app.py

import numpy as np
import re
import concurrent.futures
import gradio as gr
from datetime import datetime
import random
import moviepy
from transformers import pipeline
from transformers.pipelines.audio_utils import ffmpeg_read
from moviepy.editor import (
    ImageClip,
    VideoFileClip,
    TextClip, 
    CompositeVideoClip,
    CompositeAudioClip,
    AudioFileClip,
    concatenate_videoclips,
    concatenate_audioclips
)
from PIL import Image, ImageDraw, ImageFont
from moviepy.audio.AudioClip import AudioArrayClip
import subprocess
import json
import logging
import whisperx
import time
import os
import openai 
from openai import OpenAI 
from TTS.api import TTS
import torch
from pydub import AudioSegment
from pyannote.audio import Pipeline
import traceback
import wave

logger = logging.getLogger(__name__)

# Configure logging
logging.basicConfig(level=logging.DEBUG, format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger(__name__)
logger.info(f"MoviePy Version: {moviepy.__version__}")

# Accept license terms for Coqui XTTS
os.environ["COQUI_TOS_AGREED"] = "1"
# torch.serialization.add_safe_globals([XttsConfig])

logger.info(gr.__version__)

client = OpenAI(
    api_key= os.environ.get("openAI_api_key"),  # This is the default and can be omitted
)
hf_api_key = os.environ.get("hf_token")

def silence(duration, fps=44100):
    """
    Returns a silent AudioClip of the specified duration.
    """
    return AudioArrayClip(np.zeros((int(fps*duration), 2)), fps=fps)

def count_words_or_characters(text):
    # Count non-Chinese words
    non_chinese_words = len(re.findall(r'\b[a-zA-Z0-9]+\b', text))
    
    # Count Chinese characters
    chinese_chars = len(re.findall(r'[\u4e00-\u9fff]', text))
    
    return non_chinese_words + chinese_chars
    
# Define the passcode
PASSCODE = "show_feedback_db"

css = """
/* Adjust row height */
.dataframe-container tr {
    height: 50px !important; 
}
/* Ensure text wrapping and prevent overflow */
.dataframe-container td {
    white-space: normal !important; 
    word-break: break-word !important;
}
/* Set column widths */
[data-testid="block-container"] .scrolling-dataframe th:nth-child(1), 
[data-testid="block-container"] .scrolling-dataframe td:nth-child(1) {
    width: 6%; /* Start column */
}
[data-testid="block-container"] .scrolling-dataframe th:nth-child(2), 
[data-testid="block-container"] .scrolling-dataframe td:nth-child(2) {
    width: 47%; /* Original text */
}
[data-testid="block-container"] .scrolling-dataframe th:nth-child(3), 
[data-testid="block-container"] .scrolling-dataframe td:nth-child(3) {
    width: 47%; /* Translated text */
}
[data-testid="block-container"] .scrolling-dataframe th:nth-child(4), 
[data-testid="block-container"] .scrolling-dataframe td:nth-child(4) {
    display: none !important;
}
"""

# Function to save feedback or provide access to the database file
def handle_feedback(feedback):
    feedback = feedback.strip()  # Clean up leading/trailing whitespace
    if not feedback:
        return "Feedback cannot be empty.", None

    if feedback == PASSCODE:
        # Provide access to the feedback.db file
        return "Access granted! Download the database file below.", "feedback.db"
    else:
        # Save feedback to the database
        with sqlite3.connect("feedback.db") as conn:
            cursor = conn.cursor()
            cursor.execute("CREATE TABLE IF NOT EXISTS studio_feedback (id INTEGER PRIMARY KEY, comment TEXT)")
            cursor.execute("INSERT INTO studio_feedback (comment) VALUES (?)", (feedback,))
            conn.commit()
        return "Thank you for your feedback!", None

def segment_background_audio(audio_path, background_audio_path="background_segments.wav", speech_padding=0.15):
    """
    Segments and removes speech from audio, returning only background.
    Padding is applied around speech segments to reduce overlap/bleed.
    """
    from pyannote.audio import Pipeline
    from pydub import AudioSegment

    pipeline = Pipeline.from_pretrained("pyannote/voice-activity-detection", use_auth_token=hf_api_key)
    vad_result = pipeline(audio_path)

    full_audio = AudioSegment.from_wav(audio_path)
    full_duration_sec = len(full_audio) / 1000.0

    current_time = 0.0
    result_audio = AudioSegment.empty()

    for segment in vad_result.itersegments():
        start = max(0.0, segment.start - speech_padding)
        end = min(full_duration_sec, segment.end + speech_padding)

        # Extract non-speech segment before speech starts
        if current_time < start:
            bg = full_audio[int(current_time * 1000):int(start * 1000)]
            result_audio += bg

        # Replace speech (plus margin) with silence
        result_audio += AudioSegment.silent(duration=int((end - start) * 1000))
        current_time = end

    # Add trailing background after the last segment
    if current_time < full_duration_sec:
        result_audio += full_audio[int(current_time * 1000):]

    result_audio.export(background_audio_path, format="wav")
    return background_audio_path

def transcribe_video_with_speakers(video_path):
    # Extract audio from video
    video = VideoFileClip(video_path)
    audio_path = "audio.wav"
    video.audio.write_audiofile(audio_path)
    logger.info(f"Audio extracted from video: {audio_path}")

    segment_result = segment_background_audio(audio_path)
    print(f"Saved non-speech (background) audio to local")
    
    # Set up device
    device = "cuda" if torch.cuda.is_available() else "cpu"
    logger.info(f"Using device: {device}")
    
    try:
        # Load a medium model with float32 for broader compatibility
        model = whisperx.load_model("large-v3", device=device, compute_type="float32")
        logger.info("WhisperX model loaded")
    
        # Transcribe
        result = model.transcribe(audio_path, chunk_size=10, print_progress = True)
        logger.info("Audio transcription completed")

        # Get the detected language
        detected_language = result["language"]
        logger.debug(f"Detected language: {detected_language}")
        # Alignment
        model_a, metadata = whisperx.load_align_model(language_code=result["language"], device=device)
        result = whisperx.align(result["segments"], model_a, metadata, audio_path, device)
        logger.info("Transcription alignment completed")
    
        # Diarization (works independently of Whisper model size)
        diarize_model = whisperx.DiarizationPipeline(use_auth_token=hf_api_key, device=device)
        diarize_segments = diarize_model(audio_path)
        logger.info("Speaker diarization completed")
    
        # Assign speakers
        result = whisperx.assign_word_speakers(diarize_segments, result)
        logger.info("Speakers assigned to transcribed segments")
    
    except Exception as e:
        logger.error(f"❌ WhisperX pipeline failed: {e}")

    # Extract timestamps, text, and speaker IDs
    transcript_with_speakers = [
        {
            "start": segment["start"],
            "end": segment["end"],
            "text": segment["text"],
            "speaker": segment["speaker"]
        }
        for segment in result["segments"]
    ]

    # Collect audio for each speaker
    speaker_audio = {}
    for segment in result["segments"]:
        speaker = segment["speaker"]
        if speaker not in speaker_audio:
            speaker_audio[speaker] = []
        speaker_audio[speaker].append((segment["start"], segment["end"]))

    # Collapse and truncate speaker audio
    speaker_sample_paths = {}
    audio_clip = AudioFileClip(audio_path)
    for speaker, segments in speaker_audio.items():
        speaker_clips = [audio_clip.subclip(start, end) for start, end in segments]
        combined_clip = concatenate_audioclips(speaker_clips)
        truncated_clip = combined_clip.subclip(0, min(30, combined_clip.duration))
        sample_path = f"speaker_{speaker}_sample.wav"
        truncated_clip.write_audiofile(sample_path)
        speaker_sample_paths[speaker] = sample_path
        logger.info(f"Created sample for {speaker}: {sample_path}")

    # Clean up
    video.close()
    audio_clip.close()
    os.remove(audio_path)

    return transcript_with_speakers, detected_language

# Function to get the appropriate translation model based on target language
def get_translation_model(source_language, target_language):
    """
    Get the translation model based on the source and target language.
    Parameters:
    - target_language (str): The language to translate the content into (e.g., 'es', 'fr').
    - source_language (str): The language of the input content (default is 'en' for English).
    
    Returns:
    - str: The translation model identifier.
    """
    # List of allowable languages
    allowable_languages = ["en", "es", "fr", "zh", "de", "it", "pt", "ja", "ko", "ru"]

    # Validate source and target languages
    if source_language not in allowable_languages:
        logger.debug(f"Invalid source language '{source_language}'. Supported languages are: {', '.join(allowable_languages)}")
        # Return a default model if source language is invalid
        source_language = "en"  # Default to 'en'

    if target_language not in allowable_languages:
        logger.debug(f"Invalid target language '{target_language}'. Supported languages are: {', '.join(allowable_languages)}")
        # Return a default model if target language is invalid
        target_language = "zh"  # Default to 'zh'

    if source_language == target_language:
        source_language = "en"  # Default to 'en'
        target_language = "zh"  # Default to 'zh'

    # Return the model using string concatenation
    return f"Helsinki-NLP/opus-mt-{source_language}-{target_language}"

def translate_single_entry(entry, translator):
    original_text = entry["text"]
    translated_text = translator(original_text)[0]['translation_text']
    return {
        "start": entry["start"],
        "original": original_text,
        "translated": translated_text,
        "end": entry["end"],
        "speaker": entry["speaker"]
    }

def translate_text(transcription_json, source_language, target_language):
    # Load the translation model for the specified target language
    translation_model_id = get_translation_model(source_language, target_language)
    logger.debug(f"Translation model: {translation_model_id}")
    translator = pipeline("translation", model=translation_model_id)

    # Use ThreadPoolExecutor to parallelize translations
    with concurrent.futures.ThreadPoolExecutor() as executor:
        # Submit all translation tasks and collect results
        translate_func = lambda entry: translate_single_entry(entry, translator)
        translated_json = list(executor.map(translate_func, transcription_json))

    # Sort the translated_json by start time
    translated_json.sort(key=lambda x: x["start"])

    # Log the components being added to translated_json
    for entry in translated_json:
        logger.debug("Added to translated_json: start=%s, original=%s, translated=%s, end=%s, speaker=%s",
                     entry["start"], entry["original"], entry["translated"], entry["end"], entry["speaker"])

    return translated_json

def update_translations(file, edited_table, process_mode):
    """
    Update the translations based on user edits in the Gradio Dataframe.
    """
    output_video_path = "output_video.mp4"
    logger.debug(f"Editable Table: {edited_table}")

    if file is None:
        logger.info("No file uploaded. Please upload a video/audio file.")
        return None, [], None, "No file uploaded. Please upload a video/audio file."
        
    try:
        start_time = time.time()  # Start the timer

        # Convert the edited_table (list of lists) back to list of dictionaries
        updated_translations = [
            {
                "start": row["start"],  # Access by column name
                "original": row["original"],
                "translated": row["translated"],
                "end": row["end"]
            }
            for _, row in edited_table.iterrows()
        ]

        # Call the function to process the video with updated translations
        add_transcript_voiceover(file.name, updated_translations, output_video_path, process_mode)

        # Calculate elapsed time
        elapsed_time = time.time() - start_time
        elapsed_time_display = f"Updates applied successfully in {elapsed_time:.2f} seconds."

        return output_video_path, elapsed_time_display

    except Exception as e:
        raise ValueError(f"Error updating translations: {e}")

def create_subtitle_clip_pil(text, start_time, end_time, video_width, video_height, font_path):
    try:
        subtitle_width = int(video_width * 0.8)
        aspect_ratio = video_height / video_width
        if aspect_ratio > 1.2:  # Portrait video
            subtitle_font_size = int(video_width // 22)
        else:  # Landscape video
            subtitle_font_size = int(video_height // 24)

        font = ImageFont.truetype(font_path, subtitle_font_size)

        dummy_img = Image.new("RGBA", (subtitle_width, 1), (0, 0, 0, 0))
        draw = ImageDraw.Draw(dummy_img)

        lines = []
        line = ""
        for word in text.split():
            test_line = f"{line} {word}".strip()
            bbox = draw.textbbox((0, 0), test_line, font=font)
            w = bbox[2] - bbox[0]
            if w <= subtitle_width - 10:
                line = test_line
            else:
                lines.append(line)
                line = word
        lines.append(line)

        line_heights = [draw.textbbox((0, 0), l, font=font)[3] - draw.textbbox((0, 0), l, font=font)[1] for l in lines]
        total_height = sum(line_heights) + (len(lines) - 1) * 5
        img = Image.new("RGBA", (subtitle_width, total_height), (0, 0, 0, 0))
        draw = ImageDraw.Draw(img)

        y = 0
        for idx, line in enumerate(lines):
            bbox = draw.textbbox((0, 0), line, font=font)
            w = bbox[2] - bbox[0]
            draw.text(((subtitle_width - w) // 2, y), line, font=font, fill="yellow")
            y += line_heights[idx] + 5
            
        img_np = np.array(img)  # <- ✅ Fix: convert to NumPy
        txt_clip = ImageClip(img_np).set_start(start_time).set_duration(end_time - start_time).set_position("bottom").set_opacity(0.8)
        return txt_clip
    except Exception as e:
        logger.error(f"\u274c Failed to create subtitle clip: {e}")
        return None

def process_entry(entry, i, tts_model, video_width, video_height, process_mode, target_language, font_path, speaker_sample_paths=None):
    logger.debug(f"Processing entry {i}: {entry}")
    error_message = None

    try:
        txt_clip = create_subtitle_clip_pil(entry["translated"], entry["start"], entry["end"], video_width, video_height, font_path)
    except Exception as e:
        error_message = f"❌ Failed to create subtitle clip for entry {i}: {e}"
        logger.error(error_message)
        txt_clip = None

    audio_segment = None
    if process_mode > 1:
        try:
            segment_audio_path = f"segment_{i}_voiceover.wav"
            desired_duration = entry["end"] - entry["start"]
            desired_speed = calibrated_speed(entry['translated'], desired_duration)

            speaker = entry.get("speaker", "default")
            speaker_wav_path = f"speaker_{speaker}_sample.wav"
            
            if process_mode > 2 and speaker_wav_path and os.path.exists(speaker_wav_path) and target_language in tts_model.synthesizer.tts_model.language_manager.name_to_id.keys():
                generate_voiceover_clone(entry['translated'], tts_model, desired_speed, target_language, speaker_wav_path, segment_audio_path)
            else:
                generate_voiceover_OpenAI(entry['translated'], target_language, desired_speed, segment_audio_path)

            if not segment_audio_path or not os.path.exists(segment_audio_path):
                raise FileNotFoundError(f"Voiceover file not generated at: {segment_audio_path}")

            audio_clip = AudioFileClip(segment_audio_path)
            logger.debug(f"Audio clip duration: {audio_clip.duration}, Desired duration: {desired_duration}")

            if audio_clip.duration < desired_duration:
                silence_duration = desired_duration - audio_clip.duration
                audio_clip = concatenate_audioclips([audio_clip, silence(duration=silence_duration)])
                logger.info(f"Padded audio with {silence_duration} seconds of silence.")

            audio_segment = audio_clip.set_start(entry["start"]).set_duration(desired_duration)

        except Exception as e:
            err = f"❌ Failed to generate audio segment for entry {i}: {e}"
            logger.error(err)
            error_message = error_message + " | " + err if error_message else err
            audio_segment = None

    return i, txt_clip, audio_segment, error_message
    
def add_transcript_voiceover(video_path, translated_json, output_path, process_mode, target_language="en", speaker_sample_paths=None, background_audio_path="background_segments.wav"):
    video = VideoFileClip(video_path)
    font_path = "./NotoSansSC-Regular.ttf"

    text_clips = []
    audio_segments = []
    error_messages = []
    
    if process_mode > 2:
        global tts_model
        if tts_model is None:
            try:
                print("🔄 Loading XTTS model...")
                tts_model = TTS(model_name="tts_models/multilingual/multi-dataset/your_tts")
                print("✅ XTTS model loaded successfully.")
            except Exception as e:
                print("❌ Error loading XTTS model:")
                traceback.print_exc()
                return f"Error loading XTTS model: {e}"
                ## Need to implmenet backup option.

    with concurrent.futures.ThreadPoolExecutor() as executor:
        futures = [executor.submit(process_entry, entry, i, tts_model, video.w, video.h, process_mode, target_language, font_path, speaker_sample_paths)
                   for i, entry in enumerate(translated_json)]

        results = []
        for future in concurrent.futures.as_completed(futures):
            try:
                i, txt_clip, audio_segment, error = future.result()
                results.append((i, txt_clip, audio_segment))
                if error:
                    error_messages.append(f"[Entry {i}] {error}")
            except Exception as e:
                err = f"❌ Unexpected error in future result: {e}"
                logger.error(err)
                error_messages.append(err)

    # Sort by entry index to ensure order
    results.sort(key=lambda x: x[0])
    text_clips = [clip for _, clip, _ in results if clip]
    if process_mode>1:
        audio_segments = [segment for _, _, segment in results if segment]

    final_video = CompositeVideoClip([video] + text_clips)

    if process_mode>1 and audio_segments:
        try:
            voice_audio = CompositeAudioClip(audio_segments).set_duration(video.duration)

            if background_audio_path and os.path.exists(background_audio_path):
                background_audio = AudioFileClip(background_audio_path).set_duration(video.duration)
                final_audio = CompositeAudioClip([voice_audio, background_audio])
                # final_audio = voice_audio
                logger.info("✅ Background audio loaded and merged with voiceover.")
            else:
                final_audio = voice_audio
                logger.info("⚠️ No background audio found. Using voiceover only.")

            final_video = final_video.set_audio(final_audio)

        except Exception as e:
            logger.error(f"❌ Failed to set audio: {e}")
    
    logger.info(f"Saving the final video to: {output_path}")
    final_video.write_videofile(output_path, codec="libx264", audio_codec="aac")

    logger.info("Video processing completed successfully.")

    if error_messages:
        logger.warning("⚠️ Errors encountered during processing:")
        for msg in error_messages:
            logger.warning(msg)

    return error_messages

def generate_voiceover_OpenAI(full_text, language, desired_speed, output_audio_path):
    """
    Generate voiceover from translated text for a given language using OpenAI TTS API.
    """
    # Define the voice based on the language (for now, use 'alloy' as default)
    voice = "alloy"  # Adjust based on language if needed

    # Define the model (use tts-1 for real-time applications)
    model = "tts-1"

    max_retries = 3
    retry_count = 0

    while retry_count < max_retries:
        try:
            # Create the speech using OpenAI TTS API
            response = client.audio.speech.create(
                model=model,
                voice=voice,
                input=full_text,
                speed=desired_speed
            )
            # Save the audio to the specified path
            with open(output_audio_path, 'wb') as f:
                for chunk in response.iter_bytes():
                    f.write(chunk)
            logging.info(f"Voiceover generated successfully for {output_audio_path}")
            break

        except Exception as e:
            retry_count += 1
            logging.error(f"Error generating voiceover (retry {retry_count}/{max_retries}): {e}")
            time.sleep(5)  # Wait 5 seconds before retrying

    if retry_count == max_retries:
        raise ValueError(f"Failed to generate voiceover after {max_retries} retries.")

def generate_voiceover_clone(full_text, tts_model, desired_speed, target_language, speaker_wav_path, output_audio_path):
    try:

        tts_model.tts_to_file(
            text=full_text,
            speaker_wav=speaker_wav_path,
            language=target_language,
            file_path=output_audio_path,
            speed=desired_speed,
            split_sentences=True
        )
        msg = "✅ Voice cloning completed successfully."
        logger.info(msg)
        return output_audio_path, msg, None

    except Exception as e:
        generate_voiceover_OpenAI(full_text, target_language, desired_speed, output_audio_path)
        err_msg = f"❌ An error occurred: {str(e)}, fallback to premium voice"
        logger.error(traceback.format_exc())
        return None, err_msg, err_msg

def calibrated_speed(text, desired_duration):
    """
    Compute a speed factor to help TTS fit audio into desired duration,
    using a simple truncated linear function of characters per second.
    """
    char_count = len(text.strip())
    if char_count == 0 or desired_duration <= 0:
        return 1.0  # fallback

    cps = char_count / desired_duration  # characters per second

    # Truncated linear mapping
    if cps < 14:
        return 1.0
    elif cps > 30:
        return 2
    else:
        slope = (2 - 1.0) / (30 - 14)
        return 1.0 + slope * (cps - 14)

def upload_and_manage(file, target_language, process_mode, background_audio_path="background_segments.wav"):
    if file is None:
        logger.info("No file uploaded. Please upload a video/audio file.")
        return None, [], None, "No file uploaded. Please upload a video/audio file."

    try:
        start_time = time.time()  # Start the timer
        logger.info(f"Started processing file: {file.name}")

        # Define paths for audio and output files
        audio_path = "audio.wav"
        output_video_path = "output_video.mp4"
        voiceover_path = "voiceover.wav"
        logger.info(f"Using audio path: {audio_path}, output video path: {output_video_path}, voiceover path: {voiceover_path}")

        # Step 1: Transcribe audio from uploaded media file and get timestamps
        logger.info("Transcribing audio...")
        transcription_json, source_language = transcribe_video_with_speakers(file.name)
        logger.info(f"Transcription completed. Detected source language: {source_language}")

        # Step 2: Translate the transcription
        logger.info(f"Translating transcription from {source_language} to {target_language}...")
        translated_json = translate_text(transcription_json, source_language, target_language)
        logger.info(f"Translation completed. Number of translated segments: {len(translated_json)}")

        # Step 3: Add transcript to video based on timestamps
        logger.info("Adding translated transcript to video...")
        add_transcript_voiceover(file.name, translated_json, output_video_path, process_mode, target_language)
        logger.info(f"Transcript added to video. Output video saved at {output_video_path}")

        # Convert translated JSON into a format for the editable table
        logger.info("Converting translated JSON into editable table format...")
        editable_table = [
            [float(entry["start"]), entry["original"], entry["translated"], float(entry["end"]), entry["speaker"]]
            for entry in translated_json
        ]

        # Calculate elapsed time
        elapsed_time = time.time() - start_time
        elapsed_time_display = f"Processing completed in {elapsed_time:.2f} seconds."
        logger.info(f"Processing completed in {elapsed_time:.2f} seconds.")

        return editable_table, output_video_path, background_audio_path, elapsed_time_display

    except Exception as e:
        logger.error(f"An error occurred: {str(e)}")
        return [], None, None, f"An error occurred: {str(e)}"

# Gradio Interface with Tabs
def build_interface():
    with gr.Blocks(css=css) as demo:
        gr.Markdown("## Video Localization")
        with gr.Row():
            with gr.Column(scale=4):
                file_input = gr.File(label="Upload Video/Audio File")
                language_input = gr.Dropdown(["en", "es", "fr", "zh"], label="Select Language")  # Language codes        
                process_mode = gr.Radio(choices=[("Transcription Only", 1),("Transcription with Premium Voice",2),("Transcription with Voice Clone", 3)],label="Choose Processing Type",value=1)
                submit_button = gr.Button("Post and Process")

            with gr.Column(scale=8):
                gr.Markdown("## Edit Translations")
                
                # Editable JSON Data
                editable_table = gr.Dataframe(
                    value=[],  # Default to an empty list to avoid undefined values
                    headers=["start", "original", "translated", "end", "speaker"],
                    datatype=["number", "str", "str", "number", "str"],
                    row_count=1,  # Initially empty
                    col_count=5,
                    interactive=[False, True, True, False, False],  # Control editability
                    label="Edit Translations",
                    wrap=True  # Enables text wrapping if supported
                )
                save_changes_button = gr.Button("Save Changes")
                processed_video_output = gr.File(label="Download Processed Video", interactive=True)  # Download button
                background_audio_output = gr.File(label="Download Background Audio", interactive=True)  # Download button
                elapsed_time_display = gr.Textbox(label="Elapsed Time", lines=1, interactive=False)

            with gr.Column(scale=1):
                gr.Markdown("**Feedback**")
                feedback_input = gr.Textbox(
                    placeholder="Leave your feedback here...",
                    label=None,
                    lines=3,
                )
                feedback_btn = gr.Button("Submit Feedback")
                response_message = gr.Textbox(label=None, lines=1, interactive=False)
                db_download = gr.File(label="Download Database File", visible=False)
            
                # Link the feedback handling
                def feedback_submission(feedback):
                    message, file_path = handle_feedback(feedback)
                    if file_path:
                        return message, gr.update(value=file_path, visible=True)
                    return message, gr.update(visible=False)            

            save_changes_button.click(
                update_translations, 
                inputs=[file_input, editable_table, process_mode],
                outputs=[processed_video_output, elapsed_time_display]
            )

            submit_button.click(
                upload_and_manage, 
                inputs=[file_input, language_input, process_mode], 
                outputs=[editable_table, processed_video_output, background_audio_output, elapsed_time_display]
            )

            # Connect submit button to save_feedback_db function
            feedback_btn.click(
                feedback_submission, 
                inputs=[feedback_input], 
                outputs=[response_message, db_download]
            )

    return demo

tts_model = None
# Launch the Gradio interface
demo = build_interface()
demo.launch()