app.py

import os
import subprocess
import json
from datetime import timedelta
import tempfile
import re
import gradio as gr
import groq
from groq import Groq


# setup groq 

client = Groq(api_key=os.environ.get("Groq_Api_Key"))

def handle_groq_error(e, model_name):
    error_data = e.args[0]

    if isinstance(error_data, str):
        # Use regex to extract the JSON part of the string
        json_match = re.search(r'(\{.*\})', error_data)
        if json_match:
            json_str = json_match.group(1)
            # Ensure the JSON string is well-formed
            json_str = json_str.replace("'", '"')  # Replace single quotes with double quotes
            error_data = json.loads(json_str)

    if isinstance(e, groq.AuthenticationError):
        if isinstance(error_data, dict) and 'error' in error_data and 'message' in error_data['error']:
            error_message = error_data['error']['message']
            raise gr.Error(error_message)
    elif isinstance(e, groq.RateLimitError):
        if isinstance(error_data, dict) and 'error' in error_data and 'message' in error_data['error']:
            error_message = error_data['error']['message']
            error_message = re.sub(r'org_[a-zA-Z0-9]+', 'org_(censored)', error_message) # censor org
            raise gr.Error(error_message)
    else:
        raise gr.Error(f"Error during Groq API call: {e}")


# language codes for subtitle maker

LANGUAGE_CODES = {
    "English": "en",
    "Chinese": "zh",
    "Vietnamese": "vi",
}


# helper functions

def split_audio(input_file_path, chunk_size_mb):
    chunk_size = chunk_size_mb * 1024 * 1024  # Convert MB to bytes
    file_number = 1
    chunks = []
    with open(input_file_path, 'rb') as f:
        chunk = f.read(chunk_size)
        while chunk:
            chunk_name = f"{os.path.splitext(input_file_path)[0]}_part{file_number:03}.mp3" # Pad file number for correct ordering
            with open(chunk_name, 'wb') as chunk_file:
                chunk_file.write(chunk)
            chunks.append(chunk_name)
            file_number += 1
            chunk = f.read(chunk_size)
    return chunks

def merge_audio(chunks, output_file_path):
    with open("temp_list.txt", "w") as f:
        for file in chunks:
            f.write(f"file '{file}'\n")
    try:
        subprocess.run(
            [
                "ffmpeg",
                "-f",
                "concat",
                "-safe", "0",
                "-i",
                "temp_list.txt",
                "-c",
                "copy",
                "-y",
                output_file_path
            ],
            check=True
        )
        os.remove("temp_list.txt")
        for chunk in chunks:
            os.remove(chunk)
    except subprocess.CalledProcessError as e:
        raise gr.Error(f"Error during audio merging: {e}")


# Checks file extension, size, and downsamples or splits if needed.

ALLOWED_FILE_EXTENSIONS = ["mp3", "mp4", "mpeg", "mpga", "m4a", "wav", "webm"]
MAX_FILE_SIZE_MB = 40
CHUNK_SIZE_MB = 40

def check_file(input_file_path):
    if not input_file_path:
        raise gr.Error("Please upload an audio/video file.")

    file_size_mb = os.path.getsize(input_file_path) / (1024 * 1024)
    file_extension = input_file_path.split(".")[-1].lower()

    if file_extension not in ALLOWED_FILE_EXTENSIONS:
        raise gr.Error(f"Invalid file type (.{file_extension}). Allowed types: {', '.join(ALLOWED_FILE_EXTENSIONS)}")

    if file_size_mb > MAX_FILE_SIZE_MB:
        gr.Warning(
            f"File size too large ({file_size_mb:.2f} MB). Attempting to downsample to 16kHz MP3 128kbps. Maximum size allowed: {MAX_FILE_SIZE_MB} MB"
        )

        output_file_path = os.path.splitext(input_file_path)[0] + "_downsampled.mp3"
        try:
            subprocess.run(
                [
                    "ffmpeg",
                    "-i",
                    input_file_path,
                    "-ar",
                    "16000",
                    "-ab",
                    "128k",
                    "-ac",
                    "1",
                    "-f",
                    "mp3",
                    "-y",
                    output_file_path,
                ],
                check=True
            )

            # Check size after downsampling
            downsampled_size_mb = os.path.getsize(output_file_path) / (1024 * 1024)
            if downsampled_size_mb > MAX_FILE_SIZE_MB:
                gr.Warning(f"File still too large after downsampling ({downsampled_size_mb:.2f} MB). Splitting into {CHUNK_SIZE_MB} MB chunks.")
                return split_audio(output_file_path, CHUNK_SIZE_MB), "split"

            return output_file_path, None
        except subprocess.CalledProcessError as e:
            raise gr.Error(f"Error during downsampling: {e}")
    return input_file_path, None


# subtitle maker

def format_time(seconds):
    hours = int(seconds // 3600)
    minutes = int((seconds % 3600) // 60)
    seconds = int(seconds % 60)
    milliseconds = int((seconds % 1) * 1000)

    return f"{hours:02}:{minutes:02}:{seconds:02},{milliseconds:03}"

def json_to_srt(transcription_json):
    srt_lines = []

    for segment in transcription_json:
        start_time = format_time(segment['start'])
        end_time = format_time(segment['end'])
        text = segment['text']

        srt_line = f"{segment['id']+1}\n{start_time} --> {end_time}\n{text}\n"
        srt_lines.append(srt_line)

    return '\n'.join(srt_lines)


def generate_subtitles(input_file, prompt, language, auto_detect_language, model, include_video, font_selection, font_file, font_color, font_size, outline_thickness, outline_color):
    
    input_file_path = input_file

    processed_path, split_status = check_file(input_file_path)
    full_srt_content = ""
    total_duration = 0
    segment_id_offset = 0

    if split_status == "split":
        srt_chunks = []
        video_chunks = []
        for i, chunk_path in enumerate(processed_path):
            try:
                with open(chunk_path, "rb") as file:
                    transcription_json_response = client.audio.transcriptions.create(
                        file=(os.path.basename(chunk_path), file.read()),
                        model=model,
                        prompt=prompt,
                        response_format="verbose_json",
                        language=None if auto_detect_language else language,
                        temperature=0.0,
                    )
                transcription_json = transcription_json_response.segments

                # Adjust timestamps and segment IDs
                for segment in transcription_json:
                    segment['start'] += total_duration
                    segment['end'] += total_duration
                    segment['id'] += segment_id_offset
                segment_id_offset += len(transcription_json)
                total_duration += transcription_json[-1]['end']  # Update total duration

                srt_content = json_to_srt(transcription_json)
                full_srt_content += srt_content
                temp_srt_path = f"{os.path.splitext(chunk_path)[0]}.srt"
                with open(temp_srt_path, "w", encoding="utf-8") as temp_srt_file:
                    temp_srt_file.write(srt_content)
                    temp_srt_file.write("\n") # add a new line at the end of the srt chunk file to fix format when merged
                srt_chunks.append(temp_srt_path)

                if include_video and input_file_path.lower().endswith((".mp4", ".webm")):
                    try:
                        output_file_path = chunk_path.replace(os.path.splitext(chunk_path)[1], "_with_subs" + os.path.splitext(chunk_path)[1])
                        # Handle font selection
                        if font_selection == "Custom Font File" and font_file:
                            font_name = os.path.splitext(os.path.basename(font_file.name))[0]  # Get font filename without extension
                            font_dir = os.path.dirname(font_file.name)  # Get font directory path
                        elif font_selection == "Custom Font File" and not font_file:
                            font_name = None  # Let FFmpeg use its default Arial
                            font_dir = None  # No font directory
                            gr.Warning(f"You want to use a Custom Font File, but uploaded none. Using the default Arial font.")
                        elif font_selection == "Arial":
                            font_name = None  # Let FFmpeg use its default Arial
                            font_dir = None  # No font directory
                            
                        # FFmpeg command
                        subprocess.run(
                            [
                                "ffmpeg",
                                "-y",
                                "-i",
                                chunk_path,
                                "-vf",
                                f"subtitles={temp_srt_path}:fontsdir={font_dir}:force_style='Fontname={font_name},Fontsize={int(font_size)},PrimaryColour=&H{font_color[1:]}&,OutlineColour=&H{outline_color[1:]}&,BorderStyle={int(outline_thickness)},Outline=1'",
                                "-preset", "fast",
                                output_file_path,
                            ],
                            check=True,
                        )
                        video_chunks.append(output_file_path) 
                    except subprocess.CalledProcessError as e:
                        raise gr.Error(f"Error during subtitle addition: {e}")     
                elif include_video and not input_file_path.lower().endswith((".mp4", ".webm")):
                    gr.Warning(f"You have checked on the 'Include Video with Subtitles', but the input file {input_file_path} isn't a video (.mp4 or .webm). Returning only the SRT File.", duration=15)
            except groq.AuthenticationError as e:
                handle_groq_error(e, model)
            except groq.RateLimitError as e:
                handle_groq_error(e, model)
                gr.Warning(f"API limit reached during chunk {i+1}. Returning processed chunks only.")
                if srt_chunks and video_chunks:
                    merge_audio(video_chunks, 'merged_output_video.mp4')
                    with open('merged_output.srt', 'w', encoding="utf-8") as outfile:
                        for chunk_srt in srt_chunks:
                            with open(chunk_srt, 'r', encoding="utf-8") as infile:
                                outfile.write(infile.read())
                    return 'merged_output.srt', 'merged_output_video.mp4'
                else:
                    raise gr.Error("Subtitle generation failed due to API limits.")

        # Merge SRT chunks
        final_srt_path = os.path.splitext(input_file_path)[0] + "_final.srt"
        with open(final_srt_path, 'w', encoding="utf-8") as outfile:
            for chunk_srt in srt_chunks:
                with open(chunk_srt, 'r', encoding="utf-8") as infile:
                    outfile.write(infile.read())

        # Merge video chunks
        if video_chunks:
            merge_audio(video_chunks, 'merged_output_video.mp4')
            return final_srt_path, 'merged_output_video.mp4'
        else:
            return final_srt_path, None

    else:  # Single file processing (no splitting)
        try:
            with open(processed_path, "rb") as file:
                transcription_json_response = client.audio.transcriptions.create(
                    file=(os.path.basename(processed_path), file.read()),
                    model=model,
                    prompt=prompt,
                    response_format="verbose_json",
                    language=None if auto_detect_language else language,
                    temperature=0.0,
                )
            transcription_json = transcription_json_response.segments

            srt_content = json_to_srt(transcription_json)
            temp_srt_path = os.path.splitext(input_file_path)[0] + ".srt"
            with open(temp_srt_path, "w", encoding="utf-8") as temp_srt_file:
                temp_srt_file.write(srt_content)

            if include_video and input_file_path.lower().endswith((".mp4", ".webm")):
                try:
                    output_file_path = input_file_path.replace(
                        os.path.splitext(input_file_path)[1], "_with_subs" + os.path.splitext(input_file_path)[1]
                    )
                    # Handle font selection
                    if font_selection == "Custom Font File" and font_file:
                        font_name = os.path.splitext(os.path.basename(font_file.name))[0]  # Get font filename without extension
                        font_dir = os.path.dirname(font_file.name)  # Get font directory path
                    elif font_selection == "Custom Font File" and not font_file:
                        font_name = None  # Let FFmpeg use its default Arial
                        font_dir = None  # No font directory
                        gr.Warning(f"You want to use a Custom Font File, but uploaded none. Using the default Arial font.")
                    elif font_selection == "Arial":
                        font_name = None  # Let FFmpeg use its default Arial
                        font_dir = None  # No font directory

                    # FFmpeg command
                    subprocess.run(
                        [
                            "ffmpeg",
                            "-y",
                            "-i",
                            input_file_path,
                            "-vf",
                            f"subtitles={temp_srt_path}:fontsdir={font_dir}:force_style='FontName={font_name},Fontsize={int(font_size)},PrimaryColour=&H{font_color[1:]}&,OutlineColour=&H{outline_color[1:]}&,BorderStyle={int(outline_thickness)},Outline=1'",
                            "-preset", "fast",
                            output_file_path,
                        ],
                        check=True,
                    )
                    return temp_srt_path, output_file_path
                except subprocess.CalledProcessError as e:
                    raise gr.Error(f"Error during subtitle addition: {e}")
            elif include_video and not input_file_path.lower().endswith((".mp4", ".webm")):
                gr.Warning(f"You have checked on the 'Include Video with Subtitles', but the input file {input_file_path} isn't a video (.mp4 or .webm). Returning only the SRT File.", duration=15)
            
            return temp_srt_path, None
        except groq.AuthenticationError as e:
            handle_groq_error(e, model)
        except groq.RateLimitError as e:
            handle_groq_error(e, model)
        except ValueError as e:
            raise gr.Error(f"Error creating SRT file: {e}")


theme = gr.themes.Soft(
    primary_hue="sky",
    secondary_hue="blue",
    neutral_hue="neutral"
).set(
    border_color_primary='*neutral_300',
    block_border_width='1px',
    block_border_width_dark='1px',
    block_title_border_color='*secondary_100',
    block_title_border_color_dark='*secondary_200',
    input_background_fill_focus='*secondary_300',
    input_border_color='*border_color_primary',
    input_border_color_focus='*secondary_500',
    input_border_width='1px',
    input_border_width_dark='1px',
    slider_color='*secondary_500',
    slider_color_dark='*secondary_600'
)

css = """
.gradio-container{max-width: 1400px !important}
h1{text-align:center}
.extra-option {
    display: none;
}
.extra-option.visible {
    display: block;
}
"""



with gr.Blocks(theme=theme, css=css) as interface:
    gr.Markdown(
        """
    # Subtitle 
    """
    )

    with gr.Column():
        # Input components
        input_file = gr.File(label="Upload Audio/Video", file_types=[f".{ext}" for ext in ALLOWED_FILE_EXTENSIONS], visible=True)

    # Model and options
    model_choice_subtitles = gr.Dropdown(choices=["whisper-large-v3", "whisper-large-v3-turbo", "distil-whisper-large-v3-en"], value="whisper-large-v3-turbo", label="Audio Speech Recogition (ASR) Model", info="'whisper-large-v3' = Multilingual high quality, 'whisper-large-v3-turbo' = Multilingual fast with minimal impact on quality, good balance, 'distil-whisper-large-v3-en' = English only, fastest with also slight impact on quality")
    transcribe_prompt_subtitles = gr.Textbox(label="Prompt (Optional)", info="Specify any context or spelling corrections.")
    with gr.Row():
        language_subtitles = gr.Dropdown(choices=[(lang, code) for lang, code in LANGUAGE_CODES.items()], value="en", label="Language")
        auto_detect_language_subtitles = gr.Checkbox(label="Auto Detect Language")

    # Generate button
    transcribe_button_subtitles = gr.Button("Generate Subtitles")

    # Output and settings
    include_video_option = gr.Checkbox(label="Include Video with Subtitles")
    gr.Markdown("The SubText Rip (SRT) File, contains the subtitles, you can upload this to any video editing app for adding the subs to your video and also modify/stilyze them")
    srt_output = gr.File(label="SRT Output File")
    show_subtitle_settings = gr.Checkbox(label="Show Subtitle Video Settings", visible=False)
    with gr.Row(visible=False) as subtitle_video_settings:
        with gr.Column():
            font_selection = gr.Radio(["Arial", "Custom Font File"], value="Arial", label="Font Selection", info="Select what font to use")
            font_file = gr.File(label="Upload Font File (TTF or OTF)", file_types=[".ttf", ".otf"], visible=False)
        font_color = gr.ColorPicker(label="Font Color", value="#FFFFFF")
        font_size = gr.Slider(label="Font Size (in pixels)", minimum=10, maximum=60, value=24, step=1)
        outline_thickness = gr.Slider(label="Outline Thickness", minimum=0, maximum=5, value=1, step=1)
        outline_color = gr.ColorPicker(label="Outline Color", value="#000000")

    
    video_output = gr.Video(label="Output Video with Subtitles", visible=False)


    # Event bindings
    
    # show video output
    include_video_option.change(lambda include_video: gr.update(visible=include_video), inputs=[include_video_option], outputs=[video_output])
    # show video output subs settings checkbox
    include_video_option.change(lambda include_video: gr.update(visible=include_video), inputs=[include_video_option], outputs=[show_subtitle_settings])
    # show video output subs settings
    show_subtitle_settings.change(lambda show: gr.update(visible=show), inputs=[show_subtitle_settings], outputs=[subtitle_video_settings])
    # uncheck show subtitle settings checkbox if include video is unchecked (to make the output subs settings not visible)
    show_subtitle_settings.change(lambda show, include_video: gr.update(visible=show and include_video), inputs=[show_subtitle_settings, include_video_option], outputs=[show_subtitle_settings])
    # show custom font file selection
    font_selection.change(lambda font_selection: gr.update(visible=font_selection == "Custom Font File"), inputs=[font_selection], outputs=[font_file])
    
    # Update language dropdown based on model selection
    def update_language_options(model):
        if model == "distil-whisper-large-v3-en":
            return gr.update(choices=[("English", "en")], value="en", interactive=False)
        else:
            return gr.update(choices=[(lang, code) for lang, code in LANGUAGE_CODES.items()], value="en", interactive=True)

    model_choice_subtitles.change(fn=update_language_options, inputs=[model_choice_subtitles], outputs=[language_subtitles])

    # Modified generate subtitles event
    transcribe_button_subtitles.click(
        fn=generate_subtitles,
        inputs=[
            input_file,
            transcribe_prompt_subtitles,
            language_subtitles,
            auto_detect_language_subtitles,
            model_choice_subtitles,
            include_video_option,
            font_selection,
            font_file,
            font_color,
            font_size,
            outline_thickness,
            outline_color,
        ],
        outputs=[srt_output, video_output],
    )

interface.launch(share=True)