"""
FastAPI backend — optional REST API alongside Gradio UI.
Run: uvicorn main:app --host 0.0.0.0 --port 8000 --reload
"""

import os
import uuid
import io
from dotenv import load_dotenv
load_dotenv()

import pandas as pd
from fastapi import FastAPI, UploadFile, File, HTTPException
from fastapi.middleware.cors import CORSMiddleware

from app.models.schemas import EvaluationRequest, EvaluationResponse, Candidate
from app.services.evaluation_service import perform_hybrid_evaluation

app = FastAPI(
    title="AI Recruitment Engine",
    description="Hybrid 5-stage candidate evaluation pipeline",
    version="1.0.0",
)

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_methods=["*"],
    allow_headers=["*"],
)

# Simple in-memory cache (use Redis/DB in production)
_cache: dict = {}


@app.get("/health")
async def health():
    return {"status": "ok", "service": "AI Recruitment Engine"}


@app.post("/upload-csv")
async def upload_csv(file: UploadFile = File(...)):
    if not (file.filename or "").endswith(".csv"):
        raise HTTPException(status_code=400, detail="Please upload a .csv file.")
    try:
        content = await file.read()
        df = pd.read_csv(io.BytesIO(content)).fillna("")
        candidates = []
        for _, row in df.iterrows():
            candidates.append(Candidate(
                id=str(uuid.uuid4()),
                name=str(row.get("name", "Unknown")),
                email=str(row.get("email", "")),
                skills=str(row.get("skills", row.get("parsed_skills", ""))),
                experience=str(row.get("experience", row.get("parsed_work_experience", ""))),
                projects=str(row.get("projects", "")),
                education=str(row.get("education", row.get("parsed_metadata_education", ""))),
                resume_text=str(row.get("resume_text", row.get("parsed_summary", ""))),
            ))
        return {"count": len(candidates), "candidates": candidates}
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"CSV parse error: {e}")


@app.post("/evaluate", response_model=EvaluationResponse)
async def evaluate(request: EvaluationRequest):
    if not request.jd:
        raise HTTPException(status_code=400, detail="Job Description is required.")
    if not request.candidates:
        raise HTTPException(status_code=400, detail="At least one candidate is required.")

    response = await perform_hybrid_evaluation(request.jd, request.candidates)

    for rank in response.shortlist:
        _cache[rank.candidate_id] = rank.model_dump()
    _cache.update(response.details)

    return response


@app.get("/candidate/{candidate_id}")
async def get_candidate(candidate_id: str):
    if candidate_id not in _cache:
        raise HTTPException(status_code=404, detail="Candidate report not found.")
    return _cache[candidate_id]


if __name__ == "__main__":
    import uvicorn
    uvicorn.run(
        app,
        host=os.getenv("APP_HOST", "0.0.0.0"),
        port=int(os.getenv("APP_PORT", "8000")),
    )