app.py

import gradio as gr
import whisper
import os
from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
from docx import Document
from reportlab.pdfgen import canvas
from reportlab.pdfbase.ttfonts import TTFont
from reportlab.pdfbase import pdfmetrics
from reportlab.lib.pagesizes import A4
import arabic_reshaper
from bidi.algorithm import get_display
from pptx import Presentation
import subprocess
import shlex

# Define available Whisper models
whisper_models = {
    "Tiny (Fast, Less Accurate)": "tiny",
    "Base (Medium Speed, Medium Accuracy)": "base",
    "Small (Good Speed, Good Accuracy)": "small",
    "Medium (Slow, High Accuracy)": "medium",
    "Large (Very Slow, Highest Accuracy)": "large"
}

# Load M2M100 translation model for different languages
def load_translation_model(target_language):
    lang_codes = {
        "fa": "fa",  # Persian (Farsi)
        "es": "es",  # Spanish
        "fr": "fr",  # French
        "de": "de",  # German
        "it": "it",  # Italian
        "pt": "pt",  # Portuguese
        "ar": "ar",  # Arabic
        "zh": "zh",  # Chinese
        "hi": "hi",  # Hindi
        "ja": "ja",  # Japanese
        "ko": "ko",  # Korean
        "ru": "ru",  # Russian
    }
    target_lang_code = lang_codes.get(target_language)
    if not target_lang_code:
        raise ValueError(f"Translation model for {target_language} not supported")

    tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M")
    translation_model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M")

    tokenizer.src_lang = "en"
    tokenizer.tgt_lang = target_lang_code

    return tokenizer, translation_model

def translate_text(text, tokenizer, model):
    try:
        inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
        translated = model.generate(**inputs, forced_bos_token_id=tokenizer.get_lang_id(tokenizer.tgt_lang))
        return tokenizer.decode(translated[0], skip_special_tokens=True)
    except Exception as e:
        raise RuntimeError(f"Error during translation: {e}")

# Helper function to format timestamps in SRT format
def format_timestamp(seconds):
    milliseconds = int((seconds % 1) * 1000)
    seconds = int(seconds)
    hours = seconds // 3600
    minutes = (seconds % 3600) // 60
    seconds = seconds % 60
    return f"{hours:02}:{minutes:02}:{seconds:02},{milliseconds:03}"

# Corrected write_srt function
def write_srt(transcription, output_file, tokenizer=None, translation_model=None):
    with open(output_file, "w") as f:
        for i, segment in enumerate(transcription['segments']):
            start = segment['start']
            end = segment['end']
            text = segment['text']
            
            if translation_model:
                text = translate_text(text, tokenizer, translation_model)
            
            start_time = format_timestamp(start)
            end_time = format_timestamp(end)
            
            f.write(f"{i + 1}\n")
            f.write(f"{start_time} --> {end_time}\n")
            f.write(f"{text.strip()}\n\n")

# Embedding subtitles into video (hardsub)
def embed_hardsub_in_video(video_file, srt_file, output_video):
    command = f'ffmpeg -i "{video_file}" -vf "subtitles=\'{srt_file}\'" -c:v libx264 -crf 23 -preset medium "{output_video}"'
    try:
        process = subprocess.run(shlex.split(command), capture_output=True, text=True, timeout=300)
        if process.returncode != 0:
            raise RuntimeError(f"ffmpeg error: {process.stderr}")
    except subprocess.TimeoutExpired:
        raise RuntimeError("ffmpeg process timed out.")
    except Exception as e:
        raise RuntimeError(f"Error running ffmpeg: {e}")

# Helper function to write Word documents
def write_word(transcription, output_file, tokenizer=None, translation_model=None, target_language=None):
    doc = Document()
    rtl = target_language == "fa"
    for i, segment in enumerate(transcription['segments']):
        text = segment['text']
        if translation_model:
            text = translate_text(text, tokenizer, translation_model)
        para = doc.add_paragraph(f"{i + 1}. {text.strip()}")
        if rtl:
            para.paragraph_format.right_to_left = True
    doc.save(output_file)

# Helper function to write PDF documents
def write_pdf(transcription, output_file, tokenizer=None, translation_model=None):
    # Create PDF with A4 page size
    c = canvas.Canvas(output_file, pagesize=A4)
    app_dir = os.path.dirname(os.path.abspath(__file__))

    # Register fonts
    nazanin_font_path = os.path.join(app_dir, 'B-NAZANIN.TTF')
    arial_font_path = os.path.join(app_dir, 'Arial.ttf')

    if os.path.exists(nazanin_font_path):
        pdfmetrics.registerFont(TTFont('B-Nazanin', nazanin_font_path))
    if os.path.exists(arial_font_path):
        pdfmetrics.registerFont(TTFont('Arial', arial_font_path))

    y_position = A4[1] - 50
    line_height = 20

    for i, segment in enumerate(transcription['segments']):
        text = segment['text']
        if translation_model:
            text = translate_text(text, tokenizer, translation_model)

        line = f"{i + 1}. {text.strip()}"
        target_language = tokenizer.tgt_lang if translation_model else None

        if target_language in ['fa', 'ar']:
            reshaped_text = arabic_reshaper.reshape(line)
            bidi_text = get_display(reshaped_text)
            c.setFont('B-Nazanin', 12)
            c.drawRightString(A4[0] - 50, y_position, bidi_text)
        else:
            c.setFont('Arial', 12)
            c.drawString(50, y_position, line)

        if y_position < 50:
            c.showPage()
            y_position = A4[1] - 50

        y_position -= line_height

    c.save()
    return output_file

# Helper function to write PowerPoint slides
def write_ppt(transcription, output_file, tokenizer=None, translation_model=None):
    ppt = Presentation()
    slide = ppt.slides.add_slide(ppt.slide_layouts[5])
    text_buffer = ""
    max_chars_per_slide = 400

    for i, segment in enumerate(transcription['segments']):
        text = segment['text']
        if translation_model:
            text = translate_text(text, tokenizer, translation_model)

        line = f"{i + 1}. {text.strip()}\n"
        if len(text_buffer) + len(line) > max_chars_per_slide:
            slide.shapes.title.text = "Transcription"
            textbox = slide.shapes.add_textbox(left=0, top=0, width=ppt.slide_width, height=ppt.slide_height)
            textbox.text = text_buffer.strip()
            slide = ppt.slides.add_slide(ppt.slide_layouts[5])
            text_buffer = line
        else:
            text_buffer += line

    if text_buffer:
        slide.shapes.title.text = ""
        textbox = slide.shapes.add_textbox(left=0, top=0, width=ppt.slide_width, height=ppt.slide_height)
        textbox.text = text_buffer.strip()

    ppt.save(output_file)

# Transcribing video and generating output
def transcribe_video(video_file, language, target_language, model_name, output_format):
    actual_model_name = whisper_models[model_name]  # Map user selection to model name
    model = whisper.load_model(actual_model_name)  # Load the selected model

    if video_file is not None:  # Ensure the video_file is not None
        video_file_path = video_file.name
    else:
        raise ValueError("No video file provided. Please upload a video file.")

    result = model.transcribe(video_file_path, language=language)
    video_name = os.path.splitext(video_file_path)[0]
    if target_language != "en":
        try:
            tokenizer, translation_model = load_translation_model(target_language)
        except Exception as e:
            raise RuntimeError(f"Error loading translation model: {e}")
    else:
        tokenizer, translation_model = None, None

    srt_file = f"{video_name}.srt"
    write_srt(result, srt_file, tokenizer, translation_model)

    if output_format == "SRT":
        return srt_file
    elif output_format == "Video with Hardsub":
        output_video = f"{video_name}_with_subtitles.mp4"
        try:
            embed_hardsub_in_video(video_file_path, srt_file, output_video)
            return output_video
        except Exception as e:
            raise RuntimeError(f"Error embedding subtitles in video: {e}")
    elif output_format == "Word":
        word_file = f"{video_name}.docx"
        write_word(result, word_file, tokenizer, translation_model, target_language)
        return word_file
    elif output_format == "PDF":
        pdf_file = f"{video_name}.pdf"
        write_pdf(result, pdf_file, tokenizer, translation_model)
        return pdf_file
    elif output_format == "PowerPoint":
        ppt_file = f"{video_name}.pptx"
        write_ppt(result, ppt_file, tokenizer, translation_model)
        return ppt_file
    else:
        raise ValueError("Invalid output format selected.")

# Gradio Interface setup
iface = gr.Interface(
    fn=transcribe_video,
    inputs=[
        gr.File(label="Upload Video File"),
        gr.Dropdown(label="Select Original Video Language", choices=["en", "es", "fr", "de", "it", "pt"], value="en"),
        gr.Dropdown(label="Select Subtitle Translation Language", choices=["en", "fa", "es", "de", "fr", "it", "pt"], value="fa"),
        gr.Dropdown(label="Select Whisper Model", choices=list(whisper_models.keys()), value="Tiny (Fast, Less Accurate)"),
        gr.Radio(label="Choose Output Format", choices=["SRT", "Video with Hardsub", "Word", "PDF", "PowerPoint"], value="Video with Hardsub")
    ],
    outputs=gr.File(label="Download File"),
    title="Video Subtitle Generator with Translation & Multi-Format Output",
    description=(
        "This tool allows you to generate subtitles from a video file, translate the subtitles into multiple languages using M2M100, "
        "and export them in various formats including SRT, hardcoded subtitles in video, Word, PDF, or PowerPoint."
    ),
    theme="compact",
    live=False
)

# Run the interface
iface.launch(share=True)