backend/api/analysis_routes.py

"""Ad-hoc analysis endpoints (paste/upload text, PDF)."""

from __future__ import annotations

import logging
import io
import re
import json
from datetime import datetime
from typing import Any, Dict, List, Optional

from fastapi import APIRouter, File, Form, HTTPException, UploadFile
from pydantic import BaseModel, Field

from .conversation_service import run_resource_agent_analysis
from .storage_service import get_run_store, get_persona_store
from config.settings import get_settings
from backend.storage import RunRecord

router = APIRouter(prefix="", tags=["analysis"])
logger = logging.getLogger(__name__)


class ExportMessage(BaseModel):
    role: str
    persona: Optional[str] = None
    time: Optional[str] = None
    text: str


class AnalyzeTextRequest(BaseModel):
    text: str = Field(..., description="Raw transcript text to analyze")
    conversation_id: Optional[str] = Field(default=None, description="Optional client-generated id for this analysis run")
    source_name: Optional[str] = Field(default=None, description="Optional label for the uploaded/pasted source")
    analysis_attributes: Optional[List[str]] = Field(
        default=None,
        description="(Deprecated) analysis attributes are now configured per-pass server-side",
    )
    top_down_codebook_template_id: Optional[str] = Field(
        default=None,
        description="Top-down codebook template id to use for analysis (optional).",
    )


class AnalyzeTextResponse(BaseModel):
    run_id: Optional[str] = None
    persisted: bool = False
    conversation_id: str
    messages: List[ExportMessage]
    resources: Dict[str, Any]


def _parse_transcript_text(text: str, source_name: Optional[str]) -> List[Dict[str, Any]]:
    normalized = (text or "").replace("\r\n", "\n").replace("\r", "\n").strip()
    if not normalized:
        return []

    label = source_name or "Uploaded transcript"
    lines = [line.rstrip() for line in normalized.split("\n")]
    labeled = False
    blocks: List[Dict[str, Any]] = []

    current_role: Optional[str] = None
    current_lines: List[str] = []

    def flush():
        nonlocal current_role, current_lines
        content = "\n".join([l for l in current_lines]).strip()
        if content:
            role = current_role or "transcript"
            persona = "Surveyor" if role == "surveyor" else ("Patient" if role == "patient" else label)
            blocks.append({
                "role": role,
                "persona": persona,
                "content": content,
            })
        current_role = None
        current_lines = []

    pattern = re.compile(r"^(surveyor|interviewer|patient|respondent)\s*:\s*(.*)$", re.IGNORECASE)

    for line in lines:
        stripped = line.strip()
        if not stripped:
            if current_lines:
                current_lines.append("")
            continue

        match = pattern.match(stripped)
        if match:
            labeled = True
            flush()
            speaker = match.group(1).lower()
            current_role = "surveyor" if speaker in ("surveyor", "interviewer") else "patient"
            remainder = match.group(2).strip()
            if remainder:
                current_lines.append(remainder)
            continue

        if current_role is None:
            current_role = "transcript"
        current_lines.append(line)

    flush()

    if labeled:
        return blocks

    paragraphs = [p.strip() for p in re.split(r"\n\s*\n+", normalized) if p.strip()]
    return [{
        "role": "transcript",
        "persona": label,
        "content": p,
    } for p in paragraphs] or [{
        "role": "transcript",
        "persona": label,
        "content": normalized,
    }]


async def _analyze_from_text(
    *,
    text: str,
    conversation_id: str,
    source_name: Optional[str],
    analysis_attributes: Optional[List[str]] = None,
    top_down_codebook_template_id: Optional[str] = None,
) -> AnalyzeTextResponse:
    settings = get_settings()
    exported_at = datetime.now().isoformat()

    parsed_messages = _parse_transcript_text(text, source_name)
    if not parsed_messages:
        raise HTTPException(status_code=400, detail="No content to analyze")

    transcript: List[Dict[str, Any]] = []
    ui_messages: List[ExportMessage] = []
    for idx, msg in enumerate(parsed_messages):
        transcript.append({
            "index": idx,
            "role": msg["role"],
            "persona": msg.get("persona"),
            "content": msg["content"],
            "timestamp": exported_at,
        })
        ui_messages.append(ExportMessage(
            role=msg["role"],
            persona=msg.get("persona"),
            time=exported_at,
            text=msg["content"],
        ))

    store = get_persona_store()
    effective_analysis_system_prompt = await store.get_setting("analysis_system_prompt")
    override = top_down_codebook_template_id.strip() if isinstance(top_down_codebook_template_id, str) else ""
    template_id = await store.get_setting("top_down_codebook_template_id")
    template_id_str = template_id.strip() if isinstance(template_id, str) else ""
    template_record = await store.get_analysis_template(override, include_deleted=False) if override else None
    if not template_record and template_id_str:
        template_record = await store.get_analysis_template(template_id_str, include_deleted=False)
    if not template_record and template_id_str:
        raise HTTPException(status_code=500, detail="Default analysis framework template not found")
    resources = await run_resource_agent_analysis(
        transcript=transcript,
        llm_backend=settings.llm.backend,
        host=settings.llm.host,
        model=settings.llm.model,
        settings=settings,
        analysis_system_prompt=effective_analysis_system_prompt if isinstance(effective_analysis_system_prompt, str) else None,
        bottom_up_instructions=template_record.bottom_up_instructions if template_record else None,
        bottom_up_attributes=template_record.bottom_up_attributes if template_record else None,
        rubric_instructions=template_record.rubric_instructions if template_record else None,
        rubric_attributes=template_record.rubric_attributes if template_record else None,
        top_down_instructions=template_record.top_down_instructions if template_record else None,
        top_down_attributes=template_record.top_down_attributes if template_record else None,
        top_down_template_id=template_record.template_id if template_record else template_id_str,
        top_down_template_version_id=template_record.current_version_id if template_record else "",
        top_down_template_categories=template_record.categories if template_record else [],
    )

    persisted = False
    run_id = None
    try:
        store = get_run_store()
        run_id = conversation_id
        config_snapshot: Dict[str, Any] = {
            "llm": {
                "backend": settings.llm.backend,
                "host": settings.llm.host,
                "model": settings.llm.model,
                "timeout": settings.llm.timeout,
                "max_retries": settings.llm.max_retries,
                "retry_delay": settings.llm.retry_delay,
            },
            "text_analysis": {
                "source_name": source_name,
            },
            "analysis": {
                "analysis_system_prompt": effective_analysis_system_prompt if isinstance(effective_analysis_system_prompt, str) else None,
                "bottom_up_instructions": template_record.bottom_up_instructions if template_record else None,
                "bottom_up_attributes": template_record.bottom_up_attributes if template_record else None,
                "rubric_instructions": template_record.rubric_instructions if template_record else None,
                "rubric_attributes": template_record.rubric_attributes if template_record else None,
                "top_down_instructions": template_record.top_down_instructions if template_record else None,
                "top_down_attributes": template_record.top_down_attributes if template_record else None,
                "top_down_codebook_template_id": template_record.template_id if template_record else template_id_str,
                "top_down_codebook_template_version_id": template_record.current_version_id if template_record else "",
                "top_down_codebook_template_snapshot": template_record.categories if template_record else [],
            },
        }
        record = RunRecord(
            run_id=run_id,
            mode="text_analysis",
            status="completed",
            created_at=exported_at,
            ended_at=exported_at,
            sealed_at=exported_at,
            title=None,
            input_summary=source_name,
            config=config_snapshot,
            messages=transcript,
            analyses={"resource_agent_v2": resources},
            persona_snapshots={},
        )
        await store.save_sealed_run(record)
        persisted = True
    except Exception as e:
        logger.error(f"Failed to persist sealed text analysis {conversation_id}: {e}")
        persisted = False
        run_id = None

    return AnalyzeTextResponse(
        run_id=run_id,
        persisted=persisted,
        conversation_id=conversation_id,
        messages=ui_messages,
        resources=resources,
    )


@router.post("/analyze/text")
async def analyze_text(payload: AnalyzeTextRequest) -> AnalyzeTextResponse:
    if not isinstance(payload.text, str) or not payload.text.strip():
        raise HTTPException(status_code=400, detail="text is required")

    conversation_id = payload.conversation_id or f"analysis_{int(datetime.now().timestamp())}"
    return await _analyze_from_text(
        text=payload.text,
        conversation_id=conversation_id,
        source_name=payload.source_name,
        analysis_attributes=payload.analysis_attributes,
        top_down_codebook_template_id=payload.top_down_codebook_template_id,
    )


@router.post("/analyze/file")
async def analyze_file(
    file: UploadFile = File(...),
    conversation_id: Optional[str] = Form(default=None),
    source_name: Optional[str] = Form(default=None),
    analysis_attributes_json: Optional[str] = Form(default=None),
    top_down_codebook_template_id: Optional[str] = Form(default=None),
) -> AnalyzeTextResponse:
    data = await file.read()
    if not data:
        raise HTTPException(status_code=400, detail="Empty file")

    inferred_name = source_name or file.filename or "Uploaded file"
    cid = conversation_id or f"analysis_{int(datetime.now().timestamp())}"
    analysis_attributes: Optional[List[str]] = None
    if isinstance(analysis_attributes_json, str) and analysis_attributes_json.strip():
        try:
            parsed = json.loads(analysis_attributes_json)
            if isinstance(parsed, list):
                analysis_attributes = [str(x).strip() for x in parsed if isinstance(x, str) and str(x).strip()]
        except Exception:
            analysis_attributes = None

    filename = (file.filename or "").lower()
    content_type = (file.content_type or "").lower()

    is_pdf = filename.endswith(".pdf") or content_type == "application/pdf"
    if is_pdf:
        try:
            from pypdf import PdfReader  # type: ignore
        except Exception as e:
            raise HTTPException(status_code=500, detail=f"pypdf not available: {e}")

        try:
            reader = PdfReader(io.BytesIO(data))
            chunks: List[str] = []
            for page in reader.pages:
                page_text = (page.extract_text() or "").strip()
                if page_text:
                    chunks.append(page_text)
            extracted = "\n\n".join(chunks).strip()
        except Exception as e:
            raise HTTPException(status_code=400, detail=f"Failed to parse PDF: {e}")

        if not extracted:
            raise HTTPException(status_code=400, detail="No extractable text found in PDF")

        return await _analyze_from_text(
            text=extracted,
            conversation_id=cid,
            source_name=inferred_name,
            analysis_attributes=analysis_attributes,
            top_down_codebook_template_id=top_down_codebook_template_id,
        )

    decoded = data.decode("utf-8", errors="replace").strip()
    if not decoded:
        raise HTTPException(status_code=400, detail="No text content found in file")

    return await _analyze_from_text(
        text=decoded,
        conversation_id=cid,
        source_name=inferred_name,
        analysis_attributes=analysis_attributes,
        top_down_codebook_template_id=top_down_codebook_template_id,
    )