"""
app.py — PawMap
Build Small Hackathon · Backyard AI Track · Junho 2026
Custom frontend via gradio.Server
"""
import json
import logging
import os
import tempfile
import time
import uuid
from pathlib import Path

from gradio import Server
from gradio.data_classes import FileData
from fastapi.responses import HTMLResponse, JSONResponse
from fastapi import Query
from fastapi.staticfiles import StaticFiles

from core.ai import AnimalAI
from core.database import Database, DATA_DIR, PHOTOS_DIR
from core.matcher import AnimalMatcher
from core.seed import seed_if_empty
from core.tracer import log_trace

logging.basicConfig(level=logging.INFO)
db      = Database()
ai      = AnimalAI()
matcher = AnimalMatcher()
seed_if_empty(db)   # popula o mapa com dados de demo se o banco estiver vazio


def _photo_url(photo_path: str) -> str:
    """Convert DB-relative photo path to a URL served by the /photos/ static mount.
    photo_path is relative to DATA_DIR (e.g. 'photos/animal_42/abc.jpg').
    The static mount serves PHOTOS_DIR at /photos/, so we strip the 'photos/' prefix.
    """
    if not photo_path:
        return ""
    # Normalise separators
    p = photo_path.replace("\\", "/")
    if p.startswith("photos/"):
        p = p[len("photos/"):]
    return f"/photos/{p}"

# In-memory session store for analyze → confirm two-step flow
_pending: dict[str, dict] = {}

app = Server()

# Serve photos as static files at /photos/...
PHOTOS_DIR.mkdir(parents=True, exist_ok=True)
app.mount("/photos", StaticFiles(directory=str(PHOTOS_DIR)), name="photos")

# Serve frontend assets (CSS, JS, images) at /static/...
STATIC_DIR = Path(__file__).parent / "static"
STATIC_DIR.mkdir(exist_ok=True)
app.mount("/static", StaticFiles(directory=str(STATIC_DIR)), name="static")


# ─── Frontend ─────────────────────────────────────────────────────────────────

@app.get("/", response_class=HTMLResponse)
async def homepage():
    html_path = Path(__file__).parent / "index.html"
    return html_path.read_text(encoding="utf-8")


# ─── Data APIs (FastAPI routes, no queuing needed) ────────────────────────────

@app.get("/api/map-data")
async def get_map_data(
    species: str = Query("all"),
    timeframe: str = Query("all"),
):
    data = db.get_map_data(species, timeframe)
    for item in data:
        item["photo_url"] = _photo_url(item.pop("last_photo", "") or "")
    return JSONResponse(content=data)


@app.get("/api/animals")
async def get_animals():
    animals = db.get_recent_animals(limit=30)
    for a in animals:
        a["photo_url"] = _photo_url(a.pop("last_photo_path", "") or "")
        a.pop("embedding", None)
    return JSONResponse(content=animals)


@app.get("/api/animal/{animal_id}")
async def get_animal(animal_id: int):
    detail = db.get_animal_detail(animal_id)
    if not detail:
        return JSONResponse(content={"error": "not found"}, status_code=404)
    for s in detail.get("sightings", []):
        s["photo_url"] = _photo_url(s.get("photo_path") or "")
    for h in detail.get("help_events", []):
        h["photo_url"] = _photo_url(h.get("photo_path") or "")
    detail.get("animal", {}).pop("embedding", None)
    return JSONResponse(content=detail)


# ─── ML APIs (queued via Gradio) ──────────────────────────────────────────────

@app.api(name="analyze_image")
def analyze_image(image_path: FileData) -> dict:
    """
    Step 1: Analyze photo with AI, find similar animals.
    Returns session_id + AI description + top matches (no DB write yet).
    """
    from PIL import Image as PILImage

    img = PILImage.open(image_path["path"]).convert("RGB")

    description = ai.analyze_image(img)

    # Rejeição: a IA não detectou nenhum animal na foto
    if description.get("is_animal") is False:
        return {
            "error": "Nenhum cão ou gato identificado na foto. Por favor, fotografe um animal de rua.",
            "session_id": "",
            "description": {},
            "similar": [],
        }

    embedding   = ai.get_embedding(description)
    candidates  = db.get_all_animals_with_embeddings()
    top_matches = matcher.find_top_matches(embedding, candidates, top_n=3)

    # Enrich matches with photo URLs and sighting info
    similar = []
    for m in top_matches:
        sightings  = db.get_animal_sightings(m["id"])
        photo_path = next(
            (s["photo_path"] for s in sightings if s.get("photo_path")), None
        )
        latest = sightings[0] if sightings else {}
        similar.append({
            "id":        m["id"],
            "score_pct": round(m["score"] * 100),
            "photo_url": _photo_url(photo_path) if photo_path else "",
            "days_ago":  latest.get("days_ago", ""),
        })

    # Save image to temp file for the confirm step
    tmp = tempfile.NamedTemporaryFile(suffix=".jpg", delete=False, dir=DATA_DIR)
    img.save(tmp.name, format="JPEG", quality=85)
    tmp.close()

    session_id = uuid.uuid4().hex
    _pending[session_id] = {
        "temp_path":   tmp.name,
        "description": description,
        "embedding":   embedding,
        "timestamp":   time.time(),
    }
    _cleanup_sessions()

    log_trace({
        "event":       "analyze",
        "session_id":  session_id,
        "description": {k: v for k, v in description.items() if k not in ("_ai_success",)},
        "top_matches": [{"id": m["id"], "score_pct": m["score_pct"]} for m in similar],
    })

    return {
        "session_id":  session_id,
        "description": description,
        "similar":     similar,
    }


@app.api(name="confirm_sighting")
def confirm_sighting(
    session_id: str,
    gps_json:   str = "",
    notes:      str = "",
    condition:  str = "",
    animal_name: str = "",
) -> dict:
    """
    Step 2: User reviewed/edited the AI results → save sighting to DB.
    """
    import datetime
    from PIL import Image as PILImage

    session = _pending.pop(session_id, None)
    if not session:
        return {"error": "Sessão expirada. Tire a foto novamente."}

    img         = PILImage.open(session["temp_path"]).convert("RGB")
    description = session["description"]
    embedding   = session["embedding"]

    # Clean up temp file
    try:
        os.unlink(session["temp_path"])
    except Exception:
        pass

    # Parse GPS
    try:
        coords = json.loads(gps_json) if gps_json and gps_json.strip() else {}
    except Exception:
        coords = {}
    lat = round(float(coords["lat"]), 5) if coords.get("lat") else None
    lng = round(float(coords["lng"]), 5) if coords.get("lng") else None

    # Append condition to notes
    full_notes = notes
    if condition:
        full_notes = (notes + f" [Condição: {condition}]").strip()

    candidates = db.get_all_animals_with_embeddings()
    match      = matcher.find_match(embedding, candidates)

    clean_name = animal_name.strip() or None

    if match:
        animal_id, _ = match
        photo_path   = db.save_photo(img, animal_id=animal_id)
        db.add_sighting(animal_id, photo_path, lat, lng, full_notes)
        db.update_animal(animal_id)
        if clean_name:
            db.update_animal_name(animal_id, clean_name)
        animal  = db.get_animal(animal_id)
        count   = animal["sighting_count"]
        species = animal["species"]
        desc_obj = json.loads(animal.get("description") or "{}")
        is_new  = False
    else:
        animal_id  = db.create_animal(description, embedding, name=clean_name)
        photo_path = db.save_photo(img, animal_id=animal_id)
        db.add_sighting(animal_id, photo_path, lat, lng, full_notes)
        count    = 1
        species  = description.get("species", "dog")
        desc_obj = description
        is_new   = True

    # Display name: user-given > AI-generated fallback
    animal_row = db.get_animal(animal_id)
    saved_name = animal_row.get("name") if animal_row else None
    breed  = desc_obj.get("breed_estimate", "")
    color  = desc_obj.get("primary_color", "")
    name   = saved_name or " ".join(filter(None, [
        "Dog" if species == "dog" else "Cat",
        color.capitalize() if color else "",
        breed if breed and breed.lower() not in ("srd", "unknown", "") else "",
    ])).strip() or ("Dog" if species == "dog" else "Cat")

    result = {
        "animal_id": animal_id,
        "is_new":    is_new,
        "count":     count,
        "species":   species,
        "name":      name,
        "photo_url": _photo_url(photo_path) if photo_path else "",
        "location":  f"Lat {lat:.4f}, Lng {lng:.4f}" if lat and lng else "Localização não registrada",
        "time":      datetime.datetime.now().strftime("%H:%M"),
    }

    log_trace({
        "event":       "confirm",
        "session_id":  session_id,
        "animal_id":   animal_id,
        "is_new":      is_new,
        "species":     species,
        "sighting_count": count,
        "gps":         {"lat": lat, "lng": lng},
        "description": desc_obj,
    })

    return result


def _cleanup_sessions():
    cutoff = time.time() - 1800  # 30 min
    for k in list(_pending.keys()):
        if _pending[k]["timestamp"] < cutoff:
            try:
                os.unlink(_pending[k]["temp_path"])
            except Exception:
                pass
            _pending.pop(k, None)


# ─── Help ─────────────────────────────────────────────

@app.post("/api/animal/{animal_id}/helped")
async def mark_helped(animal_id: int):
    """Legacy — mantido por compatibilidade. Prefira submit_help_proof."""
    animal = db.get_animal(animal_id)
    if not animal:
        return JSONResponse(content={"error": "not found"}, status_code=404)
    db.add_sighting(animal_id, None, None, None, "", is_help_event=True, help_type="other")
    db.update_animal(animal_id)
    return JSONResponse(content={"ok": True})


@app.api(name="submit_help_proof")
def submit_help_proof(
    animal_id: int,
    help_type: str = "other",
    notes: str = "",
    image_path: FileData = None,
) -> dict:
    """
    Registra que alguém ajudou o animal, com foto de prova opcional.
    A IA verifica se a foto é do mesmo animal e detecta melhora de condição.
    """
    from PIL import Image as PILImage
    import json as _json

    photo_path = None
    ai_verified = False
    condition_update = None
    match_score = None

    if image_path and image_path.get("path"):
        img = PILImage.open(image_path["path"]).convert("RGB")

        # Analisa a foto com IA
        description = ai.analyze_image(img)

        if description.get("is_animal") is not False and description.get("_ai_success"):
            embedding = ai.get_embedding(description)
            candidates = db.get_all_animals_with_embeddings()

            # Verifica se é o mesmo animal
            match = matcher.find_match(embedding, candidates)
            if match:
                matched_id, score = match
                ai_verified = (matched_id == animal_id)
                match_score = round(score * 100)

            # Detecta melhora de condição
            animal_data = db.get_animal(animal_id)
            if animal_data:
                prev_desc = _json.loads(animal_data.get("description") or "{}")
                prev_condition = prev_desc.get("condition", "")
                new_condition = description.get("condition", "")
                condition_rank = {"injured": 0, "thin": 1, "healthy": 2}
                if (condition_rank.get(new_condition, -1) >
                        condition_rank.get(prev_condition, -1)):
                    condition_update = new_condition

        photo_path = db.save_photo(img, animal_id=animal_id)

    db.add_sighting(
        animal_id,
        photo_path,
        None, None,
        notes,
        is_help_event=True,
        help_type=help_type,
    )
    db.update_animal(animal_id)

    log_trace({
        "event":            "help_proof",
        "animal_id":        animal_id,
        "help_type":        help_type,
        "has_photo":        photo_path is not None,
        "ai_verified":      ai_verified,
        "match_score":      match_score,
        "condition_update": condition_update,
    })

    return {
        "ok":               True,
        "animal_id":        animal_id,
        "help_type":        help_type,
        "ai_verified":      ai_verified,
        "match_score":      match_score,
        "condition_update": condition_update,
        "photo_url":        _photo_url(photo_path) if photo_path else "",
    }


# ─── Admin ────────────────────────────────────────────

@app.get("/admin/push-traces")
async def push_traces():
    """Publica data/traces.jsonl como dataset no HF Hub.
    Acesse esta URL no browser para disparar o upload.
    Requer HF_TOKEN e HF_DATASET_ID nos Secrets do Space.
    """
    from core.tracer import push_to_hub, TRACES_PATH
    if not TRACES_PATH.exists():
        return JSONResponse(content={"ok": False, "error": "Nenhum trace encontrado ainda."})
    lines = TRACES_PATH.read_text().strip().splitlines()
    push_to_hub()
    return JSONResponse(content={"ok": True, "traces_published": len(lines)})


# ─── Launch ────────────────────────────────────────────

if __name__ == "__main__":
    DATA_DIR.mkdir(parents=True, exist_ok=True)
    PHOTOS_DIR.mkdir(parents=True, exist_ok=True)
    app.launch(
        server_name="0.0.0.0",
        server_port=int(os.environ.get("PORT", 7860)),
        show_error=True,
    )