/**
 * @file DebugSessionLogger.kt
 * @brief Per-frame diagnostic trace written to a text file for the duration of a recording.
 */
package com.example.jnicpp.bowling

import android.content.ContentValues
import android.content.Context
import android.os.Build
import android.os.Environment
import android.provider.MediaStore
import com.google.mlkit.vision.pose.PoseLandmark
import java.io.BufferedWriter
import java.io.File
import java.io.FileOutputStream
import java.io.OutputStreamWriter
import java.text.SimpleDateFormat
import java.util.Locale
import kotlin.math.sqrt

/**
 * @brief Writes one line per analyzed frame -- landmark confidence,
 *        position, and derived torso scale -- to a plain-text file in the
 *        public Downloads/bowling folder, for the duration of a single
 *        recording.
 *
 * Exists purely as a step-counting troubleshooting aid: it's a much richer,
 * un-throttled trace than the 1/sec Logcat line in
 * [BowlingCameraActivity.onPoseResult], and lands somewhere retrievable
 * without needing adb or the Logcat panel -- the file shows up like any
 * other downloaded file, so it can be opened, shared, or copied off the
 * device by whatever means is convenient.
 *
 * One instance is meant to be reused across the Activity's lifetime; call
 * [start] when a recording begins and [stop] when it ends. Calling [log]
 * while not started is a harmless no-op.
 */
class DebugSessionLogger(private val appContext: Context) {

    private var writer: BufferedWriter? = null
    private var lastLoggedMs: Long? = null

    /**
     * @brief Opens a new timestamped file in Downloads/bowling and writes a header line.
     *
     * Uses `MediaStore.Downloads` on API 29+ (scoped storage, no extra
     * permission needed) and a direct file write into the public Downloads
     * directory below that, mirroring [CameraXController.startRecording]'s
     * own API-level branching for saving the video file.
     */
    fun start() {
        stop()
        val fileName = "bowling_debug_${SimpleDateFormat("yyyyMMdd_HHmmss", Locale.US).format(java.util.Date())}.txt"
        val outputStream = try {
            if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.Q) {
                val contentValues = ContentValues().apply {
                    put(MediaStore.Downloads.DISPLAY_NAME, fileName)
                    put(MediaStore.Downloads.MIME_TYPE, "text/plain")
                    put(MediaStore.Downloads.RELATIVE_PATH, "${Environment.DIRECTORY_DOWNLOADS}/bowling")
                }
                val uri = appContext.contentResolver.insert(MediaStore.Downloads.EXTERNAL_CONTENT_URI, contentValues)
                uri?.let { appContext.contentResolver.openOutputStream(it) }
            } else {
                val dir = File(Environment.getExternalStoragePublicDirectory(Environment.DIRECTORY_DOWNLOADS), "bowling")
                dir.mkdirs()
                FileOutputStream(File(dir, fileName))
            }
        } catch (_: Exception) {
            null
        }
        writer = outputStream?.let { BufferedWriter(OutputStreamWriter(it)) }
        lastLoggedMs = null
        writer?.let {
            it.write(
                "timestampMs dtMs ankleL(y,lik) ankleR(y,lik) hipL(y,lik) hipR(y,lik) shoulderL(y,lik) shoulderR(y,lik) " +
                    "wristL(y,lik) wristR(y,lik) torsoScalePx stepCount",
            )
            it.newLine()
            it.flush()
        }
    }

    /**
     * @brief Appends one frame's diagnostic data as a line, if a session is currently open.
     * @param landmarks EMA-smoothed landmarks for this frame, keyed by ML Kit's `PoseLandmark` type constant.
     * @param timestampMs Wall-clock time this frame was analyzed, in milliseconds.
     * @param stepCount Current cumulative step count at the time of this frame.
     */
    fun log(landmarks: Map<Int, SmoothedLandmark>, timestampMs: Long, stepCount: Int) {
        val out = writer ?: return

        val ankleL = landmarks[PoseLandmark.LEFT_ANKLE]
        val ankleR = landmarks[PoseLandmark.RIGHT_ANKLE]
        val hipL = landmarks[PoseLandmark.LEFT_HIP]
        val hipR = landmarks[PoseLandmark.RIGHT_HIP]
        val shoulderL = landmarks[PoseLandmark.LEFT_SHOULDER]
        val shoulderR = landmarks[PoseLandmark.RIGHT_SHOULDER]
        val wristL = landmarks[PoseLandmark.LEFT_WRIST]
        val wristR = landmarks[PoseLandmark.RIGHT_WRIST]

        val torsoScale = torsoScale(shoulderL, shoulderR, hipL, hipR)
        val dtMs = lastLoggedMs?.let { timestampMs - it }
        lastLoggedMs = timestampMs

        val line = "$timestampMs " +
            "${dtMs ?: "-"} " +
            "${format(ankleL)} ${format(ankleR)} " +
            "${format(hipL)} ${format(hipR)} " +
            "${format(shoulderL)} ${format(shoulderR)} " +
            "${format(wristL)} ${format(wristR)} " +
            "${torsoScale?.let { "%.1f".format(it) } ?: "-"} " +
            stepCount.toString()

        try {
            out.write(line)
            out.newLine()
            // Flush every line, not just on stop() -- if the app is force-
            // stopped mid-recording the file should still have everything
            // logged up to that point rather than losing a buffered tail.
            out.flush()
        } catch (_: Exception) {
            // A failed debug write shouldn't disrupt the actual recording.
        }
    }

    /** @brief Closes the current file, if one is open. Safe to call even if nothing is open. */
    fun stop() {
        try {
            writer?.close()
        } catch (_: Exception) {
            // Nothing useful to do about a failed close on a debug file.
        }
        writer = null
    }

    private fun format(landmark: SmoothedLandmark?): String =
        if (landmark == null) "-" else "(%.1f,%.2f)".format(landmark.y, landmark.inFrameLikelihood)

    /** @brief Shoulder-to-hip pixel distance, matching [LiveStepDetector]'s own torso-scale definition. */
    private fun torsoScale(
        shoulderL: SmoothedLandmark?,
        shoulderR: SmoothedLandmark?,
        hipL: SmoothedLandmark?,
        hipR: SmoothedLandmark?,
    ): Float? {
        val shoulder = shoulderL ?: shoulderR ?: return null
        val hip = hipL ?: hipR ?: return null
        val dx = shoulder.x - hip.x
        val dy = shoulder.y - hip.y
        return sqrt((dx * dx + dy * dy)).takeIf { it > 0f }
    }
}