app/api/v1/csv_analysis.py

from __future__ import annotations

import json
from typing import Annotated, Any, Dict, List, Optional

from fastapi import APIRouter, File, Form, HTTPException, Request, UploadFile
from pydantic import BaseModel, ValidationError

from app.api.deps import get_redis_scripts
from app.config import get_settings
from app.services.chat_service import chat_completion
from app.services.csv_analysis_service import (
    execute_csv_chat_blocks,
    get_dataset_info,
)
from app.services.prompts import get_csv_system_prompt
from app.utils.json_utils import extract_json_blocks


class _AnalyzeBlock(BaseModel):
    description: str = ""
    python_code: str = ""


class _VisualizationBlock(BaseModel):
    description: str = ""
    python_code: str = ""


class _AIResponse(BaseModel):
    analyze: List[_AnalyzeBlock] = []
    visualization: List[_VisualizationBlock] = []
    message: str = ""

router = APIRouter()
_settings = get_settings()
_MAX_UPLOAD_BYTES = _settings.max_upload_bytes


@router.post(
    "/csv/info",
    summary="Get metadata for up to 10 CSV files (upload or URL)",
)
async def get_csv_info(
    files: Annotated[Optional[List[UploadFile]], File(description="CSV files to inspect (max 10 total with URLs)")] = None,
    urls: Annotated[Optional[str], Form(description="JSON array of file URLs (max 10 total with files)")] = None,
):
    parsed_urls: List[str] = []
    if urls:
        try:
            parsed_urls = json.loads(urls)
            if not isinstance(parsed_urls, list) or not all(isinstance(u, str) for u in parsed_urls):
                raise ValueError("urls must be a JSON array of strings")
        except (json.JSONDecodeError, ValueError) as exc:
            raise HTTPException(status_code=400, detail=str(exc))

    file_count = len(files) if files else 0
    url_count = len(parsed_urls)
    total = file_count + url_count

    if total == 0:
        raise HTTPException(status_code=400, detail="Provide at least one file or URL")
    if total > 10:
        raise HTTPException(status_code=400, detail=f"Maximum 10 sources allowed (got {total})")

    results: List[dict] = []

    if files:
        for f in files:
            try:
                data = await f.read()
            except Exception as exc:
                results.append({"source": getattr(f, "filename", "unknown"), "success": False, "error": f"Read error: {exc}"})
                continue

            if len(data) > _MAX_UPLOAD_BYTES:
                results.append({"source": f.filename or "unknown", "success": False, "error": f"File exceeds {_settings.max_upload_mb} MB limit"})
                continue

            if not data:
                results.append({"source": f.filename or "unknown", "success": False, "error": "Empty file"})
                continue

            try:
                meta = await get_dataset_info(data)
                meta["source"] = f.filename or "upload"
                results.append(meta)
            except Exception as exc:
                results.append({"source": f.filename or "upload", "success": False, "error": str(exc)})

    for url in parsed_urls:
        if not url.startswith(("http://", "https://")):
            results.append({"source": url, "success": False, "error": "Only http/https URLs are supported"})
            continue

        try:
            meta = await get_dataset_info(url)
            meta["source"] = url
            results.append(meta)
        except Exception as exc:
            results.append({"source": url, "success": False, "error": str(exc)})

    return {
        "success": True,
        "total": total,
        "succeeded": sum(1 for r in results if r.get("success")),
        "failed": sum(1 for r in results if not r.get("success")),
        "results": results,
    }


# @router.post(
#     "/csv/analyze",
#     summary="Execute Python analysis code against a CSV file (upload or URL)",
# )
# async def analyze_csv(
#     file: Annotated[Optional[UploadFile], File(description="CSV file to analyze")] = None,
#     url: Annotated[Optional[str], Form(description="URL to a CSV file")] = None,
#     code: str = Form(..., description="Python code to execute (df pre-loaded with CSV data)"),
#     token: str = Depends(require_auth),
# ):
#     if not file and not url:
#         raise HTTPException(status_code=400, detail="Provide either a file or a URL")

#     if file and url:
#         raise HTTPException(status_code=400, detail="Provide either a file or a URL, not both")

#     if file:
#         data = await file.read()
#         if len(data) > _MAX_UPLOAD_BYTES:
#             raise HTTPException(status_code=413, detail=f"File exceeds {_settings.max_upload_mb} MB limit")
#         if not data:
#             raise HTTPException(status_code=400, detail="Empty file")
#         result = await analyze_csv_dataset(data, code)
#     else:
#         result = await analyze_csv_dataset(url, code)

#     return result


# @router.post(
#     "/csv/chart",
#     summary="Generate a chart from CSV data and return as base64 PNG (upload or URL)",
# )
# async def chart_csv(
#     file: Annotated[Optional[UploadFile], File(description="CSV file for chart generation")] = None,
#     url: Annotated[Optional[str], Form(description="URL to a CSV file")] = None,
#     code: str = Form(..., description="Python chart code (df pre-loaded, use matplotlib/seaborn)"),
#     token: str = Depends(require_auth),
# ):
#     if not file and not url:
#         raise HTTPException(status_code=400, detail="Provide either a file or a URL")

#     if file and url:
#         raise HTTPException(status_code=400, detail="Provide either a file or a URL, not both")

#     if file:
#         data = await file.read()
#         if len(data) > _MAX_UPLOAD_BYTES:
#             raise HTTPException(status_code=413, detail=f"File exceeds {_settings.max_upload_mb} MB limit")
#         if not data:
#             raise HTTPException(status_code=400, detail="Empty file")
#         result = await create_csv_chart(data, code)
#     else:
#         result = await create_csv_chart(url, code)

#     return result


@router.post(
    "/csv/chat",
    summary="Chat with AI about a CSV file — returns analysis + chart code results",
)
async def csv_chat(
    request: Request,
    file: Annotated[Optional[UploadFile], File(description="CSV file to analyze")] = None,
):
    content_type = request.headers.get("content-type", "")

    url: Optional[str] = None
    query: Optional[str] = None

    if "application/json" in content_type:
        try:
            body = await request.json()
            url = body.get("url")
            query = body.get("query")
        except Exception as exc:
            raise HTTPException(status_code=400, detail=f"Invalid JSON body: {exc}")
    else:
        form = await request.form()
        url = form.get("url")
        query = form.get("query")

    has_file = file is not None
    has_url = bool(url)

    if not has_file and not has_url:
        raise HTTPException(status_code=400, detail="Provide either a file or a URL")
    if has_file and has_url:
        raise HTTPException(status_code=400, detail="Provide either a file or a URL, not both")

    if has_file:
        data = await file.read()
        if len(data) > _MAX_UPLOAD_BYTES:
            raise HTTPException(status_code=413, detail=f"File exceeds {_settings.max_upload_mb} MB limit")
        if not data:
            raise HTTPException(status_code=400, detail="Empty file")
        source: Any = data
    else:
        source = url

    if not query:
        raise HTTPException(status_code=400, detail="Query is required")

    metadata = await get_dataset_info(source)
    system_prompt = get_csv_system_prompt(metadata)

    messages = [
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": query},
    ]

    redis, scripts = get_redis_scripts(request)

    try:
        ai_response = await chat_completion(
            messages=messages,
            response_format={"type": "json_object"},
            max_tokens=12000,
            redis=redis,
            scripts=scripts,
        )
    except RuntimeError as e:
        raise HTTPException(status_code=502, detail=str(e))

    parsed = ai_response.get("parsed")
    if not parsed:
        choices = ai_response.get("choices", [])
        content = choices[0].get("message", {}).get("content", "") if choices else ""
        blocks = extract_json_blocks(content)
        if blocks:
            parsed = blocks[0]
        else:
            try:
                parsed = json.loads(content)
            except (json.JSONDecodeError, TypeError):
                pass

    if not isinstance(parsed, dict):
        return {
            "success": False,
            "message": None,
            "analyze": [],
            "visualizations": [],
            "error": "AI response was not valid JSON",
        }

    try:
        ai_data = _AIResponse(**parsed)
    except ValidationError as exc:
        return {
            "success": False,
            "message": None,
            "analyze": [],
            "visualizations": [],
            "error": f"AI response failed schema validation: {exc}",
        }

    message_text = ai_data.message
    has_content = bool(message_text.strip()) if message_text else False

    analyze_blocks_raw = [b.model_dump() for b in ai_data.analyze]
    viz_blocks_raw = [b.model_dump() for b in ai_data.visualization]

    exec_result = await execute_csv_chat_blocks(
        source=source,
        analyze_blocks=analyze_blocks_raw,
        viz_blocks=viz_blocks_raw,
    )

    if not exec_result["success"]:
        return {
            "success": False,
            "message": ai_data.message if has_content else None,
            "analyze": [],
            "visualizations": [],
            "error": exec_result.get("error", "Code execution failed"),
        }

    results = exec_result.get("results", {})
    raw_analyze = results.get("analyze", [])
    raw_visualizations = results.get("visualization", [])

    analyze_results: List[Dict[str, Any]] = []
    for i, block in enumerate(ai_data.analyze):
        raw = raw_analyze[i] if i < len(raw_analyze) else {}
        code = block.python_code.strip()
        if not code:
            continue
        analyze_results.append({
            "description": block.description,
            "code": code,
            "success": raw.get("success", False),
            "output": raw.get("output", ""),
            "error": raw.get("error"),
            "execution_time_ms": exec_result["execution_time_ms"],
        })

    viz_results: List[Dict[str, Any]] = []
    for i, block in enumerate(ai_data.visualization):
        raw = raw_visualizations[i] if i < len(raw_visualizations) else {}
        code = block.python_code.strip()
        if not code:
            continue
        viz_results.append({
            "description": block.description,
            "code": code,
            "success": raw.get("success", False),
            "image_base64": raw.get("image_base64"),
            "error": raw.get("error"),
            "execution_time_ms": exec_result["execution_time_ms"],
        })

    return {
        "success": True,
        "message": ai_data.message if has_content else None,
        "analyze": analyze_results,
        "visualizations": viz_results,
        "error": None,
    }