import numpy as np
import cvxpy as cp
import re
import copy
import concurrent.futures
import gradio as gr
from datetime import datetime
import random
import moviepy
from transformers import pipeline
from transformers.pipelines.audio_utils import ffmpeg_read
from moviepy.editor import (
    ImageClip,
    VideoFileClip,
    TextClip, 
    CompositeVideoClip,
    CompositeAudioClip,
    AudioFileClip,
    concatenate_videoclips,
    concatenate_audioclips
)
from PIL import Image, ImageDraw, ImageFont
from moviepy.audio.AudioClip import AudioArrayClip
import subprocess
import json
import logging
import whisperx
import time
import os
import openai 
from openai import OpenAI 
import traceback
from TTS.api import TTS
import torch
from pydub import AudioSegment
from pyannote.audio import Pipeline
import wave
import librosa
import noisereduce as nr
import soundfile as sf
from paddleocr import PaddleOCR
import cv2
from rapidfuzz import fuzz
from tqdm import tqdm
import threading


logger = logging.getLogger(__name__)

# Configure logging
logging.basicConfig(level=logging.DEBUG, format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger(__name__)
logger.info(f"MoviePy Version: {moviepy.__version__}")

# Accept license terms for Coqui XTTS
os.environ["COQUI_TOS_AGREED"] = "1"
# torch.serialization.add_safe_globals([XttsConfig])

logger.info(gr.__version__)

client = OpenAI(
    api_key= os.environ.get("openAI_api_key"),  # This is the default and can be omitted
)
hf_api_key = os.environ.get("hf_token")

def silence(duration, fps=44100):
    """
    Returns a silent AudioClip of the specified duration.
    """
    return AudioArrayClip(np.zeros((int(fps*duration), 2)), fps=fps)

def count_words_or_characters(text):
    # Count non-Chinese words
    non_chinese_words = len(re.findall(r'\b[a-zA-Z0-9]+\b', text))
    
    # Count Chinese characters
    chinese_chars = len(re.findall(r'[\u4e00-\u9fff]', text))
    
    return non_chinese_words + chinese_chars
    
# Define the passcode
PASSCODE = "show_feedback_db"

css = """
/* Adjust row height */
.dataframe-container tr {
    height: 50px !important; 
}

/* Ensure text wrapping and prevent overflow */
.dataframe-container td {
    white-space: normal !important; 
    word-break: break-word !important;
}

/* Set column widths */
[data-testid="block-container"] .scrolling-dataframe th:nth-child(1), 
[data-testid="block-container"] .scrolling-dataframe td:nth-child(1) {
    width: 6%; /* Start column */
}

[data-testid="block-container"] .scrolling-dataframe th:nth-child(2), 
[data-testid="block-container"] .scrolling-dataframe td:nth-child(2) {
    width: 47%; /* Original text */
}

[data-testid="block-container"] .scrolling-dataframe th:nth-child(3), 
[data-testid="block-container"] .scrolling-dataframe td:nth-child(3) {
    width: 47%; /* Translated text */
}

[data-testid="block-container"] .scrolling-dataframe th:nth-child(4), 
[data-testid="block-container"] .scrolling-dataframe td:nth-child(4) {
    display: none !important;
}
"""

# Function to save feedback or provide access to the database file
def handle_feedback(feedback):
    feedback = feedback.strip()  # Clean up leading/trailing whitespace
    if not feedback:
        return "Feedback cannot be empty.", None

    if feedback == PASSCODE:
        # Provide access to the feedback.db file
        return "Access granted! Download the database file below.", "feedback.db"
    else:
        # Save feedback to the database
        with sqlite3.connect("feedback.db") as conn:
            cursor = conn.cursor()
            cursor.execute("CREATE TABLE IF NOT EXISTS studio_feedback (id INTEGER PRIMARY KEY, comment TEXT)")
            cursor.execute("INSERT INTO studio_feedback (comment) VALUES (?)", (feedback,))
            conn.commit()
        return "Thank you for your feedback!", None

def segment_background_audio(audio_path, background_audio_path="background_segments.wav"):
    """
    Uses Demucs to separate audio and extract background (non-vocal) parts.
    Merges drums, bass, and other stems into a single background track.
    """
    # Step 1: Run Demucs using the 4-stem model
    subprocess.run([
        "demucs",
        "-n", "htdemucs",  # 4-stem model
        audio_path
    ], check=True)

    # Step 2: Locate separated stem files
    filename = os.path.splitext(os.path.basename(audio_path))[0]
    stem_dir = os.path.join("separated", "htdemucs", filename)

    # Step 3: Load and merge background stems
    drums = AudioSegment.from_wav(os.path.join(stem_dir, "drums.wav"))
    bass = AudioSegment.from_wav(os.path.join(stem_dir, "bass.wav"))
    other = AudioSegment.from_wav(os.path.join(stem_dir, "other.wav"))

    background = drums.overlay(bass).overlay(other)

    # Step 4: Export the merged background
    background.export(background_audio_path, format="wav")
    return background_audio_path

def transcribe_video_with_speakers(video_path):
    # Extract audio from video
    video = VideoFileClip(video_path)
    audio_path = "audio.wav"
    video.audio.write_audiofile(audio_path)
    logger.info(f"Audio extracted from video: {audio_path}")

    segment_result = segment_background_audio(audio_path)
    print(f"Saved non-speech (background) audio to local")
    
    # Set up device
    device = "cuda" if torch.cuda.is_available() else "cpu"
    logger.info(f"Using device: {device}")
    
    try:
        # Load a medium model with float32 for broader compatibility
        model = whisperx.load_model("large-v3", device=device, compute_type="float32")
        logger.info("WhisperX model loaded")
    
        # Transcribe
        result = model.transcribe(audio_path, chunk_size=6, print_progress = True)
        logger.info("Audio transcription completed")

        # Get the detected language
        detected_language = result["language"]
        logger.debug(f"Detected language: {detected_language}")
        # Alignment
        # model_a, metadata = whisperx.load_align_model(language_code=result["language"], device=device)
        # result = whisperx.align(result["segments"], model_a, metadata, audio_path, device)
        # logger.info("Transcription alignment completed")
    
        # Diarization (works independently of Whisper model size)
        diarize_model = whisperx.DiarizationPipeline(use_auth_token=hf_api_key, device=device)
        diarize_segments = diarize_model(audio_path)
        logger.info("Speaker diarization completed")
    
        # Assign speakers
        result = whisperx.assign_word_speakers(diarize_segments, result)
        logger.info("Speakers assigned to transcribed segments")
    
    except Exception as e:
        logger.error(f"❌ WhisperX pipeline failed: {e}")

    # Extract timestamps, text, and speaker IDs
    transcript_with_speakers = [
        {
            "start": segment["start"],
            "end": segment["end"],
            "text": segment["text"],
            "speaker": segment.get("speaker", "SPEAKER_00")
        }
        for segment in result["segments"]
    ]

    # Collect audio for each speaker
    speaker_audio = {}
    for segment in result["segments"]:
        speaker = segment["speaker"]
        if speaker not in speaker_audio:
            speaker_audio[speaker] = []
        speaker_audio[speaker].append((segment["start"], segment["end"]))

    # Collapse and truncate speaker audio
    speaker_sample_paths = {}
    audio_clip = AudioFileClip(audio_path)
    for speaker, segments in speaker_audio.items():
        speaker_clips = [audio_clip.subclip(start, end) for start, end in segments]
        combined_clip = concatenate_audioclips(speaker_clips)
        truncated_clip = combined_clip.subclip(0, min(30, combined_clip.duration))
        sample_path = f"speaker_{speaker}_sample.wav"
        truncated_clip.write_audiofile(sample_path)
        speaker_sample_paths[speaker] = sample_path
        logger.info(f"Created sample for {speaker}: {sample_path}")

    # Clean up
    video.close()
    audio_clip.close()
    os.remove(audio_path)

    return transcript_with_speakers, detected_language

# Function to get the appropriate translation model based on target language
def get_translation_model(source_language, target_language):
    """
    Get the translation model based on the source and target language.

    Parameters:
    - target_language (str): The language to translate the content into (e.g., 'es', 'fr').
    - source_language (str): The language of the input content (default is 'en' for English).
    
    Returns:
    - str: The translation model identifier.
    """
    # List of allowable languages
    allowable_languages = ["en", "es", "fr", "zh", "de", "it", "pt", "ja", "ko", "ru", "hi", "tr"]

    # Validate source and target languages
    if source_language not in allowable_languages:
        logger.debug(f"Invalid source language '{source_language}'. Supported languages are: {', '.join(allowable_languages)}")
        # Return a default model if source language is invalid
        source_language = "en"  # Default to 'en'

    if target_language not in allowable_languages:
        logger.debug(f"Invalid target language '{target_language}'. Supported languages are: {', '.join(allowable_languages)}")
        # Return a default model if target language is invalid
        target_language = "zh"  # Default to 'zh'

    if source_language == target_language:
        source_language = "en"  # Default to 'en'
        target_language = "zh"  # Default to 'zh'

    # Return the model using string concatenation
    return f"Helsinki-NLP/opus-mt-{source_language}-{target_language}"

def translate_single_entry(entry, translator):
    original_text = entry["text"]
    translated_text = translator(original_text)[0]['translation_text']
    return {
        "start": entry["start"],
        "original": original_text,
        "translated": translated_text,
        "end": entry["end"],
        "speaker": entry["speaker"]
    }

def translate_text(transcription_json, source_language, target_language):
    # Load the translation model for the specified target language
    translation_model_id = get_translation_model(source_language, target_language)
    logger.debug(f"Translation model: {translation_model_id}")
    translator = pipeline("translation", model=translation_model_id)

    # Use ThreadPoolExecutor to parallelize translations
    with concurrent.futures.ThreadPoolExecutor() as executor:
        # Submit all translation tasks and collect results
        translate_func = lambda entry: translate_single_entry(entry, translator)
        translated_json = list(executor.map(translate_func, transcription_json))

    # Sort the translated_json by start time
    translated_json.sort(key=lambda x: x["start"])

    # Log the components being added to translated_json
    for entry in translated_json:
        logger.debug("Added to translated_json: start=%s, original=%s, translated=%s, end=%s, speaker=%s",
                     entry["start"], entry["original"], entry["translated"], entry["end"], entry["speaker"])

    return translated_json

def update_translations(file, edited_table, process_mode):
    """
    Update the translations based on user edits in the Gradio Dataframe.
    """
    output_video_path = "output_video.mp4"
    logger.debug(f"Editable Table: {edited_table}")

    if file is None:
        logger.info("No file uploaded. Please upload a video/audio file.")
        return None, [], None, "No file uploaded. Please upload a video/audio file."
        
    try:
        start_time = time.time()  # Start the timer

        # Convert the edited_table (list of lists) back to list of dictionaries
        updated_translations = [
            {
                "start": row["start"],  # Access by column name
                "original": row["original"],
                "translated": row["translated"],
                "end": row["end"]
            }
            for _, row in edited_table.iterrows()
        ]

        # Call the function to process the video with updated translations
        add_transcript_voiceover(file.name, updated_translations, output_video_path, process_mode)

        # Calculate elapsed time
        elapsed_time = time.time() - start_time
        elapsed_time_display = f"Updates applied successfully in {elapsed_time:.2f} seconds."

        return output_video_path, elapsed_time_display

    except Exception as e:
        raise ValueError(f"Error updating translations: {e}")

def create_subtitle_clip_pil(text, start_time, end_time, video_width, video_height, font_path):
    try:
        subtitle_width = int(video_width * 0.8)
        aspect_ratio = video_height / video_width
        subtitle_font_size = int(video_width // 22 if aspect_ratio > 1.2 else video_height // 24)

        font = ImageFont.truetype(font_path, subtitle_font_size)

        dummy_img = Image.new("RGBA", (subtitle_width, 1), (0, 0, 0, 0))
        draw = ImageDraw.Draw(dummy_img)

        # Word wrapping
        lines = []
        line = ""
        for word in text.split():
            test_line = f"{line} {word}".strip()
            bbox = draw.textbbox((0, 0), test_line, font=font)
            w = bbox[2] - bbox[0]
            if w <= subtitle_width - 10:
                line = test_line
            else:
                lines.append(line)
                line = word
        lines.append(line)
        
        outline_width=2
        line_heights = [draw.textbbox((0, 0), l, font=font)[3] - draw.textbbox((0, 0), l, font=font)[1] for l in lines]
        total_height = sum(line_heights) + (len(lines) - 1) * 5 + 6 * outline_width

        img = Image.new("RGBA", (subtitle_width, total_height), (0, 0, 0, 0))
        draw = ImageDraw.Draw(img)

        def draw_text_with_outline(draw, pos, text, font, fill="yellow", outline="black", outline_width = outline_width):
            x, y = pos
            # Draw outline
            for dx in range(-outline_width, outline_width + 1):
                for dy in range(-outline_width, outline_width + 1):
                    if dx != 0 or dy != 0:
                        draw.text((x + dx, y + dy), text, font=font, fill=outline)
            # Draw main text
            draw.text((x, y), text, font=font, fill=fill)

        y = 0
        for idx, line in enumerate(lines):
            bbox = draw.textbbox((0, 0), line, font=font)
            w = bbox[2] - bbox[0]
            x = (subtitle_width - w) // 2
            draw_text_with_outline(draw, (x, y), line, font)
            y += line_heights[idx] + 5

        img_np = np.array(img)
        margin = int(video_height * 0.05)
        img_clip = ImageClip(img_np) # Create the ImageClip first
        image_height = img_clip.size[1]
        txt_clip = (
            img_clip  # Use the already created clip
            .set_start(start_time)
            .set_duration(end_time - start_time)
            .set_position(("center", video_height - image_height - margin))
            .set_opacity(0.9)
        )

        return txt_clip

    except Exception as e:
        logger.error(f"❌ Failed to create subtitle clip: {e}")
        return None

def solve_optimal_alignment(original_segments, generated_durations, total_duration):
    """
    Aligns speech segments using quadratic programming. If optimization fails,
    applies greedy fallback: center shorter segments, stretch longer ones.
    Logs alignment results for traceability.
    """
    N = len(original_segments)
    d = np.array(generated_durations)
    m = np.array([(seg['start'] + seg['end']) / 2 for seg in original_segments])

    try:
        s = cp.Variable(N)
        objective = cp.Minimize(cp.sum_squares(s + d / 2 - m))

        constraints = [s[0] >= 0]
        for i in range(N - 1):
            constraints.append(s[i] + d[i] <= s[i + 1])
        constraints.append(s[N - 1] + d[N - 1] <= total_duration)

        problem = cp.Problem(objective, constraints)
        problem.solve()

        if s.value is None:
            raise ValueError("Solver failed")

        for i in range(N):
            original_segments[i]['start'] = round(s.value[i], 3)
            original_segments[i]['end'] = round(s.value[i] + d[i], 3)
            logger.info(
                f"[OPT] Segment {i}: duration={d[i]:.2f}s | start={original_segments[i]['start']:.2f}s | "
                f"end={original_segments[i]['end']:.2f}s | mid={m[i]:.2f}s"
            )

    except Exception as e:
        logger.warning(f"⚠️ Optimization failed: {e}, falling back to greedy alignment.")

        for i in range(N):
            orig_start = original_segments[i]['start']
            orig_end = original_segments[i]['end']
            orig_mid = (orig_start + orig_end) / 2
            gen_duration = generated_durations[i]
            orig_duration = orig_end - orig_start

            if gen_duration <= orig_duration:
                new_start = orig_mid - gen_duration / 2
                new_end = orig_mid + gen_duration / 2
            else:
                extra = (gen_duration - orig_duration) / 2
                new_start = orig_start - extra
                new_end = orig_end + extra

                if i > 0:
                    prev_end = original_segments[i - 1]['end']
                    new_start = max(new_start, prev_end + 0.01)

                if i < N - 1:
                    next_start = original_segments[i + 1]['start']
                    new_end = min(new_end, next_start - 0.01)

                if new_end <= new_start:
                    new_start = orig_start
                    new_end = orig_start + gen_duration

            original_segments[i]['start'] = round(new_start, 3)
            original_segments[i]['end'] = round(new_end, 3)

            logger.info(
                f"[FALLBACK] Segment {i}: duration={gen_duration:.2f}s | start={new_start:.2f}s | "
                f"end={new_end:.2f}s | original_mid={orig_mid:.2f}s"
            )

    return original_segments

ocr_model = None
ocr_lock = threading.Lock()

def init_ocr_model():
    global ocr_model
    with ocr_lock:
        if ocr_model is None:
            ocr_model = PaddleOCR(use_angle_cls=True, lang="ch")

def find_best_subtitle_region(frame, ocr_model, region_height_ratio=0.35, num_strips=5, min_conf=0.5):
    """
    Automatically identifies the best subtitle region in a video frame using OCR confidence.

    Parameters:
    - frame: full video frame (BGR np.ndarray)
    - ocr_model: a loaded PaddleOCR model
    - region_height_ratio: portion of image height to scan (from bottom up)
    - num_strips: how many horizontal strips to evaluate
    - min_conf: minimum average confidence to consider a region valid

    Returns:
    - crop_region: the cropped image region with highest OCR confidence
    - region_box: (y_start, y_end) of the region in the original frame
    """
    height, width, _ = frame.shape
    region_height = int(height * region_height_ratio)
    base_y_start = height - region_height
    strip_height = region_height // num_strips

    best_score = -1
    best_crop = None
    best_bounds = (0, height)

    for i in range(num_strips):
        y_start = base_y_start + i * strip_height
        y_end = y_start + strip_height
        strip = frame[y_start:y_end, :]

        try:
            result = ocr_model.ocr(strip, cls=True)
            if not result or not result[0]:
                continue

            total_score = sum(line[1][1] for line in result[0])
            avg_score = total_score / len(result[0])

            if avg_score > best_score:
                best_score = avg_score
                best_crop = strip
                best_bounds = (y_start, y_end)

        except Exception as e:
            continue  # Fail silently on OCR issues

    if best_score >= min_conf and best_crop is not None:
        return best_crop, best_bounds
    else:
        # Fallback to center-bottom strip
        fallback_y = height - int(height * 0.2)
        return frame[fallback_y:, :], (fallback_y, height)

def ocr_frame_worker(args):
    frame_idx, frame_time, frame = args

    init_ocr_model()  # Ensure model is loaded once per process

    if frame is None or frame.size == 0:
        return {"time": frame_time, "text": ""}

    if not isinstance(frame, np.ndarray):
        return {"time": frame_time, "text": ""}

    if frame.dtype != np.uint8:
        frame = frame.astype(np.uint8)

    try:
        subtitle_crop, _ = find_best_subtitle_region(frame, ocr_model)
        result = ocr_model.ocr(subtitle_crop, cls=True)
        texts = [line[1][0] for line in result[0]] if result[0] else []
        combined_text = " ".join(texts).strip()
        return {"time": frame_time, "text": combined_text}
    except Exception as e:
        print(f"⚠️ OCR failed at {frame_time:.2f}s: {e}")
        return {"time": frame_time, "text": ""}

def frame_is_in_audio_segments(frame_time, audio_segments, tolerance=0.2):
    for segment in audio_segments:
        start, end = segment["start"], segment["end"]
        if (start - tolerance) <= frame_time <= (end + tolerance):
            return True
    return False

def extract_ocr_subtitles_parallel(video_path, transcription_json, interval_sec=0.5, num_workers=4):
    cap = cv2.VideoCapture(video_path)
    fps = cap.get(cv2.CAP_PROP_FPS)
    frames = []
    frame_idx = 0
    success, frame = cap.read()

    while success:
        if frame_idx % int(fps * interval_sec) == 0:
            frame_time = frame_idx / fps
            if frame_is_in_audio_segments(frame_time, transcription_json):
                frames.append((frame_idx, frame_time, frame.copy()))
        success, frame = cap.read()
        frame_idx += 1
    cap.release()

    ocr_results = []
    ocr_failures = 0  # Count OCR failures
    with concurrent.futures.ThreadPoolExecutor(max_workers=num_workers) as executor:
        futures = [executor.submit(ocr_frame_worker, frame) for frame in frames]

        for f in tqdm(concurrent.futures.as_completed(futures), total=len(futures)):
            try:
                result = f.result()
                if result["text"]:
                    ocr_results.append(result)
            except Exception as e:
                ocr_failures += 1

    logger.info(f"✅ OCR extraction completed: {len(ocr_results)} frames successful, {ocr_failures} frames failed.")
    return ocr_results


def collapse_ocr_subtitles(ocr_json, text_similarity_threshold=90):
    collapsed = []
    current = None
    for entry in ocr_json:
        time = entry["time"]
        text = entry["text"]

        if not current:
            current = {"start": time, "end": time, "text": text}
            continue

        sim = fuzz.ratio(current["text"], text)
        if sim >= text_similarity_threshold:
            current["end"] = time
        else:
            collapsed.append(current)
            current = {"start": time, "end": time, "text": text}
    if current:
        collapsed.append(current)

    # Log collapsed OCR summary
    logger.info(f"✅ OCR subtitles collapsed into {len(collapsed)} segments.")
    for idx, seg in enumerate(collapsed):
        logger.debug(f"[OCR Collapsed {idx}] {seg['start']:.2f}s - {seg['end']:.2f}s: {seg['text'][:50]}...")

    return collapsed

def post_edit_transcribed_segments(transcription_json, video_path,
                                   interval_sec=0.5,
                                   text_similarity_threshold=80,
                                   time_tolerance=1.0,
                                   num_workers=4):
    """
    Given WhisperX transcription (transcription_json) and video,
    use OCR subtitles to post-correct and safely insert missing captions.
    """

    # Step 1: Extract OCR subtitles (only near audio segments)
    ocr_json = extract_ocr_subtitles_parallel(
        video_path, 
        transcription_json, 
        interval_sec=interval_sec, 
        num_workers=num_workers
    )

    # Step 2: Collapse repetitive OCR
    collapsed_ocr = collapse_ocr_subtitles(ocr_json, text_similarity_threshold=90)

    # Step 3: Refine existing WhisperX segments (Phase 1)
    merged_segments = []
    used_ocr_indices = set()

    for entry_idx, entry in enumerate(transcription_json):
        start = entry.get("start", 0)
        end = entry.get("end", 0)
        base_text = entry.get("text", "")

        best_match_idx = None
        best_score = -1

        for ocr_idx, ocr in enumerate(collapsed_ocr):
            time_overlap = not (ocr["end"] < start - time_tolerance or ocr["start"] > end + time_tolerance)
            if not time_overlap:
                continue

            sim = fuzz.ratio(ocr["text"], base_text)
            if sim > best_score:
                best_score = sim
                best_match_idx = ocr_idx

        updated_entry = entry.copy()
        if best_match_idx is not None and best_score >= text_similarity_threshold:
            updated_entry["text"] = collapsed_ocr[best_match_idx]["text"]
            updated_entry["ocr_matched"] = True
            updated_entry["ocr_similarity"] = best_score
            used_ocr_indices.add(best_match_idx)
        else:
            updated_entry["ocr_matched"] = False
            updated_entry["ocr_similarity"] = best_score if best_score >= 0 else None

        merged_segments.append(updated_entry)
    
    # Step 4: Insert unused OCR segments (Phase 2)
    inserted_segments = []
    for ocr_idx, ocr in enumerate(collapsed_ocr):
        if ocr_idx in used_ocr_indices:
            continue
    
        # Check for fuzzy duplicates in WhisperX
        duplicate = False
        for whisper_seg in transcription_json:
            if abs(ocr["start"] - whisper_seg["start"]) < time_tolerance or abs(ocr["end"] - whisper_seg["end"]) < time_tolerance:
                sim = fuzz.ratio(ocr["text"], whisper_seg["text"])
                if sim >= text_similarity_threshold:
                    duplicate = True
                    break
    
        if duplicate:
            logger.debug(f"🟡 Skipping near-duplicate OCR: '{ocr['text']}'")
            continue
    
        # Infer speaker from nearest WhisperX entry
        nearby = sorted(transcription_json, key=lambda x: abs(x["start"] - ocr["start"]))
        speaker_guess = nearby[0].get("speaker", "unknown") if nearby else "unknown"
    
        inserted_segment = {
            "start": ocr["start"],
            "end": ocr["end"],
            "text": ocr["text"],
            "speaker": speaker_guess
        }
        inserted_segments.append(inserted_segment)

    # Step 5: Combine and sort
    final_segments = merged_segments + inserted_segments
    final_segments = sorted(final_segments, key=lambda x: x["start"])

    print(f"✅ Post-editing completed: {len(final_segments)} total segments "
          f"({len(inserted_segments)} OCR-inserted segments)")

    return final_segments


def process_entry(entry, i, tts_model, video_width, video_height, process_mode, target_language, font_path, speaker_sample_paths=None):
    logger.debug(f"Processing entry {i}: {entry}")
    error_message = None

    try:
        txt_clip = create_subtitle_clip_pil(entry["translated"], entry["start"], entry["end"], video_width, video_height, font_path)
    except Exception as e:
        error_message = f"❌ Failed to create subtitle clip for entry {i}: {e}"
        logger.error(error_message)
        txt_clip = None

    audio_segment = None
    actual_duration = 0.0
    if process_mode > 1:
        try:
            segment_audio_path = f"segment_{i}_voiceover.wav"
            desired_duration = entry["end"] - entry["start"]
            desired_speed = entry['speed'] #calibrated_speed(entry['translated'], desired_duration)

            speaker = entry.get("speaker", "SPEAKER_00")
            speaker_wav_path = f"speaker_{speaker}_sample.wav"

            if process_mode > 2 and speaker_wav_path and os.path.exists(speaker_wav_path) and target_language in tts_model.synthesizer.tts_model.language_manager.name_to_id.keys():
                generate_voiceover_clone(entry['translated'], tts_model, desired_speed, target_language, speaker_wav_path, segment_audio_path)
            else:
                generate_voiceover_OpenAI(entry['translated'], target_language, desired_speed, segment_audio_path)

            if not segment_audio_path or not os.path.exists(segment_audio_path):
                raise FileNotFoundError(f"Voiceover file not generated at: {segment_audio_path}")

            audio_clip = AudioFileClip(segment_audio_path)
            actual_duration = audio_clip.duration

            audio_segment = audio_clip  # Do not set start here, alignment happens later

        except Exception as e:
            err = f"❌ Failed to generate audio segment for entry {i}: {e}"
            logger.error(err)
            error_message = error_message + " | " + err if error_message else err
            audio_segment = None

    return i, txt_clip, audio_segment, actual_duration, error_message


def add_transcript_voiceover(video_path, translated_json, output_path, process_mode, target_language="en", speaker_sample_paths=None, background_audio_path="background_segments.wav"):

    video = VideoFileClip(video_path)
    font_path = "./NotoSansSC-Regular.ttf"

    text_clips = []
    audio_segments = []
    actual_durations = []
    error_messages = []

    if process_mode > 2:
        global tts_model
        if tts_model is None:
            try:
                print("🔄 Loading XTTS model...")
                from TTS.api import TTS
                tts_model = TTS(model_name="tts_models/multilingual/multi-dataset/your_tts")
                print("✅ XTTS model loaded successfully.")
            except Exception as e:
                print("❌ Error loading XTTS model:")
                traceback.print_exc()
                return f"Error loading XTTS model: {e}"

    with concurrent.futures.ThreadPoolExecutor() as executor:
        futures = [executor.submit(process_entry, entry, i, tts_model, video.w, video.h, process_mode, target_language, font_path, speaker_sample_paths)
                   for i, entry in enumerate(translated_json)]

        results = []
        for future in concurrent.futures.as_completed(futures):
            try:
                i, txt_clip, audio_segment, actual_duration, error = future.result()
                results.append((i, txt_clip, audio_segment, actual_duration))
                if error:
                    error_messages.append(f"[Entry {i}] {error}")
            except Exception as e:
                err = f"❌ Unexpected error in future result: {e}"
                error_messages.append(err)

    results.sort(key=lambda x: x[0])
    text_clips = [clip for _, clip, _, _ in results if clip]
    generated_durations = [dur for _, _, _, dur in results if dur > 0]

    # Align using optimization (modifies translated_json in-place)
    translated_json = solve_optimal_alignment(translated_json, generated_durations, video.duration)

    # Set aligned timings
    audio_segments = []
    for i, entry in enumerate(translated_json):
        segment = results[i][2]  # AudioFileClip
        if segment:
            segment = segment.set_start(entry['start']).set_duration(entry['end'] - entry['start'])
            audio_segments.append(segment)

    final_video = CompositeVideoClip([video] + text_clips)

    if process_mode > 1 and audio_segments:
        try:
            voice_audio = CompositeAudioClip(audio_segments).set_duration(video.duration)

            if background_audio_path and os.path.exists(background_audio_path):
                background_audio = AudioFileClip(background_audio_path).set_duration(video.duration)
                final_audio = CompositeAudioClip([voice_audio, background_audio])
            else:
                final_audio = voice_audio

            final_video = final_video.set_audio(final_audio)

        except Exception as e:
            print(f"❌ Failed to set audio: {e}")

    final_video.write_videofile(output_path, codec="libx264", audio_codec="aac")

    return error_messages

def generate_voiceover_OpenAI(full_text, language, desired_speed, output_audio_path):
    """
    Generate voiceover from translated text for a given language using OpenAI TTS API.
    """
    # Define the voice based on the language (for now, use 'alloy' as default)
    voice = "alloy"  # Adjust based on language if needed

    # Define the model (use tts-1 for real-time applications)
    model = "tts-1"

    max_retries = 3
    retry_count = 0

    while retry_count < max_retries:
        try:
            # Create the speech using OpenAI TTS API
            response = client.audio.speech.create(
                model=model,
                voice=voice,
                input=full_text,
                speed=desired_speed
            )
            # Save the audio to the specified path
            with open(output_audio_path, 'wb') as f:
                for chunk in response.iter_bytes():
                    f.write(chunk)
            logging.info(f"Voiceover generated successfully for {output_audio_path}")
            break

        except Exception as e:
            retry_count += 1
            logging.error(f"Error generating voiceover (retry {retry_count}/{max_retries}): {e}")
            time.sleep(5)  # Wait 5 seconds before retrying

    if retry_count == max_retries:
        raise ValueError(f"Failed to generate voiceover after {max_retries} retries.")

def generate_voiceover_clone(full_text, tts_model, desired_speed, target_language, speaker_wav_path, output_audio_path):
    try:

        tts_model.tts_to_file(
            text=full_text,
            speaker_wav=speaker_wav_path,
            language=target_language,
            file_path=output_audio_path,
            speed=desired_speed,
            split_sentences=True
        )
        msg = (
            f"✅ Voice cloning completed successfully. "
            f"[Speaker Wav: {speaker_wav_path}] [Speed: {desired_speed}]"
        )
        logger.info(msg)
        return output_audio_path, msg, None

    except Exception as e:
        generate_voiceover_OpenAI(full_text, target_language, desired_speed, output_audio_path)
        err_msg = f"❌ An error occurred: {str(e)}, fallback to premium voice"
        logger.error(traceback.format_exc())
        return None, err_msg, err_msg

def apply_adaptive_speed(translated_json_raw, source_language, target_language, k=3.0, default_prior_speed=5.0):
    """
    Adds `speed` (relative, 1.0 = normal speed) and `target_duration` (sec) to each segment
    using shrinkage-based estimation, language stretch ratios, and optional style modifiers.
    Speeds are clamped to [0.85, 1.7] to avoid unnatural TTS behavior.
    """
    translated_json = copy.deepcopy(translated_json_raw)

    # Prior average speech speeds by (category, target language)
    priors = {
        ("drama", "en"): 5.0,
        ("drama", "zh"): 4.5,
        ("tutorial", "en"): 5.2,
        ("tutorial", "zh"): 4.8,
        ("shortplay", "en"): 5.1,
        ("shortplay", "zh"): 4.7,
    }

    # Adjustment ratio based on language pair (source → target)
    lang_ratio = {
        ("zh", "en"): 0.85,
        ("en", "zh"): 1.15,
        ("zh", "jp"): 1.05,
        ("en", "ja"): 0.9,
    }

    # Optional style modulation factor
    style_modifiers = {
        "dramatic": 0.9,
        "urgent": 1.1,
        "neutral": 1.0
    }

    for idx, entry in enumerate(translated_json):
        start, end = float(entry.get("start", 0)), float(entry.get("end", 0))
        duration = max(0.1, end - start)

        original_text = entry.get("original", "")
        translated_text = entry.get("translated", "")
        category = entry.get("category", "drama")
        source_lang = source_language
        target_lang = target_language
        style = entry.get("style", "neutral").lower()

        # Observed speed from original
        base_text = original_text or translated_text
        obs_speed = len(base_text) / duration

        # Prior speed
        prior_speed = priors.get((category, target_lang), default_prior_speed)

        # Shrinkage
        shrink_speed = (duration * obs_speed + k * prior_speed) / (duration + k)

        # Language pacing adjustment
        ratio = lang_ratio.get((source_lang, target_lang), 1.0)
        adjusted_speed = shrink_speed * ratio

        # Style modulation
        mod = style_modifiers.get(style, 1.0)
        adjusted_speed *= mod

        # Final relative speed (normalized to prior)
        relative_speed = adjusted_speed / prior_speed

        # Clamp relative speed to [0.85, 1.7]
        relative_speed = max(0.85, min(1.7, relative_speed))

        # Compute target duration for synthesis
        target_chars = len(translated_text)
        target_duration = round(target_chars / adjusted_speed, 2)

        # Logging
        logger.info(
            f"[Segment {idx}] dur={duration:.2f}s | obs_speed={obs_speed:.2f} | prior={prior_speed:.2f} | "
            f"shrinked={shrink_speed:.2f} | lang_ratio={ratio} | style_mod={mod} | "
            f"adj_speed={adjusted_speed:.2f} | rel_speed={relative_speed:.2f} | "
            f"target_dur={target_duration:.2f}s"
        )

        entry["speed"] = round(relative_speed, 3)
        entry["target_duration"] = target_duration

    return translated_json

def calibrated_speed(text, desired_duration):
    """
    Compute a speed factor to help TTS fit audio into desired duration,
    using a simple truncated linear function of characters per second.
    """
    char_count = len(text.strip())
    if char_count == 0 or desired_duration <= 0:
        return 1.0  # fallback

    cps = char_count / desired_duration  # characters per second

    # Truncated linear mapping
    if cps < 14:
        return 1.0
    elif cps > 25.2:
        return 1.7
    else:
        slope = (1.7 - 1.0) / (25.2 - 14)
        return 1.0 + slope * (cps - 14)

def upload_and_manage(file, target_language, process_mode):
    if file is None:
        logger.info("No file uploaded. Please upload a video/audio file.")
        return None, [], None, "No file uploaded. Please upload a video/audio file."

    try:
        start_time = time.time()  # Start the timer
        logger.info(f"Started processing file: {file.name}")

        # Define paths for audio and output files
        audio_path = "audio.wav"
        output_video_path = "output_video.mp4"
        voiceover_path = "voiceover.wav"
        logger.info(f"Using audio path: {audio_path}, output video path: {output_video_path}, voiceover path: {voiceover_path}")

        # Step 1: Transcribe audio from uploaded media file and get timestamps
        logger.info("Transcribing audio...")
        transcription_json, source_language = transcribe_video_with_speakers(file.name)
        logger.info(f"Transcription completed. Detected source language: {source_language}")

        transcription_json_merged = post_edit_transcribed_segments(transcription_json, file.name)
        # Step 2: Translate the transcription
        logger.info(f"Translating transcription from {source_language} to {target_language}...")
        translated_json_raw = translate_text(transcription_json_merged, source_language, target_language)
        logger.info(f"Translation completed. Number of translated segments: {len(translated_json_raw)}")

        translated_json = apply_adaptive_speed(translated_json_raw, source_language, target_language)
        
        # Step 3: Add transcript to video based on timestamps
        logger.info("Adding translated transcript to video...")
        add_transcript_voiceover(file.name, translated_json, output_video_path, process_mode, target_language)
        logger.info(f"Transcript added to video. Output video saved at {output_video_path}")

        # Convert translated JSON into a format for the editable table
        logger.info("Converting translated JSON into editable table format...")
        editable_table = [
            [float(entry["start"]), entry["original"], entry["translated"], float(entry["end"]), entry["speaker"]]
            for entry in translated_json
        ]

        # Calculate elapsed time
        elapsed_time = time.time() - start_time
        elapsed_time_display = f"Processing completed in {elapsed_time:.2f} seconds."
        logger.info(f"Processing completed in {elapsed_time:.2f} seconds.")

        return editable_table, output_video_path, elapsed_time_display

    except Exception as e:
        logger.error(f"An error occurred: {str(e)}")
        return [], None, f"An error occurred: {str(e)}"

# Gradio Interface with Tabs
def build_interface():
    with gr.Blocks(css=css) as demo:
        gr.Markdown("## Video Localization")
        with gr.Row():
            with gr.Column(scale=4):
                file_input = gr.File(label="Upload Video/Audio File")
                language_input = gr.Dropdown(["en", "es", "fr", "zh"], label="Select Language")  # Language codes        
                process_mode = gr.Radio(choices=[("Transcription Only", 1),("Transcription with Premium Voice",2),("Transcription with Voice Clone", 3)],label="Choose Processing Type",value=1)
                submit_button = gr.Button("Post and Process")

            with gr.Column(scale=8):
                gr.Markdown("## Edit Translations")
                
                # Editable JSON Data
                editable_table = gr.Dataframe(
                    value=[],  # Default to an empty list to avoid undefined values
                    headers=["start", "original", "translated", "end", "speaker"],
                    datatype=["number", "str", "str", "number", "str"],
                    row_count=1,  # Initially empty
                    col_count=5,
                    interactive=[False, True, True, False, False],  # Control editability
                    label="Edit Translations",
                    wrap=True  # Enables text wrapping if supported
                )
                save_changes_button = gr.Button("Save Changes")
                processed_video_output = gr.File(label="Download Processed Video", interactive=True)  # Download button
                elapsed_time_display = gr.Textbox(label="Elapsed Time", lines=1, interactive=False)

            with gr.Column(scale=1):
                gr.Markdown("**Feedback**")
                feedback_input = gr.Textbox(
                    placeholder="Leave your feedback here...",
                    label=None,
                    lines=3,
                )
                feedback_btn = gr.Button("Submit Feedback")
                response_message = gr.Textbox(label=None, lines=1, interactive=False)
                db_download = gr.File(label="Download Database File", visible=False)
            
                # Link the feedback handling
                def feedback_submission(feedback):
                    message, file_path = handle_feedback(feedback)
                    if file_path:
                        return message, gr.update(value=file_path, visible=True)
                    return message, gr.update(visible=False)            

            save_changes_button.click(
                update_translations, 
                inputs=[file_input, editable_table, process_mode],
                outputs=[processed_video_output, elapsed_time_display]
            )

            submit_button.click(
                upload_and_manage, 
                inputs=[file_input, language_input, process_mode], 
                outputs=[editable_table, processed_video_output, elapsed_time_display]
            )

            # Connect submit button to save_feedback_db function
            feedback_btn.click(
                feedback_submission, 
                inputs=[feedback_input], 
                outputs=[response_message, db_download]
            )

    return demo

tts_model = None
# Launch the Gradio interface
demo = build_interface()
demo.launch()