app.py

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 pyannote.audio import Pipeline
import wave
import librosa
import noisereduce as nr
from paddleocr import PaddleOCR
import cv2
from rapidfuzz import fuzz
from tqdm import tqdm
import threading
import requests
import webrtcvad
from pydub import AudioSegment
from pydub.silence import split_on_silence
import soundfile as sf
import langcodes

# ISO 639-3 → ISO 639-1
def iso_639_3_to_1(code3):
    try:
        return langcodes.Language.get(code3).language
    except:
        return 'en'

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")
ELEVENLABS_API_KEY = os.environ.get("elevenlabs_token")
# Correct API endpoint for ElevenLabs Scribe
ELEVENLABS_SCRIBE_API_URL = "https://api.elevenlabs.io/v1/speech-to-text"

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", speech_audio_path="speech_segment.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
    vocals = AudioSegment.from_wav(os.path.join(stem_dir, "vocals.wav"))
    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")
    vocals.export(speech_audio_path, format="wav")
    return background_audio_path, speech_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, speech_audio_path = 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(speech_audio_path, chunk_size=4, 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, speech_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(speech_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 = {}
    logger.info("🔎 Start collecting valid audio segments per speaker...")
    
    for idx, segment in enumerate(result["segments"]):
        speaker = segment.get("speaker", "SPEAKER_00")
        start = segment["start"]
        end = segment["end"]
    
        if end > start and (end - start) > 0.05:  # Require >50ms duration
            if speaker not in speaker_audio:
                speaker_audio[speaker] = [(start, end)]
            else:
                speaker_audio[speaker].append((start, end))
            
            logger.debug(f"Segment {idx}: Added to speaker {speaker} [{start:.2f}s → {end:.2f}s]")
        else:
            logger.warning(f"⚠️ Segment {idx} discarded: invalid duration ({start:.2f}s → {end:.2f}s)")
    
    # Collapse and truncate speaker audio
    speaker_sample_paths = {}
    audio_clip = AudioFileClip(speech_audio_path)
    
    logger.info(f"🔎 Found {len(speaker_audio)} speakers with valid segments. Start creating speaker samples...")
    
    for speaker, segments in speaker_audio.items():
        logger.info(f"🔹 Speaker {speaker}: {len(segments)} valid segments")
    
        speaker_clips = [audio_clip.subclip(start, end) for start, end in segments]
        if not speaker_clips:
            logger.warning(f"⚠️ No valid audio clips for speaker {speaker}. Skipping sample creation.")
            continue
    
        if len(speaker_clips) == 1:
            logger.debug(f"Speaker {speaker}: Only one clip, skipping concatenation.")
            combined_clip = speaker_clips[0]
        else:
            logger.debug(f"Speaker {speaker}: Concatenating {len(speaker_clips)} clips.")
            combined_clip = concatenate_audioclips(speaker_clips)
    
        truncated_clip = combined_clip.subclip(0, min(30, combined_clip.duration))
        logger.debug(f"Speaker {speaker}: Truncated to {truncated_clip.duration:.2f} seconds.")

        # Step 4: Save the final result
        sample_path = f"speaker_{speaker}_sample.wav"
        truncated_clip.write_audiofile(sample_path)
        speaker_sample_paths[speaker] = sample_path
        logger.info(f"✅ Created and saved sample for {speaker}: {sample_path}")

    # Cleanup
    logger.info("🧹 Closing audio clip and removing temporary files...")
    video.close()
    audio_clip.close()
    os.remove(speech_audio_path)
    logger.info("✅ Finished processing all speaker samples.")

    return transcript_with_speakers, detected_language

def segment_audio_from_video(video_path, separate_background = True):
    # 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 = None
    speech_audio_path = audio_path

    if separate_background:
        # Assuming segment_background_audio returns a tuple (segment_result, speech_audio_path)
        segment_result, speech_audio_path = segment_background_audio(audio_path)
        print(f"Saved non-speech (background) audio to local")
    else:
        logger.info("Background audio separation skipped as per separate_background=False.")

    device = "cuda" if torch.cuda.is_available() else "cpu"
    logger.info(f"Using device: {device}")

    try:
        model = whisperx.load_model("large-v3", device=device, compute_type="float32")
        logger.info("WhisperX model loaded")
        result = model.transcribe(speech_audio_path, chunk_size=4, print_progress=True)
        logger.info("Audio transcription completed")
    except Exception as e:
        logger.error(f"❌ WhisperX pipeline failed: {e}")
        return audio_path, segment_result, []

    # Return segment boundaries (only timestamps, not text)
    transcript_with_speakers = [
        {
            "start": segment["start"],
            "end": segment["end"]
        }
        for segment in result["segments"]
        if segment["end"] > segment["start"]
    ]

    return audio_path, segment_result, transcript_with_speakers
    
def clean_transcribed_text(text: str) -> str:
    """
    Remove noise tags like (panting), [booming sound], repeated symbols, and trim whitespace.
    """
    text = re.sub(r"[\(\[\{].*?[\)\]\}]", "", text)
    text = re.sub(r"[_,.~`^•·。！？!?,，\.\/\\\-–—=+]+", " ", text)
    text = re.sub(r"\s+", " ", text).strip()
    return text

def transcribe_segments_with_scribe(full_audio_path, segments):
    transcribed_segments = []
    detected_language = "unknown"
    error_message = None

    if not os.path.exists(full_audio_path):
        return [], detected_language, f"Full audio file not found at {full_audio_path}"

    try:
        audio_clip = AudioFileClip(full_audio_path)
        headers = {"xi-api-key": ELEVENLABS_API_KEY}
        data = {"model_id": "scribe_v1"}
        params = {"diarize": "false"}

        logger.info(f"Starting transcription of {len(segments)} segments with ElevenLabs Scribe...")

        for i, segment in enumerate(segments):
            start, end = segment["start"], segment["end"]
            if end <= start:
                logger.warning(f"Skipping invalid segment {i}: {start:.2f}s → {end:.2f}s")
                continue

            temp_segment_audio_path = f"temp_segment_{i}.wav"
            try:
                sub_clip = audio_clip.subclip(start, end)
                sub_clip.write_audiofile(temp_segment_audio_path, codec='pcm_s16le')

                with open(temp_segment_audio_path, "rb") as audio_file:
                    files = {"file": (os.path.basename(temp_segment_audio_path), audio_file, "audio/wav")}
                    response = requests.post(ELEVENLABS_SCRIBE_API_URL, headers=headers, files=files, data=data, params=params)
                    response.raise_for_status()
                    scribe_result = response.json()

                    raw_text = scribe_result.get("text") or " ".join(
                        [w.get("text", "") for w in scribe_result.get("words", []) if w.get("type") == "word"]
                    )

                    cleaned_text = clean_transcribed_text(raw_text)
                    if cleaned_text:
                        transcribed_segments.append({
                            "start": start,
                            "end": end,
                            "text": cleaned_text,
                            "speaker": "SPEAKER_00"
                        })
                    else:
                        logger.info(f"Segment {i+1} discarded: cleaned text is empty.")

                    if "language_code" in scribe_result and detected_language == "unknown":
                        detected_language = iso_639_3_to_1(scribe_result["language_code"])

            except Exception as e:
                logger.error(f"Error processing segment {i+1}: {e}")
            finally:
                if os.path.exists(temp_segment_audio_path):
                    os.remove(temp_segment_audio_path)

        logger.info("All segments processed by ElevenLabs Scribe.")

    except Exception as e:
        error_message = f"An error occurred: {e}"
        logger.error(error_message)
    finally:
        if 'audio_clip' in locals():
            audio_clip.close()

    return transcribed_segments, detected_language, error_message

from collections import Counter

def process_scribe_output(scribe_response, max_line_length=50):
    """
    Processes the Scribe API response to clean the text and generate line-level timestamps.

    Args:
        scribe_response (dict): The raw response dictionary from the Scribe API.
        max_line_length (int): The maximum number of characters desired per line before
                                a new line is created. This is an approximate guide.

    Returns:
        list: A list of dictionaries, where each dictionary represents a line
              and contains 'text', 'start_time', 'end_time', and 'speaker_id'.
    """
    cleaned_words = []
    for word_info in scribe_response['words']:
        text = word_info['text']
        start = word_info['start']
        end = word_info['end']
        word_type = word_info['type']
        speaker_id = word_info.get('speaker_id', None)

        if word_type == 'audio_event':
            continue  # Remove audio event tags like ［背景音］
        elif word_type == 'spacing':
            if cleaned_words and cleaned_words[-1]['text'].endswith(' '):
                continue
            text = ' '

        cleaned_words.append({
            'text': text,
            'start': start,
            'end': end,
            'speaker_id': speaker_id
        })

    lines = []
    current_line_words = []
    current_line_start_time = None

    for i, word_info in enumerate(cleaned_words):
        if not current_line_words:
            current_line_start_time = word_info['start']

        current_line_words.append(word_info)

        current_line_text = "".join([w['text'] for w in current_line_words]).strip()

        line_should_end = (
            len(current_line_text) >= max_line_length or
            i == len(cleaned_words) - 1 or
            word_info['text'].endswith(('。', '？', '！'))
        )

        if line_should_end:
            line_text = current_line_text
            line_end_time = word_info['end']

            # Majority speaker_id in this line
            speaker_ids = [w['speaker_id'] for w in current_line_words if w['speaker_id'] is not None]
            speaker_id = Counter(speaker_ids).most_common(1)[0][0] if speaker_ids else None

            lines.append({
                'original': line_text,
                'start': current_line_start_time,
                'end': line_end_time,
                'speaker': speaker_id
            })

            current_line_words = []
            current_line_start_time = None

    return lines

def transcribe_with_scribe(full_audio_path):
    transcribed_segments = []
    detected_language = "unknown"
    error_message = None

    if not os.path.exists(full_audio_path):
        return [], detected_language, f"Full audio file not found at {full_audio_path}"

    headers = {"xi-api-key": ELEVENLABS_API_KEY}
    data = {
        "model_id": "scribe_v1",
        "diarize": "true"
    }

    logger.info(f"Starting transcription for full audio: {full_audio_path}")

    with open(full_audio_path, "rb") as audio_file:
        files = {"file": (os.path.basename(full_audio_path), audio_file, "audio/wav")}
        response = requests.post(ELEVENLABS_SCRIBE_API_URL, headers=headers, files=files, data=data)
        response.raise_for_status()
        scribe_result = response.json()
    return scribe_result

# 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
    and allow the user to download the updated JSON.
    """
    output_video_path = "output_video.mp4"
    updated_json_path = "updated_translations.json" # Define the path for the updated JSON file
    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, None

    try:
        start_time = time.time()  # Start the timer

        # Convert the edited_table (pandas DataFrame) back to list of dictionaries
        # Ensure column names match the original structure
        updated_translations = [
            {
                "start": row["start"],
                "original": row["original"],
                "translated": row["translated"],
                "end": row["end"],
                "speaker": row["speaker"] # Include speaker if it's part of your translation structure
            }
            for _, row in edited_table.iterrows()
        ]

        # Save the updated translations to a JSON file
        with open(updated_json_path, 'w', encoding='utf-8') as f:
            json.dump(updated_translations, f, indent=4, ensure_ascii=False)
        logger.info(f"Updated translations saved to: {updated_json_path}")

        # 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."

        if not os.path.isfile(updated_json_path):
            logger.error(f"Expected file at {updated_json_path} but got a directory.")
            return [], None, "", f"Translated JSON path is invalid."
        # Return the path to the updated JSON file as well
        return output_video_path, updated_json_path, elapsed_time_display
    except Exception as e:
        logger.error(f"Error updating translations: {e}")
        # Return Nones for all outputs if an error occurs
        return None, updated_json_path, None

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])

    if N == 0 or len(generated_durations) == 0:
        logger.warning("⚠️ Alignment skipped: empty segments or durations.")
        return original_segments  # or raise an error, depending on your app logic
    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

WHISPERX_TO_PADDLEOCR_LANG = {
    "zh": "ch",        # Chinese
    "en": "en",        # English
    "fr": "fr",        # French
    "de": "german",    # German
    "ja": "japan",     # Japanese
    "ko": "korean",    # Korean
    "ru": "russian",   # Russian
    "it": "italian",   # Italian
    "es": "spanish",   # Spanish
    # Add more mappings as needed
}

ocr_model = None
ocr_lock = threading.Lock()

def init_ocr_model(source_lang):
    """
    Initializes the PaddleOCR model using the mapped language.
    """
    global ocr_model
    with ocr_lock:
        if ocr_model is not None:
            return  # already initialized

        paddle_lang = WHISPERX_TO_PADDLEOCR_LANG.get(source_lang, "en")
        logger.info(f"🔤 Initializing OCR model for source language: {source_lang} → PaddleOCR lang: {paddle_lang}")
        ocr_model = PaddleOCR(use_angle_cls=True, lang=paddle_lang)

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, source_language, min_confidence=0.7):
    frame_idx, frame_time, frame = args

    init_ocr_model(source_language)  # Load model in thread-safe way

    if frame is None or frame.size == 0 or not isinstance(frame, np.ndarray):
        return {"time": frame_time, "text": ""}

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

    try:
        result = ocr_model.ocr(frame, cls=True)
        lines = result[0] if result else []
        texts = [line[1][0] for line in lines if line[1][1] >= min_confidence]
        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, source_language, interval_sec=0.2, 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, source_language) 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
            current["text"] = text
            logger.debug(f"MERGED: Current end extended to {time:.2f}s for text: '{current['text'][:50]}...' (Similarity: {sim})")
        else:
            logger.debug(f"NOT MERGING (Similarity: {sim} < Threshold: {text_similarity_threshold}):")
            logger.debug(f"  Previous segment: {current['start']:.2f}s - {current['end']:.2f}s: '{current['text'][:50]}...'")
            logger.debug(f"  New segment: {time:.2f}s: '{text[:50]}...'")
            collapsed.append(current)
            current = {"start": time, "end": time, "text": text}


    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 merge_speaker_and_time_from_whisperx(
    ocr_json,
    whisperx_json,
    replace_threshold=90,
    time_tolerance=1.0
):
    merged = []
    used_whisperx = set()
    whisperx_used_flags = [False] * len(whisperx_json)

    # Step 1: Attempt to match each OCR entry to a WhisperX entry
    for ocr in ocr_json:
        ocr_start, ocr_end = ocr["start"], ocr["end"]
        ocr_text = ocr["text"]

        best_match = None
        best_score = -1
        best_idx = None

        for idx, wx in enumerate(whisperx_json):
            wx_start, wx_end = wx["start"], wx["end"]
            wx_text = wx["text"]

            # Check for time overlap
            overlap = not (ocr_end < wx_start - time_tolerance or ocr_start > wx_end + time_tolerance)
            if not overlap:
                continue

            sim = fuzz.ratio(ocr_text, wx_text)
            if sim > best_score:
                best_score = sim
                best_match = wx
                best_idx = idx

        if best_match and best_score >= replace_threshold:
            # Replace WhisperX segment with higher quality OCR text
            new_segment = copy.deepcopy(best_match)
            new_segment["text"] = ocr_text
            new_segment["ocr_replaced"] = True
            new_segment["ocr_similarity"] = best_score
            whisperx_used_flags[best_idx] = True
            merged.append(new_segment)
        else:
            # No replacement, check if this OCR is outside WhisperX time coverage
            covered = any(
                abs((ocr_start + ocr_end)/2 - (wx["start"] + wx["end"])/2) < time_tolerance
                for wx in whisperx_json
            )
            if not covered:
                new_segment = copy.deepcopy(ocr)
                new_segment["ocr_added"] = True
                new_segment["speaker"] = "UNKNOWN"
                merged.append(new_segment)

    # Step 2: Add untouched WhisperX segments
    for idx, wx in enumerate(whisperx_json):
        if not whisperx_used_flags[idx]:
            merged.append(wx)

    # Step 3: Sort all merged segments
    merged = sorted(merged, key=lambda x: x["start"])

    return merged

def realign_ocr_segments(merged_ocr_json, min_gap=0.2):
    """
    Realign OCR segments to avoid overlaps using midpoint-based adjustment.
    """
    merged_ocr_json = sorted(merged_ocr_json, key=lambda x: x["start"])

    for i in range(1, len(merged_ocr_json)):
        prev = merged_ocr_json[i - 1]
        curr = merged_ocr_json[i]

        # If current overlaps with previous, adjust
        if curr["start"] < prev["end"] + min_gap:
            midpoint = (prev["end"] + curr["start"]) / 2
            prev["end"] = round(midpoint - min_gap / 2, 3)
            curr["start"] = round(midpoint + min_gap / 2, 3)

            # Prevent negative durations
            if curr["start"] >= curr["end"]:
                curr["end"] = round(curr["start"] + 0.3, 3)

    return merged_ocr_json
    
def post_edit_transcribed_segments(transcription_json, video_path, source_language,
                                   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, 
        source_language,
        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: Merge and realign OCR segments.
    ocr_merged = merge_speaker_and_time_from_whisperx(collapsed_ocr, transcription_json)
    ocr_realigned = realign_ocr_segments(ocr_merged)

    logger.info(f"✅ Final merged and realigned OCR: {len(ocr_realigned)} segments")
    return ocr_realigned

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)

    # Sort and filter together
    results.sort(key=lambda x: x[0])
    text_clips = [clip for _, clip, _, _ in results if clip]

    filtered = [(translated_json[i], txt, aud, dur) for i, txt, aud, dur in results if dur > 0]
    
    translated_json = [entry for entry, _, _, _ in filtered]
    generated_durations = [dur for _, _, _, dur in filtered]
    
    # 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, process_mode, 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)

    priors = {
        ("drama", "en"): 5.0,
        ("drama", "zh"): 4.5,
        ("drama", "fr"): 4.2,
        ("drama", "es"): 4.3,
        ("tutorial", "en"): 5.2,
        ("tutorial", "zh"): 4.8,
        ("tutorial", "fr"): 4.5,
        ("tutorial", "es"): 4.5,
        ("shortplay", "en"): 5.1,
        ("shortplay", "zh"): 4.7,
        ("shortplay", "fr"): 4.3,
        ("shortplay", "es"): 4.4,
    }

    # 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,
        ("en", "fr"): 0.85,
        ("en", "es"): 0.88,
        ("en", "de"): 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)

# Modified upload_and_manage function
def upload_and_manage(file, target_language, process_mode, separate_background_audio): # Added separate_background_audio
    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" # This will be the full extracted audio
        output_video_path = "output_video.mp4"
        voiceover_path = "voiceover.wav"
        translated_json_filepath = "translated_output.json"

        logger.info(f"Using audio path: {audio_path}, output video path: {output_video_path}, voiceover path: {voiceover_path}")

        # Step 1: Segment audio from the uploaded video/audio file
        logger.info("Segmenting audio...")
        # Pass the separate_background_audio boolean from the Gradio input
        temp_audio_for_vad, background_audio_path, speech_segments = segment_audio_from_video(
            file.name, 
            separate_background=separate_background_audio
        )
        if not speech_segments:
            raise Exception("No speech segments detected in the audio.")
        logger.info(f"Audio segmentation completed. Found {len(speech_segments)} segments.")

        # Step 2: Transcribe the segments using ElevenLabs Scribe
        logger.info("Transcribing audio segments...")
        transcription_json, source_language, trans_error = transcribe_segments_with_scribe(temp_audio_for_vad, speech_segments)
        if trans_error:
            raise Exception(f"Transcription failed: {trans_error}")
        logger.info(f"Transcription completed. Detected source language: {source_language}")

        transcription_json_merged = transcription_json
        #post_edit_transcribed_segments(transcription_json, file.name, source_language)

        # Log number of transcribed segments and preview a few
        if isinstance(transcription_json, list):
            logger.info(f"Transcribed {len(transcription_json)} segments.")
            for i, segment in enumerate(transcription_json[:3]):  # preview first 3 lines
                logger.debug(f"[Line {i+1}] {segment}")
        else:
            logger.warning("Transcription output is not a list. Check transcribe_segments_with_scribe output format.")

        # 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, process_mode)

        # New: Save the translated JSON to a file
        with open(translated_json_filepath, "w", encoding="utf-8") as f:
            json.dump(translated_json, f, ensure_ascii=False, indent=4)
        logger.info(f"Translated JSON saved to {translated_json_filepath}")
            
        # 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, background_audio_path = background_audio_path)
        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.")

        if not os.path.isfile(output_video_path):
            logger.error(f"Expected file at {output_video_path} but got a directory.")
            return [], None, "", f"Output video path is invalid."

        if not os.path.isfile(translated_json_filepath):
            logger.error(f"Expected file at {translated_json_filepath} but got a directory.")
            return [], None, "", f"Translated JSON path is invalid."

        return editable_table, output_video_path, translated_json_filepath, elapsed_time_display

    except Exception as e:
        logger.error(f"An error occurred: {str(e)}")
        return [], None, translated_json_filepath, 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)
                
                # New Gradio Checkbox for background audio separation
                separate_background_checkbox = gr.Checkbox(
                    label="Separate Background Audio (Recommended)",
                    value=True, # Default to True
                    interactive=True
                )

                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)
                translated_json_download = gr.File(label="Download Translated JSON", interactive=True) # New: JSON download
            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, translated_json_download, elapsed_time_display]
            )
            submit_button.click(
                upload_and_manage,
                inputs=[file_input, language_input, process_mode, separate_background_checkbox], # Add checkbox as input
                outputs=[editable_table, processed_video_output, translated_json_download, 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()