package api

import (
	"bytes"
	"context"
	"encoding/json"
	"errors"
	"fmt"
	"io"
	"log/slog"
	"mime/multipart"
	"net/http"
	"net/textproto"
	"path"
	"strings"
	"sync"
	"time"

	"github.com/sandrodach/mindsift/internal/auth"
)

// Self-hosted speech recognition: the browser records audio and sends it here, MindSift passes it on
// to an OpenAI-compatible Whisper server (STT_URL, e.g. speaches) and returns the text. Nothing is
// stored. A limited number of transcriptions run at the same time (STT_MAX_PARALLEL), the others
// wait in line; each user has an hourly budget (STT_PER_USER_PER_HOUR). Problems are logged, the
// browser only gets a short message.

type voiceService struct {
	slots chan struct{}

	mu      sync.Mutex
	usage   map[string][]time.Time // userID -> transcriptions within the last hour
	status  voiceStatus
	checked time.Time
}

// voiceStatus is what the admin page shows about the Whisper server.
type voiceStatus struct {
	Reachable  bool   `json:"reachable"`
	ModelReady bool   `json:"modelReady"`
	Error      string `json:"error,omitempty"`
	CheckedAt  string `json:"checkedAt,omitempty"`
}

func newVoiceService(parallel int) *voiceService {
	if parallel < 1 {
		parallel = 1
	}
	return &voiceService{slots: make(chan struct{}, parallel), usage: map[string][]time.Time{}}
}

func (s *Server) voiceMode() string {
	return s.cfg.Speech.Mode(s.cfg.DemoMode)
}

// allowVoice counts one transcription for the user; false when the hourly budget is used up.
func (v *voiceService) allowVoice(userID string, perHour int, now time.Time) bool {
	v.mu.Lock()
	defer v.mu.Unlock()
	recent := v.usage[userID][:0]
	for _, t := range v.usage[userID] {
		if now.Sub(t) < time.Hour {
			recent = append(recent, t)
		}
	}
	if len(recent) >= perHour {
		v.usage[userID] = recent
		return false
	}
	v.usage[userID] = append(recent, now)
	return true
}

// sttEndpoint joins the base URL with an API path; a base URL ending in /v1 works as well.
func (s *Server) sttEndpoint(p string) string {
	base := strings.TrimSuffix(s.cfg.Speech.URL, "/v1")
	return base + p
}

func (s *Server) sttRequest(ctx context.Context, method, p string, body io.Reader, contentType string) (*http.Response, error) {
	req, err := http.NewRequestWithContext(ctx, method, s.sttEndpoint(p), body)
	if err != nil {
		return nil, err
	}
	if contentType != "" {
		req.Header.Set("Content-Type", contentType)
	}
	if s.cfg.Speech.APIKey != "" {
		req.Header.Set("Authorization", "Bearer "+s.cfg.Speech.APIKey)
	}
	return http.DefaultClient.Do(req)
}

func (s *Server) handleVoiceTranscribe(w http.ResponseWriter, r *http.Request) {
	if s.voiceMode() != "server" {
		writeError(w, http.StatusNotFound, "Serverseitige Spracherkennung ist nicht eingerichtet")
		return
	}
	cfg := s.cfg.Speech
	userID := auth.GetUserID(r.Context())
	if !s.voice.allowVoice(userID, cfg.PerUserPerHour, time.Now()) {
		writeError(w, http.StatusTooManyRequests, "Stundenlimit für die Spracherkennung erreicht. Bitte später erneut versuchen.")
		return
	}
	allowLongTransfer(w)
	file, header, err := r.FormFile("file")
	if err != nil {
		var tooBig *http.MaxBytesError
		if errors.As(err, &tooBig) {
			writeError(w, http.StatusRequestEntityTooLarge, fmt.Sprintf("Aufnahme zu groß (höchstens %d MB)", cfg.MaxUploadMB))
			return
		}
		writeError(w, http.StatusBadRequest, "Keine Aufnahme im Feld \"file\"")
		return
	}
	defer file.Close()
	audio, err := io.ReadAll(file)
	if err != nil || len(audio) == 0 {
		writeError(w, http.StatusBadRequest, "Aufnahme konnte nicht gelesen werden")
		return
	}

	// Wait for a free slot; the request may be cancelled meanwhile (user stopped waiting)
	ctx, cancel := context.WithTimeout(r.Context(), time.Duration(cfg.TimeoutSeconds)*time.Second)
	defer cancel()
	select {
	case s.voice.slots <- struct{}{}:
		defer func() { <-s.voice.slots }()
	case <-ctx.Done():
		slog.Warn("Spracherkennung: keine freie Kapazität innerhalb der Zeitgrenze", "user_id", userID, "parallel", cfg.MaxParallel)
		writeError(w, http.StatusServiceUnavailable, "Die Spracherkennung ist gerade ausgelastet. Bitte gleich noch einmal versuchen.")
		return
	}

	started := time.Now()
	text, err := s.transcribe(ctx, audio, header.Filename, header.Header.Get("Content-Type"))
	if err != nil {
		slog.Warn("Spracherkennung fehlgeschlagen", "url", s.sttEndpoint("/v1/audio/transcriptions"), "model", cfg.Model,
			"bytes", len(audio), "duration_ms", time.Since(started).Milliseconds(), "error", err)
		msg := "Spracherkennung fehlgeschlagen"
		if errors.Is(err, context.DeadlineExceeded) {
			msg = "Spracherkennung hat zu lange gedauert"
		}
		writeError(w, http.StatusBadGateway, msg)
		return
	}
	slog.Debug("Spracherkennung", "user_id", userID, "bytes", len(audio), "duration_ms", time.Since(started).Milliseconds())
	writeJSON(w, http.StatusOK, map[string]string{"text": text})
}

// transcribe sends the audio to the Whisper server (OpenAI API: POST /v1/audio/transcriptions).
func (s *Server) transcribe(ctx context.Context, audio []byte, filename, contentType string) (string, error) {
	var body bytes.Buffer
	mw := multipart.NewWriter(&body)
	if filename == "" {
		filename = "aufnahme.webm"
	}
	part := textproto.MIMEHeader{}
	part.Set("Content-Disposition", fmt.Sprintf(`form-data; name="file"; filename="%s"`, path.Base(strings.ReplaceAll(filename, `"`, ""))))
	if contentType == "" {
		contentType = "application/octet-stream"
	}
	part.Set("Content-Type", contentType)
	fw, err := mw.CreatePart(part)
	if err != nil {
		return "", err
	}
	if _, err := fw.Write(audio); err != nil {
		return "", err
	}
	_ = mw.WriteField("model", s.cfg.Speech.Model)
	if lang := s.cfg.Speech.Language; lang != "" && lang != "auto" {
		_ = mw.WriteField("language", lang)
	}
	_ = mw.WriteField("response_format", "json")
	if err := mw.Close(); err != nil {
		return "", err
	}

	resp, err := s.sttRequest(ctx, http.MethodPost, "/v1/audio/transcriptions", &body, mw.FormDataContentType())
	if err != nil {
		return "", err
	}
	defer resp.Body.Close()
	raw, _ := io.ReadAll(io.LimitReader(resp.Body, 1<<20))
	if resp.StatusCode != http.StatusOK {
		return "", fmt.Errorf("HTTP %d: %s", resp.StatusCode, strings.TrimSpace(string(raw)))
	}
	var out struct {
		Text string `json:"text"`
	}
	if err := json.Unmarshal(raw, &out); err != nil {
		return "", fmt.Errorf("unerwartete Antwort: %s", strings.TrimSpace(string(raw)))
	}
	return strings.TrimSpace(out.Text), nil
}

// checkVoice asks the Whisper server whether it is up and has the model.
func (s *Server) checkVoice(ctx context.Context) voiceStatus {
	st := voiceStatus{CheckedAt: time.Now().UTC().Format(time.RFC3339)}
	resp, err := s.sttRequest(ctx, http.MethodGet, "/v1/models", nil, "")
	if err != nil {
		st.Error = err.Error()
		return st
	}
	defer resp.Body.Close()
	if resp.StatusCode != http.StatusOK {
		st.Error = fmt.Sprintf("GET /v1/models: HTTP %d", resp.StatusCode)
		return st
	}
	st.Reachable = true
	var list struct {
		Data []struct {
			ID string `json:"id"`
		} `json:"data"`
	}
	_ = json.NewDecoder(io.LimitReader(resp.Body, 4<<20)).Decode(&list)
	for _, m := range list.Data {
		if m.ID == s.cfg.Speech.Model {
			st.ModelReady = true
		}
	}
	if !st.ModelReady {
		st.Error = "Modell ist noch nicht heruntergeladen"
	}
	return st
}

func (s *Server) setVoiceStatus(st voiceStatus) {
	s.voice.mu.Lock()
	s.voice.status = st
	s.voice.checked = time.Now()
	s.voice.mu.Unlock()
}

// startVoiceCheck waits for the Whisper server after startup (it may start later than MindSift) and
// asks it to download the model if it does not have it yet, so the first recording works.
func (s *Server) startVoiceCheck() {
	cfg := s.cfg.Speech
	slog.Info("Spracherkennung: Server-Modus", "url", cfg.URL, "model", cfg.Model, "language", cfg.Language,
		"max_parallel", cfg.MaxParallel, "per_user_per_hour", cfg.PerUserPerHour, "max_upload_mb", cfg.MaxUploadMB)
	downloadRequested := false
	for attempt := 0; ; attempt++ {
		ctx, cancel := context.WithTimeout(context.Background(), 10*time.Second)
		st := s.checkVoice(ctx)
		cancel()
		s.setVoiceStatus(st)
		switch {
		case st.ModelReady:
			slog.Info("Spracherkennung bereit", "url", cfg.URL, "model", cfg.Model)
			return
		case st.Reachable && !downloadRequested:
			downloadRequested = true
			slog.Info("Spracherkennung: Modell wird heruntergeladen (kann einige Minuten dauern)", "model", cfg.Model)
			go s.requestModelDownload()
		case !st.Reachable && attempt%6 == 0:
			slog.Warn("Spracherkennung: Whisper-Server nicht erreichbar, neuer Versuch folgt", "url", cfg.URL, "error", st.Error)
		}
		time.Sleep(10 * time.Second)
	}
}

func (s *Server) requestModelDownload() {
	// Model IDs contain a slash (owner/name); speaches takes them as a path
	ctx, cancel := context.WithTimeout(context.Background(), 2*time.Hour)
	defer cancel()
	resp, err := s.sttRequest(ctx, http.MethodPost, "/v1/models/"+s.cfg.Speech.Model, nil, "")
	if err != nil {
		slog.Warn("Spracherkennung: Modell-Download fehlgeschlagen", "model", s.cfg.Speech.Model, "error", err)
		return
	}
	defer resp.Body.Close()
	raw, _ := io.ReadAll(io.LimitReader(resp.Body, 4096))
	if resp.StatusCode >= 300 {
		slog.Warn("Spracherkennung: Modell-Download abgelehnt", "model", s.cfg.Speech.Model, "status", resp.StatusCode, "body", strings.TrimSpace(string(raw)))
	}
}

// handleAdminVoiceStatus shows the admin which speech recognition is active (configured by
// environment only) and checks the Whisper server right now.
func (s *Server) handleAdminVoiceStatus(w http.ResponseWriter, r *http.Request) {
	cfg := s.cfg.Speech
	out := map[string]interface{}{
		"mode":           s.voiceMode(),
		"demo":           s.cfg.DemoMode,
		"maxSeconds":     cfg.MaxSeconds,
		"maxUploadMb":    cfg.MaxUploadMB,
		"maxParallel":    cfg.MaxParallel,
		"perUserPerHour": cfg.PerUserPerHour,
		"timeoutSeconds": cfg.TimeoutSeconds,
	}
	if cfg.URL != "" {
		out["url"] = cfg.URL
		out["model"] = cfg.Model
		out["language"] = cfg.Language
		out["apiKeySet"] = cfg.APIKey != ""
	}
	if s.voiceMode() == "server" {
		ctx, cancel := context.WithTimeout(r.Context(), 5*time.Second)
		st := s.checkVoice(ctx)
		cancel()
		s.setVoiceStatus(st)
		out["status"] = st
	}
	writeJSON(w, http.StatusOK, out)
}