# Initalize a pipeline
from kokoro import KPipeline
# from IPython.display import display, Audio
# import soundfile as sf
import os
from huggingface_hub import list_repo_files
import uuid
import re 
import gradio as gr


#translate langauge 
from deep_translator import GoogleTranslator
def bulk_translate(text, target_language, chunk_size=500):
    language_map_local = {
    "American English": "en",  
    "British English": "en",  
    "Hindi": "hi",
    "Spanish": "es",
    "French": "fr",
    "Italian": "it",
    "Brazilian Portuguese": "pt",
    "Japanese": "ja",
    "Mandarin Chinese": "zh-CN"
    }
    # lang_code = GoogleTranslator().get_supported_languages(as_dict=True).get(target_language.lower())
    lang_code=language_map_local[target_language]
    sentences = re.split(r'(?<=[.!?])\s+', text)  # Split text into sentences
    chunks = []
    current_chunk = ""

    for sentence in sentences:
        if len(current_chunk) + len(sentence) <= chunk_size:
            current_chunk += " " + sentence
        else:
            chunks.append(current_chunk.strip())
            current_chunk = sentence

    if current_chunk:
        chunks.append(current_chunk.strip())

    translated_chunks = [GoogleTranslator(target=lang_code).translate(chunk) for chunk in chunks]
    result=" ".join(translated_chunks)
    return result.strip()
    
# Language mapping dictionary
language_map = {
    "American English": "a",
    "British English": "b",
    "Hindi": "h",
    "Spanish": "e",
    "French": "f",
    "Italian": "i",
    "Brazilian Portuguese": "p",
    "Japanese": "j",
    "Mandarin Chinese": "z"
}


def update_pipeline(Language):
    """ Updates the pipeline only if the language has changed. """
    global pipeline, last_used_language
    # Get language code, default to 'a' if not found
    new_lang = language_map.get(Language, "a")

    # Only update if the language is different
    if new_lang != last_used_language:
        pipeline = KPipeline(lang_code=new_lang)
        last_used_language = new_lang 
        try:
            pipeline = KPipeline(lang_code=new_lang)
            last_used_language = new_lang  # Update last used language
        except Exception as e:
            gr.Warning(f"Make sure the input text is in {Language}",duration=10)
            gr.Warning(f"Fallback to English Language",duration=5)
            pipeline = KPipeline(lang_code="a")  # Fallback to English
            last_used_language = "a"



def get_voice_names(repo_id):
    """Fetches and returns a list of voice names (without extensions) from the given Hugging Face repository."""
    return [os.path.splitext(file.replace("voices/", ""))[0] for file in list_repo_files(repo_id) if file.startswith("voices/")]

def create_audio_dir():
    """Creates the 'kokoro_audio' directory in the root folder if it doesn't exist."""
    root_dir = os.getcwd()  # Use current working directory instead of __file__
    audio_dir = os.path.join(root_dir, "kokoro_audio")

    if not os.path.exists(audio_dir):
        os.makedirs(audio_dir)
        print(f"Created directory: {audio_dir}")
    else:
        print(f"Directory already exists: {audio_dir}")
    return audio_dir

import re

def clean_text(text):
    # Define replacement rules
    replacements = {
        "–": " ",  # Replace en-dash with space
        "-": " ",  # Replace hyphen with space
        "**": " ", # Replace double asterisks with space
        "*": " ",  # Replace single asterisk with space
        "#": " ",  # Replace hash with space
    }

    # Apply replacements
    for old, new in replacements.items():
        text = text.replace(old, new)

    # Remove emojis using regex (covering wide range of Unicode characters)
    emoji_pattern = re.compile(
        r'[\U0001F600-\U0001F64F]|'  # Emoticons
        r'[\U0001F300-\U0001F5FF]|'  # Miscellaneous symbols and pictographs
        r'[\U0001F680-\U0001F6FF]|'  # Transport and map symbols
        r'[\U0001F700-\U0001F77F]|'  # Alchemical symbols
        r'[\U0001F780-\U0001F7FF]|'  # Geometric shapes extended
        r'[\U0001F800-\U0001F8FF]|'  # Supplemental arrows-C
        r'[\U0001F900-\U0001F9FF]|'  # Supplemental symbols and pictographs
        r'[\U0001FA00-\U0001FA6F]|'  # Chess symbols
        r'[\U0001FA70-\U0001FAFF]|'  # Symbols and pictographs extended-A
        r'[\U00002702-\U000027B0]|'  # Dingbats
        r'[\U0001F1E0-\U0001F1FF]'   # Flags (iOS)
        r'', flags=re.UNICODE)
  
    text = emoji_pattern.sub(r'', text)

    # Remove multiple spaces and extra line breaks
    text = re.sub(r'\s+', ' ', text).strip()

    return text

def tts_file_name(text,language):
    global temp_folder
    # Remove all non-alphabetic characters and convert to lowercase
    text = re.sub(r'[^a-zA-Z\s]', '', text)  # Retain only alphabets and spaces
    text = text.lower().strip()             # Convert to lowercase and strip leading/trailing spaces
    text = text.replace(" ", "_")           # Replace spaces with underscores
    language=language.replace(" ", "_").strip()   
    # Truncate or handle empty text
    truncated_text = text[:20] if len(text) > 20 else text if len(text) > 0 else language
    
    # Generate a random string for uniqueness
    random_string = uuid.uuid4().hex[:8].upper()
    
    # Construct the file name
    file_name = f"{temp_folder}/{truncated_text}_{random_string}.wav"
    return file_name


# import soundfile as sf
import numpy as np
import wave
from pydub import AudioSegment
from pydub.silence import split_on_silence

def remove_silence_function(file_path,minimum_silence=50):
    # Extract file name and format from the provided path
    output_path = file_path.replace(".wav", "_no_silence.wav")
    audio_format = "wav"
    # Reading and splitting the audio file into chunks
    sound = AudioSegment.from_file(file_path, format=audio_format)
    audio_chunks = split_on_silence(sound,
                                    min_silence_len=100,
                                    silence_thresh=-45,
                                    keep_silence=minimum_silence) 
    # Putting the file back together
    combined = AudioSegment.empty()
    for chunk in audio_chunks:
        combined += chunk
    combined.export(output_path, format=audio_format)
    return output_path
def generate_and_save_audio(text, Language="American English",voice="af_bella", speed=1,remove_silence=False,keep_silence_up_to=0.05):
    text=clean_text(text)
    update_pipeline(Language)
    generator = pipeline(text, voice=voice, speed=speed, split_pattern=r'\n+')
    save_path=tts_file_name(text,Language)
    # Open the WAV file for writing
    timestamps={}
    with wave.open(save_path, 'wb') as wav_file:
        # Set the WAV file parameters
        wav_file.setnchannels(1)  # Mono audio
        wav_file.setsampwidth(2)  # 2 bytes per sample (16-bit audio)
        wav_file.setframerate(24000)  # Sample rate
        for i, result in enumerate(generator):
          gs = result.graphemes # str
        #   print(f"\n{i}: {gs}")
          ps = result.phonemes # str
          # audio = result.audio.cpu().numpy()
          audio = result.audio
          tokens = result.tokens # List[en.MToken]
          timestamps[i]={"text":gs,"words":[]}
          if Language in ["American English", "British English"]:
            for t in tokens:
                # print(t.text, repr(t.whitespace), t.start_ts, t.end_ts)
                timestamps[i]["words"].append({"word":t.text,"start":t.start_ts,"end":t.end_ts})
          audio_np = audio.numpy()  # Convert Tensor to NumPy array
          audio_int16 = (audio_np * 32767).astype(np.int16)  # Scale to 16-bit range
          audio_bytes = audio_int16.tobytes()  # Convert to bytes
          # Write the audio chunk to the WAV file
          wav_file.writeframes(audio_bytes)
    if remove_silence:            
      keep_silence = int(keep_silence_up_to * 1000)
      new_wave_file=remove_silence_function(save_path,minimum_silence=keep_silence)
      return new_wave_file,timestamps
    return save_path,timestamps

def adjust_timestamps(timestamp_dict):
    adjusted_timestamps = []
    last_end_time = 0  # Tracks the last word's end time

    for segment_id in sorted(timestamp_dict.keys()):
        segment = timestamp_dict[segment_id]
        words = segment["words"]

        for word_entry in words:
            # Skip word entries with start or end time as None or 0
            if word_entry["start"] in [None, 0] and word_entry["end"] in [None, 0]:
                continue

            # Fill in None values with the last valid timestamp or use 0 as default
            word_start = word_entry["start"] if word_entry["start"] is not None else last_end_time
            word_end = word_entry["end"] if word_entry["end"] is not None else word_start  # Use word_start if end is None

            new_start = word_start + last_end_time
            new_end = word_end + last_end_time

            adjusted_timestamps.append({
                "word": word_entry["word"],
                "start": round(new_start, 3),
                "end": round(new_end, 3)
            })

        # Update last_end_time to the last word's end time in this segment
        if words:
            last_end_time = adjusted_timestamps[-1]["end"]

    return adjusted_timestamps


import string

def write_word_srt(word_level_timestamps, output_file="word.srt", skip_punctuation=True):
    with open(output_file, "w", encoding="utf-8") as f:
        index = 1  # Track subtitle numbering separately

        for entry in word_level_timestamps:
            word = entry["word"]
            
            # Skip punctuation if enabled
            if skip_punctuation and all(char in string.punctuation for char in word):
                continue

            start_time = entry["start"]
            end_time = entry["end"]

            # Convert seconds to SRT time format (HH:MM:SS,mmm)
            def format_srt_time(seconds):
                hours = int(seconds // 3600)
                minutes = int((seconds % 3600) // 60)
                sec = int(seconds % 60)
                millisec = int((seconds % 1) * 1000)
                return f"{hours:02}:{minutes:02}:{sec:02},{millisec:03}"

            start_srt = format_srt_time(start_time)
            end_srt = format_srt_time(end_time)

            # Write entry to SRT file
            f.write(f"{index}\n{start_srt} --> {end_srt}\n{word}\n\n")
            index += 1  # Increment subtitle number

import string

def write_sentence_srt(word_level_timestamps, output_file="subtitles.srt", max_words=8, min_pause=0.1):
    subtitles = []  # Stores subtitle blocks
    subtitle_words = []  # Temporary list for words in the current subtitle
    start_time = None  # Tracks start time of current subtitle

    remove_punctuation = ['"',"—"]  # Add punctuations to remove if needed

    for i, entry in enumerate(word_level_timestamps):
        word = entry["word"]
        word_start = entry["start"]
        word_end = entry["end"]

        # Skip selected punctuation from remove_punctuation list
        if word in remove_punctuation:
            continue  

        # Attach punctuation to the previous word
        if word in string.punctuation:
            if subtitle_words:
                subtitle_words[-1] = (subtitle_words[-1][0] + word, subtitle_words[-1][1])
            continue  

        # Start a new subtitle block if needed
        if start_time is None:
            start_time = word_start

        # Calculate pause duration if this is not the first word
        if subtitle_words:
            last_word_end = subtitle_words[-1][1]
            pause_duration = word_start - last_word_end
        else:
            pause_duration = 0

        # **NEW FIX:** If pause is too long, create a new subtitle but ensure continuity
        if (word.endswith(('.', '!', '?')) and len(subtitle_words) >= 5) or len(subtitle_words) >= max_words or pause_duration > min_pause:
            end_time = subtitle_words[-1][1]  # Use last word's end time
            subtitle_text = " ".join(w[0] for w in subtitle_words)
            subtitles.append((start_time, end_time, subtitle_text))

            # Reset for the next subtitle, but **ensure continuity**
            subtitle_words = [(word, word_end)]  # **Carry the current word to avoid delay**
            start_time = word_start  # **Start at the current word, not None**

            continue  # Avoid adding the word twice

        # Add the current word to the subtitle
        subtitle_words.append((word, word_end))

    # Ensure last subtitle is added if anything remains
    if subtitle_words:
        end_time = subtitle_words[-1][1]
        subtitle_text = " ".join(w[0] for w in subtitle_words)
        subtitles.append((start_time, end_time, subtitle_text))

    # Function to format SRT timestamps
    def format_srt_time(seconds):
        hours = int(seconds // 3600)
        minutes = int((seconds % 3600) // 60)
        sec = int(seconds % 60)
        millisec = int((seconds % 1) * 1000)
        return f"{hours:02}:{minutes:02}:{sec:02},{millisec:03}"

    # Write subtitles to SRT file
    with open(output_file, "w", encoding="utf-8") as f:
        for i, (start, end, text) in enumerate(subtitles, start=1):
            f.write(f"{i}\n{format_srt_time(start)} --> {format_srt_time(end)}\n{text}\n\n")

    # print(f"SRT file '{output_file}' created successfully!")


import json
import re

def fix_punctuation(text):
    # Remove spaces before punctuation marks (., ?, !, ,)
    text = re.sub(r'\s([.,?!])', r'\1', text)
    
    # Handle quotation marks: remove spaces before and after them
    text = text.replace('" ', '"')
    text = text.replace(' "', '"')
    text = text.replace('" ', '"')
    
    # Track quotation marks to add space after closing quotes
    track = 0
    result = []
    
    for index, char in enumerate(text):
        if char == '"':
            track += 1
            result.append(char)
            # If it's a closing quote (even number of quotes), add a space after it
            if track % 2 == 0:
                result.append(' ')
        else:
            result.append(char)
    text=''.join(result)
    return text.strip()



def make_json(word_timestamps, json_file_name):
    data = {}
    temp = []
    inside_quote = False  # Track if we are inside a quoted sentence
    start_time = word_timestamps[0]['start']  # Initialize with the first word's start time
    end_time = word_timestamps[0]['end']  # Initialize with the first word's end time
    words_in_sentence = []
    sentence_id = 0  # Initialize sentence ID

    # Process each word in word_timestamps
    for i, word_data in enumerate(word_timestamps):
        word = word_data['word']
        word_start = word_data['start']
        word_end = word_data['end']

        # Collect word info for JSON
        words_in_sentence.append({'word': word, 'start': word_start, 'end': word_end})

        # Update the end_time for the sentence based on the current word
        end_time = word_end

        # Properly handle opening and closing quotation marks
        if word == '"':
            if inside_quote:
                temp[-1] += '"'  # Attach closing quote to the last word
            else:
                temp.append('"')  # Keep opening quote as a separate entry
            inside_quote = not inside_quote  # Toggle inside_quote state
        else:
            temp.append(word)

        # Check if this is a sentence-ending punctuation
        if word.endswith(('.', '?', '!')) and not inside_quote:
            # Ensure the next word is NOT a dialogue tag before finalizing the sentence
            if i + 1 < len(word_timestamps):
                next_word = word_timestamps[i + 1]['word']
                if next_word[0].islower():  # Likely a dialogue tag like "he said"
                    continue  # Do not break the sentence yet

            # Store the full sentence for JSON and reset word collection for next sentence
            sentence = " ".join(temp)
            sentence = fix_punctuation(sentence)  # Fix punctuation in the sentence
            data[sentence_id] = {
                'text': sentence,
                'duration': end_time - start_time,
                'start': start_time,
                'end': end_time,
                'words': words_in_sentence
            }

            # Reset for the next sentence
            temp = []
            words_in_sentence = []
            start_time = word_data['start']  # Update the start time for the next sentence
            sentence_id += 1  # Increment sentence ID

    # Handle any remaining words if necessary
    if temp:
        sentence = " ".join(temp)
        sentence = fix_punctuation(sentence)  # Fix punctuation in the sentence
        data[sentence_id] = {
            'text': sentence,
            'duration': end_time - start_time,
            'start': start_time,
            'end': end_time,
            'words': words_in_sentence
        }

    # Write data to JSON file
    with open(json_file_name, 'w') as json_file:
        json.dump(data, json_file, indent=4)
    return json_file_name




import os

def modify_filename(save_path: str, prefix: str = ""):
    directory, filename = os.path.split(save_path)
    name, ext = os.path.splitext(filename)
    new_filename = f"{prefix}{name}{ext}"
    return os.path.join(directory, new_filename)
import shutil
def save_current_data():
    if os.path.exists("./last"):
        shutil.rmtree("./last")
    os.makedirs("./last",exist_ok=True)
    
     
def KOKORO_TTS_API(text, Language="American English",voice="af_bella", speed=1,translate_text=False,remove_silence=False,keep_silence_up_to=0.05):
    if translate_text:    
        text=bulk_translate(text, Language, chunk_size=500)
    save_path,timestamps=generate_and_save_audio(text=text, Language=Language,voice=voice, speed=speed,remove_silence=remove_silence,keep_silence_up_to=keep_silence_up_to)
    if remove_silence==False:
        if Language in ["American English", "British English"]:
            word_level_timestamps=adjust_timestamps(timestamps)
            word_level_srt = modify_filename(save_path.replace(".wav", ".srt"), prefix="word_level_")
            normal_srt = modify_filename(save_path.replace(".wav", ".srt"), prefix="sentence_")
            json_file = modify_filename(save_path.replace(".wav", ".json"), prefix="duration_")
            write_word_srt(word_level_timestamps, output_file=word_level_srt, skip_punctuation=True)
            write_sentence_srt(word_level_timestamps, output_file=normal_srt, min_pause=0.01)
            make_json(word_level_timestamps, json_file)
            save_current_data()
            shutil.copy(save_path, "./last/")
            shutil.copy(word_level_srt, "./last/")
            shutil.copy(normal_srt, "./last/")
            shutil.copy(json_file, "./last/")
            return save_path,save_path,word_level_srt,normal_srt,json_file
    return save_path,save_path,None,None,None    
    
    



def ui():
    def toggle_autoplay(autoplay):
        return gr.Audio(interactive=False, label='Output Audio', autoplay=autoplay)

    # Define examples in the format you mentioned
    dummy_examples = [
        ["Hey, y'all, let’s grab some coffee and catch up!", "American English", "af_bella"],
        ["I'd like a large coffee, please.", "British English", "bf_isabella"],
        ["नमस्ते, कैसे हो?", "Hindi", "hf_alpha"],
        ["Hola, ¿cómo estás?", "Spanish", "ef_dora"],
        ["Bonjour, comment ça va?", "French", "ff_siwis"],
        ["Ciao, come stai?", "Italian", "if_sara"],
        ["Olá, como você está?", "Brazilian Portuguese", "pf_dora"],
        ["こんにちは、お元気ですか？", "Japanese", "jf_nezumi"],
        ["你好，你怎么样?", "Mandarin Chinese", "zf_xiaoni"]
    ]
    
    with gr.Blocks() as demo:
        # gr.Markdown("<center><h1 style='font-size: 40px;'>KOKORO TTS</h1></center>")  # Larger title with CSS
        gr.Markdown("[Install on Your Local System](https://github.com/NeuralFalconYT/kokoro_v1)")
        lang_list = ['American English', 'British English', 'Hindi', 'Spanish', 'French', 'Italian', 'Brazilian Portuguese', 'Japanese', 'Mandarin Chinese']
        voice_names = get_voice_names("hexgrad/Kokoro-82M")

        with gr.Row():
            with gr.Column():
                text = gr.Textbox(label='📝 Enter Text', lines=3)
                
                with gr.Row():
                    language_name = gr.Dropdown(lang_list, label="🌍 Select Language", value=lang_list[0])

                with gr.Row():
                    voice_name = gr.Dropdown(voice_names, label="🎙️ Choose VoicePack", value='af_heart')#voice_names[0])

                with gr.Row():
                    generate_btn = gr.Button('🚀 Generate', variant='primary')

                with gr.Accordion('🎛️ Audio Settings', open=False):
                    speed = gr.Slider(minimum=0.25, maximum=2, value=1, step=0.1, label='⚡️Speed', info='Adjust the speaking speed')
                    translate_text = gr.Checkbox(value=False, label='🌐 Translate Text to Selected Language')
                    remove_silence = gr.Checkbox(value=False, label='✂️ Remove Silence From TTS')

            with gr.Column():
                audio = gr.Audio(interactive=False, label='🔊 Output Audio', autoplay=True)
                audio_file = gr.File(label='📥 Download Audio')
                # word_level_srt_file = gr.File(label='Download Word-Level SRT')
                # srt_file = gr.File(label='Download Sentence-Level SRT')
                # sentence_duration_file = gr.File(label='Download Sentence Duration JSON')
                with gr.Accordion('🎬 Autoplay, Subtitle, Timestamp', open=False):
                    autoplay = gr.Checkbox(value=True, label='▶️ Autoplay')
                    autoplay.change(toggle_autoplay, inputs=[autoplay], outputs=[audio])
                    word_level_srt_file = gr.File(label='📝 Download Word-Level SRT')
                    srt_file = gr.File(label='📜 Download Sentence-Level SRT')
                    sentence_duration_file = gr.File(label='⏳ Download Sentence Timestamp JSON')

        text.submit(KOKORO_TTS_API, inputs=[text, language_name, voice_name, speed,translate_text, remove_silence], outputs=[audio, audio_file,word_level_srt_file,srt_file,sentence_duration_file])
        generate_btn.click(KOKORO_TTS_API, inputs=[text, language_name, voice_name, speed,translate_text, remove_silence], outputs=[audio, audio_file,word_level_srt_file,srt_file,sentence_duration_file])

        # Add examples to the interface
        gr.Examples(examples=dummy_examples, inputs=[text, language_name, voice_name])

    return demo

def tutorial():
    # Markdown explanation for language code
    explanation = """
    ## Language Code Explanation:
    Example: `'af_bella'` 
    - **'a'** stands for **American English**.
    - **'f_'** stands for **Female** (If it were 'm_', it would mean Male).
    - **'bella'** refers to the specific voice.

    The first character in the voice code stands for the language:
    - **"a"**: American English
    - **"b"**: British English
    - **"h"**: Hindi
    - **"e"**: Spanish
    - **"f"**: French
    - **"i"**: Italian
    - **"p"**: Brazilian Portuguese
    - **"j"**: Japanese
    - **"z"**: Mandarin Chinese

    The second character stands for gender:
    - **"f_"**: Female
    - **"m_"**: Male
    """
    with gr.Blocks() as demo2:
        gr.Markdown("[Install on Your Local System](https://github.com/NeuralFalconYT/kokoro_v1)")
        gr.Markdown(explanation)  # Display the explanation
    return demo2



import click
@click.command()
@click.option("--debug", is_flag=True, default=False, help="Enable debug mode.")
@click.option("--share", is_flag=True, default=False, help="Enable sharing of the interface.")
def main(debug, share):
    demo1 = ui()
    demo2 = tutorial()
    demo = gr.TabbedInterface([demo1, demo2],["Multilingual TTS","VoicePack Explanation"],title="Kokoro TTS")#,theme='JohnSmith9982/small_and_pretty')
    demo.queue().launch(debug=debug, share=share)
    # demo.queue().launch(debug=debug, share=share,server_port=9000)
    #Run on local network
    # laptop_ip="192.168.0.30"
    # port=8080
    # demo.queue().launch(debug=debug, share=share,server_name=laptop_ip,server_port=port)



# Initialize default pipeline
last_used_language = "a"
pipeline = KPipeline(lang_code=last_used_language)
temp_folder = create_audio_dir()
if __name__ == "__main__":
    main()