_rows]

    next_cursor = None
    if has_next and job_rows:
        last = job_rows[-1]
        created = last["created_at"]
        ts = created.isoformat() if hasattr(created, "isoformat") else str(created)
        next_cursor = f"{ts}|{last['job_id']}"

    return PaginatedJobsResponse(items=items, next_cursor=next_cursor)