modcomms/backend/app/services/analysis_service.py

import logging
from typing import Callable, Awaitable, List, Tuple, Optional

from app.models.schemas import SubReview, AgentReview, OverallStatus

logger = logging.getLogger(__name__)
from app.agents.brand_agent import BrandAgent
from app.agents.channel_best_practices_agent import ChannelBestPracticesAgent
from app.agents.channel_tech_specs_agent import ChannelTechSpecsAgent
from app.agents.legal_agent import LegalAgent
from app.agents.lead_agent import LeadAgent
from app.services.gemini_service import GeminiService
from app.services.reference_docs import ReferenceDocsService
from app.services.pdf_service import pdf_service


# Type alias for the callback function
AgentCallback = Callable[[str, SubReview | None], Awaitable[None]]


class AnalysisService:
    """
    Orchestrates the multi-agent proof analysis.

    Runs agents sequentially and provides callbacks for real-time updates.
    """

    # Agent execution order
    AGENT_ORDER = ["Legal Agent", "Brand Agent", "Channel Best Practices Agent", "Channel Tech Specs Agent"]

    def __init__(
        self,
        gemini_service: GeminiService,
        reference_docs: ReferenceDocsService,
    ):
        """
        Initialize the analysis service with all required agents.

        Args:
            gemini_service: Service for Gemini API calls
            reference_docs: Service for loading reference documents
        """
        self.gemini_service = gemini_service
        self.reference_docs = reference_docs

        # Initialize agents
        self.agents = {
            "Legal Agent": LegalAgent(gemini_service, reference_docs),
            "Brand Agent": BrandAgent(gemini_service, reference_docs),
            "Channel Best Practices Agent": ChannelBestPracticesAgent(gemini_service, reference_docs),
            "Channel Tech Specs Agent": ChannelTechSpecsAgent(gemini_service, reference_docs),
        }
        self.lead_agent = LeadAgent(gemini_service)

    async def analyze_proof(
        self,
        file_data: bytes,
        file_type: str,
        on_agent_update: AgentCallback | None = None,
        is_wip: bool = False,
        brand: str = "Barclaycard",
    ) -> Tuple[AgentReview, Optional[List[Tuple[bytes, int, int]]]]:
        """
        Analyze a proof using all agents sequentially.

        Args:
            file_data: Raw bytes of the file to analyze
            file_type: MIME type of the file
            on_agent_update: Optional callback for real-time agent updates.
                           Called with (agent_name, None) when agent starts,
                           and (agent_name, review) when agent completes.
            is_wip: Whether this is a work-in-progress analysis
            brand: Brand to use for brand guidelines analysis ('Barclays' or 'Barclaycard')

        Returns:
            Tuple of:
            - Complete AgentReview with all agent results and overall verdict
            - List of rasterized PDF pages if input was PDF, else None
              Each page is (png_bytes, width, height)
        """
        logger.info(f"[ANALYSIS] Starting proof analysis - file_type: {file_type}, file_size: {len(file_data)} bytes, is_wip: {is_wip}, brand: {brand}")
        reviews: dict[str, SubReview] = {}

        # Prepare images for analysis
        pdf_pages: Optional[List[Tuple[bytes, int, int]]] = None
        images: List[Tuple[bytes, str]] = []

        if file_type == "application/pdf":
            # Rasterize PDF to PNG images
            logger.info("[ANALYSIS] Detected PDF, rasterizing pages...")
            try:
                pdf_pages = pdf_service.rasterize(file_data, max_pages=10)
                images = [(png_data, "image/png") for png_data, _, _ in pdf_pages]
                logger.info(f"[ANALYSIS] Rasterized {len(images)} PDF pages")
            except ValueError as e:
                logger.error(f"[ANALYSIS] PDF rasterization failed: {str(e)}")
                # Return error review if PDF cannot be processed
                error_review = SubReview(
                    ragStatus="Error",
                    feedback=f"Failed to process PDF: {str(e)}",
                    issues=[]
                )
                return AgentReview(
                    legalAgentReview=error_review,
                    brandAgentReview=error_review,
                    channelBestPracticesAgentReview=error_review,
                    channelTechSpecsAgentReview=error_review,
                    leadAgentSummary=f"Analysis could not proceed due to PDF processing error: {str(e)}",
                    overallStatus="Analysis Error",
                    financialPromotionReason=None,
                ), None
        else:
            # Single image/video - wrap in list
            images = [(file_data, file_type)]

        # Run each agent sequentially
        for agent_name in self.AGENT_ORDER:
            agent = self.agents[agent_name]

            logger.info(f"[ANALYSIS] Starting agent: {agent_name}")

            # Notify that agent is starting
            if on_agent_update:
                await on_agent_update(agent_name, None)

            # Run the agent with images list
            # Pass brand to Brand Agent for selecting appropriate guidelines
            if agent_name == "Brand Agent":
                review = await agent.analyze(images, brand=brand)
            else:
                review = await agent.analyze(images)
            reviews[agent_name] = review

            logger.info(f"[ANALYSIS] Agent completed: {agent_name} - ragStatus: {review.ragStatus}")

            # Notify that agent completed
            if on_agent_update:
                await on_agent_update(agent_name, review)

        # Get lead agent synthesis
        logger.info("[ANALYSIS] Starting lead agent synthesis")
        if on_agent_update:
            await on_agent_update("Summary", None)

        overall_status, summary, financial_promotion_reason = await self.lead_agent.synthesize(reviews)
        logger.info(f"[ANALYSIS] Analysis complete - overallStatus: {overall_status}")

        # Build the complete AgentReview
        return AgentReview(
            legalAgentReview=reviews["Legal Agent"],
            brandAgentReview=reviews["Brand Agent"],
            channelBestPracticesAgentReview=reviews["Channel Best Practices Agent"],
            channelTechSpecsAgentReview=reviews["Channel Tech Specs Agent"],
            leadAgentSummary=summary,
            overallStatus=overall_status,
            financialPromotionReason=financial_promotion_reason,
        ), pdf_pages