app.py

import os
import subprocess
import random
import numpy as np
import json
from datetime import timedelta
import tempfile
import gradio as gr
from groq import Groq

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


# llms

MAX_SEED = np.iinfo(np.int32).max

def update_max_tokens(model):
    if model in ["llama3-70b-8192", "llama3-8b-8192", "gemma-7b-it", "gemma2-9b-it"]:
        return gr.update(maximum=8192)
    elif model == "mixtral-8x7b-32768":
        return gr.update(maximum=32768)

def create_history_messages(history):
    history_messages = [{"role": "user", "content": m[0]} for m in history]
    history_messages.extend([{"role": "assistant", "content": m[1]} for m in history])
    return history_messages

def generate_response(prompt, history, model, temperature, max_tokens, top_p, seed):
    messages = create_history_messages(history)
    messages.append({"role": "user", "content": prompt})
    print(messages)

    if seed == 0:
        seed = random.randint(1, MAX_SEED)

    stream = client.chat.completions.create(
        messages=messages,
        model=model,
        temperature=temperature,
        max_tokens=max_tokens,
        top_p=top_p,
        seed=seed,
        stop=None,
        stream=True,
    )

    response = ""
    for chunk in stream:
        delta_content = chunk.choices[0].delta.content
        if delta_content is not None:
            response += delta_content
            yield response

    return response

# speech to text

ALLOWED_FILE_EXTENSIONS = ["mp3", "mp4", "mpeg", "mpga", "m4a", "wav", "webm"]
MAX_FILE_SIZE_MB = 25

LANGUAGE_CODES = {
    "English": "en",
    "Chinese": "zh",
    "German": "de",
    "Spanish": "es",
    "Russian": "ru",
    "Korean": "ko",
    "French": "fr",
    "Japanese": "ja",
    "Portuguese": "pt",
    "Turkish": "tr",
    "Polish": "pl",
    "Catalan": "ca",
    "Dutch": "nl",
    "Arabic": "ar",
    "Swedish": "sv",
    "Italian": "it",
    "Indonesian": "id",
    "Hindi": "hi",
    "Finnish": "fi",
    "Vietnamese": "vi",
    "Hebrew": "he",
    "Ukrainian": "uk",
    "Greek": "el",
    "Malay": "ms",
    "Czech": "cs",
    "Romanian": "ro",
    "Danish": "da",
    "Hungarian": "hu",
    "Tamil": "ta",
    "Norwegian": "no",
    "Thai": "th",
    "Urdu": "ur",
    "Croatian": "hr",
    "Bulgarian": "bg",
    "Lithuanian": "lt",
    "Latin": "la",
    "Māori": "mi",
    "Malayalam": "ml",
    "Welsh": "cy",
    "Slovak": "sk",
    "Telugu": "te",
    "Persian": "fa",
    "Latvian": "lv",
    "Bengali": "bn",
    "Serbian": "sr",
    "Azerbaijani": "az",
    "Slovenian": "sl",
    "Kannada": "kn",
    "Estonian": "et",
    "Macedonian": "mk",
    "Breton": "br",
    "Basque": "eu",
    "Icelandic": "is",
    "Armenian": "hy",
    "Nepali": "ne",
    "Mongolian": "mn",
    "Bosnian": "bs",
    "Kazakh": "kk",
    "Albanian": "sq",
    "Swahili": "sw",
    "Galician": "gl",
    "Marathi": "mr",
    "Panjabi": "pa",
    "Sinhala": "si",
    "Khmer": "km",
    "Shona": "sn",
    "Yoruba": "yo",
    "Somali": "so",
    "Afrikaans": "af",
    "Occitan": "oc",
    "Georgian": "ka",
    "Belarusian": "be",
    "Tajik": "tg",
    "Sindhi": "sd",
    "Gujarati": "gu",
    "Amharic": "am",
    "Yiddish": "yi",
    "Lao": "lo",
    "Uzbek": "uz",
    "Faroese": "fo",
    "Haitian": "ht",
    "Pashto": "ps",
    "Turkmen": "tk",
    "Norwegian Nynorsk": "nn",
    "Maltese": "mt",
    "Sanskrit": "sa",
    "Luxembourgish": "lb",
    "Burmese": "my",
    "Tibetan": "bo",
    "Tagalog": "tl",
    "Malagasy": "mg",
    "Assamese": "as",
    "Tatar": "tt",
    "Hawaiian": "haw",
    "Lingala": "ln",
    "Hausa": "ha",
    "Bashkir": "ba",
    "jw": "jw",
    "Sundanese": "su",
}

# Checks file extension, size, and downsamples if needed.
def check_file(audio_file_path):
    if not audio_file_path:
        return None, gr.Error("Please upload an audio file.")

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

    if file_extension not in ALLOWED_FILE_EXTENSIONS:
        return (
            None,
            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. Maximum allowed: {MAX_FILE_SIZE_MB} MB"
        )

        output_file_path = os.path.splitext(audio_file_path)[0] + "_downsampled.wav"
        try:
            subprocess.run(
                [
                    "ffmpeg",
                    "-i",
                    audio_file_path,
                    "-ar",
                    "16000",
                    "-ac",
                    "1",
                    "-map",
                    "0:a:",
                    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:
                return (
                    None,
                    gr.Error(
                        f"File size still too large after downsampling ({downsampled_size_mb:.2f} MB). Maximum allowed: {MAX_FILE_SIZE_MB} MB"
                    ),
                )

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


def transcribe_audio(audio_file_path, prompt, language, auto_detect_language, model):
    # Check and process the file first
    processed_path, error_message = check_file(audio_file_path)

    # If there's an error during file check
    if error_message:
        return error_message

    with open(processed_path, "rb") as file:
        transcription = client.audio.transcriptions.create(
            file=(os.path.basename(processed_path), file.read()),
            model=model,
            prompt=prompt,
            response_format="text",
            language=None if auto_detect_language else language,
            temperature=0.0,
        )
    return transcription.text


def translate_audio(audio_file_path, prompt, model):
    # Check and process the file first
    processed_path, error_message = check_file(audio_file_path)

    # If there's an error during file check
    if error_message:
        return error_message

    with open(processed_path, "rb") as file:
        translation = client.audio.translations.create(
            file=(os.path.basename(processed_path), file.read()),
            model=model,
            prompt=prompt,
            response_format="text",
            temperature=0.0,
        )
    return translation.text


# subtitles maker

# helper function convert json transcription to srt
from datetime import timedelta

def create_srt_from_text(transcription_text):
    srt_lines = []
    start_time = timedelta(seconds=0)

    # Define a function to calculate the duration based on text length
    def calculate_duration(text):
        words_per_minute = 110
        words = len(text.split())
        duration_seconds = (words / words_per_minute) * 60
        return timedelta(seconds=duration_seconds)

    text_parts = transcription_text.split(".")
    
    for i, text_part in enumerate(text_parts):
        text_part = text_part.strip()
        if text_part:
            duration = calculate_duration(text_part)
            end_time = start_time + duration
            start_timestamp = str(start_time).split('.')[0] + ',' + str(start_time.microseconds // 1000).zfill(3)
            end_timestamp = str(end_time).split('.')[0] + ',' + str(end_time.microseconds // 1000).zfill(3)
            srt_lines.append(f"{i + 1}\n{start_timestamp} --> {end_timestamp}\n{text_part.strip()}\n\n")
            start_time = end_time  # Move to the next time slot
    
    return "".join(srt_lines)


# getting transcription + using helper function + adding subs to video if input is video
def generate_subtitles(audio_file_path, prompt, language, auto_detect_language, model):
    # Check and process the file first
    processed_path, error_message = check_file(audio_file_path)

    # If there's an error during file check
    if error_message:
        return error_message, None, None

    with open(processed_path, "rb") as file:
        transcription_json = client.audio.transcriptions.create(
            file=(os.path.basename(processed_path), file.read()),
            model=model,
            prompt=prompt,
            response_format="json",
            language=None if auto_detect_language else language,  # Conditional language parameter
            temperature=0.0,
        )

    # Convert the Transcription object to a dictionary
    transcription_json = json.loads(transcription_json.to_json())
    transcription_text = transcription_json['text']

    srt_content = create_srt_from_text(transcription_text)

    # Create a temporary file for SRT content    
    with tempfile.NamedTemporaryFile(mode="w", suffix=".srt", delete=False) as temp_srt_file:
        temp_srt_path = temp_srt_file.name
        temp_srt_file.write(srt_content)

    # Generate subtitles and add to video if input is video
    if audio_file_path.lower().endswith((".amp4", ".awebm")):
        try:
            # Use ffmpeg to burn subtitles into the video
            output_file_path = audio_file_path.replace(os.path.splitext(audio_file_path)[1], "_with_subs" + os.path.splitext(audio_file_path)[1])
            subprocess.run(
                [
                    "ffmpeg",
                    "-i",
                    audio_file_path,
                    "-vf",
                    f"subtitles={temp_srt_path}",
                    output_file_path,
                ],
                check=True,
            )
            return temp_srt_path, output_file_path, None
        except subprocess.CalledProcessError as e:
            return None, None, gr.Error(f"Error during subtitle addition: {e}")

    return temp_srt_path, None, None 
    




with gr.Blocks() as demo:
    gr.Markdown(
        """
    # Groq API UI
    Inference by Groq
    Hugging Face Space by [Nick088](https://linktr.ee/Nick088)
    """
    )
    with gr.Tabs():
        with gr.TabItem("select option here:"):
            with gr.Tabs():
                with gr.TabItem("Speech To Text"):
                    gr.Markdown("Speech to Text coming soon!")
                with gr.TabItem("LLMs"):
                    with gr.Column():
                        model = gr.Dropdown(
                            choices=[
                                "llama3-70b-8192",
                                "llama3-8b-8192",
                                "mixtral-8x7b-32768",
                                "gemma-7b-it",
                                "gemma2-9b-it",
                            ],
                            value="llama3-70b-8192",
                            label="Model",
                        )
                        temperature = gr.Slider(
                            minimum=0.0,
                            maximum=1.0,
                            step=0.01,
                            value=0.5,
                            label="Temperature",
                            info="Controls diversity of the generated text. Lower is more deterministic, higher is more creative.",
                        )
                        max_tokens = gr.Slider(
                            minimum=1,
                            maximum=8192,
                            step=1,
                            value=4096,
                            label="Max Tokens",
                            info="The maximum number of tokens that the model can process in a single response.<br>Maximums: 8k for gemma 7b it, gemma2 9b it, llama 7b & 70b, 32k for mixtral 8x7b.",
                        )
                        top_p = gr.Slider(
                            minimum=0.0,
                            maximum=1.0,
                            step=0.01,
                            value=0.5,
                            label="Top P",
                            info="A method of text generation where a model will only consider the most probable next tokens that make up the probability p.",
                        )
                        seed = gr.Number(
                            precision=0, value=42, label="Seed", info="A starting point to initiate generation, use 0 for random"
                        )
                        model.change(update_max_tokens, inputs=[model], outputs=max_tokens)
                        chatbot = gr.ChatInterface(
                            fn=generate_response,                                         
                            chatbot=None,
                            additional_inputs=[
                                model,                       
                                temperature,
                                max_tokens,
                                top_p,
                                seed,  
                            ],
                        )
                        model.change(update_max_tokens, inputs=[model], outputs=max_tokens)

                
                with gr.TabItem("Transcription"):
                    gr.Markdown("Transcript audio from files to text!")
                    with gr.Column():
                        audio_input = gr.File(
                            type="filepath", label="Upload File containing Audio", file_types=[f".{ext}" for ext in ALLOWED_FILE_EXTENSIONS]
                        )
                        model_choice_transcribe = gr.Dropdown(
                            choices=["whisper-large-v3"],  # Only include 'whisper-large-v3'
                            value="whisper-large-v3",
                            label="Model",
                        )
                        transcribe_prompt = gr.Textbox(
                            label="Prompt (Optional)",
                            info="Specify any context or spelling corrections.",
                        )
                        language = gr.Dropdown(
                             choices=[(lang, code) for lang, code in LANGUAGE_CODES.items()],
                             value="en",
                             label="Language",
                        )
                        auto_detect_language = gr.Checkbox(label="Auto Detect Language")
                        transcribe_button = gr.Button("Transcribe")
                    transcription_output = gr.Textbox(label="Transcription")
                    transcribe_button.click(
                        transcribe_audio,
                        inputs=[audio_input, transcribe_prompt, language, auto_detect_language, model_choice_transcribe],
                        outputs=transcription_output,
                    )
                with gr.TabItem("Translation"):
                    gr.Markdown("Transcript audio from files and translate them to English text!")
                    with gr.Column():
                        audio_input_translate = gr.File(
                            type="filepath", label="Upload File containing Audio", file_types=[f".{ext}" for ext in ALLOWED_FILE_EXTENSIONS]
                        )
                        model_choice_translate = gr.Dropdown(
                            choices=["whisper-large-v3"],  # Only include 'whisper-large-v3'
                            value="whisper-large-v3",
                            label="Model",
                        )
                        translate_prompt = gr.Textbox(
                            label="Prompt (Optional)",
                            info="Specify any context or spelling corrections.",
                        )
                        translate_button = gr.Button("Translate")
                    translation_output = gr.Textbox(label="Translation")
                    translate_button.click(
                        translate_audio,
                        inputs=[audio_input_translate, translate_prompt, model_choice_translate],
                        outputs=translation_output,
                    )
                with gr.TabItem("Subtitle Maker"):
                    with gr.Column():
                        audio_input_subtitles = gr.File(
                            label="Upload Audio/Video",
                            file_types=[f".{ext}" for ext in ALLOWED_FILE_EXTENSIONS],
                        )
                        model_choice_subtitles = gr.Dropdown(
                            choices=["whisper-large-v3"],  # Only include 'whisper-large-v3'
                            value="whisper-large-v3",
                            label="Model",
                        )
                        transcribe_prompt_subtitles = gr.Textbox(
                            label="Prompt (Optional)",
                            info="Specify any context or spelling corrections.",
                        )
                        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"
                        )
                        transcribe_button_subtitles = gr.Button("Generate Subtitles")
                        srt_output = gr.File(label="SRT Output File")
                        video_output = gr.File(label="Output Video with Subtitles")
                        transcribe_button_subtitles.click(
                            generate_subtitles,
                            inputs=[
                                audio_input_subtitles,
                                transcribe_prompt_subtitles,
                                language_subtitles,
                                auto_detect_language_subtitles,
                                model_choice_subtitles,
                            ],
                            outputs=[srt_output, video_output, gr.Textbox(label="Error")]
                        )
                
demo.launch()