Update app.py

app.py

@@ -12,7 +12,6 @@ from bidi.algorithm import get_display
 from pptx import Presentation
 import subprocess
 import shlex
-import yt_dlp
 
 # Load the Whisper model (smaller model for faster transcription)
 model = whisper.load_model("tiny")
@@ -105,153 +104,87 @@ def write_word(transcription, output_file, tokenizer=None, translation_model=Non
             para.paragraph_format.right_to_left = True
     doc.save(output_file)
 
-# Helper function to reverse text for RTL
-def reverse_text_for_rtl(text):
-    return ' '.join([word[::-1] for word in text.split()])
-
 # 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)
-    # Get the directory where app.py is located
     app_dir = os.path.dirname(os.path.abspath(__file__))
 
-    # Construct the full path to the font files
+    # Register fonts
     nazanin_font_path = os.path.join(app_dir, 'B-NAZANIN.TTF')
     arial_font_path = os.path.join(app_dir, 'Arial.ttf')
 
-    # Register B-Nazanin font
     if os.path.exists(nazanin_font_path):
-        try:
-            pdfmetrics.registerFont(TTFont('B-Nazanin', nazanin_font_path))
-        except Exception as e:
-            raise RuntimeError(f"Error registering B-Nazanin font: {e}.")
-    else:
-        raise FileNotFoundError(f"B-Nazanin font file not found at {nazanin_font_path}. Please ensure it is available.")
-
-    # Register Arial font
+        pdfmetrics.registerFont(TTFont('B-Nazanin', nazanin_font_path))
     if os.path.exists(arial_font_path):
-        try:
-            pdfmetrics.registerFont(TTFont('Arial', arial_font_path))
-        except Exception as e:
-            raise RuntimeError(f"Error registering Arial font: {e}.")
-    else:
-        raise FileNotFoundError(f"Arial font file not found at {arial_font_path}. Please ensure it is available.")
+        pdfmetrics.registerFont(TTFont('Arial', arial_font_path))
 
-    # Initialize y position from top of page
-    y_position = A4[1] - 50  # Start 50 points from top
+    y_position = A4[1] - 50
     line_height = 20
 
-    # Process each segment
     for i, segment in enumerate(transcription['segments']):
         text = segment['text']
-
-        # Translate if translation model is provided
         if translation_model:
             text = translate_text(text, tokenizer, translation_model)
 
-        # Format the line with segment number
         line = f"{i + 1}. {text.strip()}"
+        target_language = tokenizer.tgt_lang if translation_model else None
 
-        # Determine target language for font and text direction
-        target_language = None
-        if translation_model:
-            # Assuming target language can be inferred from the tokenizer
-            target_language = tokenizer.tgt_lang
-
-        # Reshape and reorder the text for correct RTL display if necessary
         if target_language in ['fa', 'ar']:
             reshaped_text = arabic_reshaper.reshape(line)
             bidi_text = get_display(reshaped_text)
-            # Set font for RTL languages
-            c.setFont('B-Nazanin', 12)  
-            # Draw the text right-aligned
-            c.drawRightString(A4[0] - 50, y_position, bidi_text)  # 50 points margin from right
+            c.setFont('B-Nazanin', 12)
+            c.drawRightString(A4[0] - 50, y_position, bidi_text)
         else:
-            c.setFont('Arial', 12)  # Use Arial for other languages
-            c.drawString(50, y_position, line)  # Left aligned
+            c.setFont('Arial', 12)
+            c.drawString(50, y_position, line)
 
-        # Add new page if needed
-        if y_position < 50:  # Leave 50 points margin at bottom
+        if y_position < 50:
             c.showPage()
-            y_position = A4[1] - 50  # Reset y position for new page
+            y_position = A4[1] - 50
 
-        # Update y position for next line
         y_position -= line_height
 
-    # Save the PDF
     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])  # Create the first slide
-    text_buffer = ""  # Initialize an empty buffer to accumulate text
-    max_chars_per_slide = 400  # Set a character limit for each slide
+    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']
-        
-        # Translate if translation model is provided
         if translation_model:
             text = translate_text(text, tokenizer, translation_model)
 
-        # Format the line with segment number
         line = f"{i + 1}. {text.strip()}\n"
-
-        # Check if adding this line exceeds the character limit
         if len(text_buffer) + len(line) > max_chars_per_slide:
-            # If so, add the accumulated text to the current slide
-            slide.shapes.title.text = "Transcription"  # Set the title for the 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()
-            
-            # Create a new slide and reset the buffer
             slide = ppt.slides.add_slide(ppt.slide_layouts[5])
-            text_buffer = line  # Start the new slide with the current line
+            text_buffer = line
         else:
-            # Otherwise, keep accumulating text
             text_buffer += line
 
-    # Add any remaining text in the buffer to the last slide
     if text_buffer:
-        slide.shapes.title.text = ""  # Set the title for the last slide
+        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)
 
-
-# Function to download YouTube video
-def download_youtube_video(url):
-    ydl_opts = {
-        'format': 'mp4',
-        'outtmpl': 'downloaded_video.mp4',
-        'nocheckcertificate': True,  # Disable certificate check
-    }
-    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
-        ydl.download([url])
-    return 'downloaded_video.mp4'
-
-
 # Transcribing video and generating output
 def transcribe_video(video_file, video_url, language, target_language, output_format):
-    if video_url:
-        video_file_path = download_youtube_video(video_url)
-    else:
-        video_file_path = video_file.name
-
+    video_file_path = video_file.name
     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}")
+        tokenizer, translation_model = load_translation_model(target_language)
     else:
         tokenizer, translation_model = None, None
 
@@ -262,11 +195,8 @@ def transcribe_video(video_file, video_url, language, target_language, output_fo
         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}")
+        embed_hardsub_in_video(video_file_path, srt_file, output_video)
+        return output_video
     elif output_format == "Word":
         word_file = f"{video_name}.docx"
         write_word(result, word_file, tokenizer, translation_model, target_language)
@@ -280,28 +210,9 @@ def transcribe_video(video_file, video_url, language, target_language, output_fo
         write_ppt(result, ppt_file, tokenizer, translation_model)
         return ppt_file
 
-# Gradio interface with YouTube URL
+# Gradio interface without YouTube
 iface = gr.Interface(
     fn=transcribe_video,
     inputs=[
-        gr.File(label="Upload Video File (or leave empty for YouTube link)"),  # Removed 'optional=True'
-        gr.Textbox(label="YouTube Video URL (optional)", placeholder="https://www.youtube.com/watch?v=..."),
-        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.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 (Supports YouTube)",
-    description=(
-        "This tool allows you to generate subtitles from a video file or YouTube link using Whisper, "
-        "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
-)
-
-if __name__ == "__main__":
-    iface.launch()
-
-
+        gr.File(label="Upload Video File"),  # Removed YouTube URL input
+        gr.Dropdown(label="Select Original Video Language", choices