app.py

import gradio as gr
from datetime import datetime
import random
from transformers import pipeline
from transformers.pipelines.audio_utils import ffmpeg_read
from moviepy import (
    ImageClip, 
    VideoFileClip,
    TextClip, 
    CompositeVideoClip, 
    AudioFileClip,
    concatenate_videoclips
)
import subprocess
import speech_recognition as sr
import json
from nltk.tokenize import sent_tokenize
import logging
from textblob import TextBlob
import whisper

import sqlite3

# Define the passcode
PASSCODE = "show_feedback_db"

# 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

# Configure logging
logging.basicConfig(level=logging.DEBUG, format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger(__name__)

def list_available_fonts():
    try:
        # Run the 'fc-list' command to list fonts
        result = subprocess.run(
            ["fc-list", "--format", "%{file}\\n"],
            stdout=subprocess.PIPE,
            stderr=subprocess.PIPE,
            text=True,
            check=True
        )
        fonts = result.stdout.splitlines()
        logger.debug(f"Available fonts:\n{fonts}")
        return fonts
    except subprocess.CalledProcessError as e:
        logger.error(f"Error while listing fonts: {e.stderr}")
        return []

def split_into_sentences(text):
    blob = TextBlob(text)
    return [str(sentence) for sentence in blob.sentences]

def transcribe_video(video_path):
    # Load the video file and extract audio
    video = VideoFileClip(video_path)
    audio_path = "audio.wav"
    video.audio.write_audiofile(audio_path)
    
    # Load Whisper model
    model = whisper.load_model("base")  # Options: tiny, base, small, medium, large

    # Transcribe with Whisper
    result = model.transcribe(audio_path, word_timestamps=True)
    
    # Extract timestamps and text
    transcript_with_timestamps = [
        {
            "start": segment["start"],
            "end": segment["end"],
            "text": segment["text"]
        }
        for segment in result["segments"]
    ]
    return transcript_with_timestamps

# Function to get the appropriate translation model based on target language
def get_translation_model(target_language):
    # Map of target languages to their corresponding model names
    model_map = {
        "es": "Helsinki-NLP/opus-mt-en-es",  # English to Spanish
        "fr": "Helsinki-NLP/opus-mt-en-fr",  # English to French
        "zh": "Helsinki-NLP/opus-mt-en-zh",  # English to Chinese
        # Add more languages as needed
    }
    return model_map.get(target_language, "Helsinki-NLP/opus-mt-en-zh")  # Default to Chinese if not found

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

    # Prepare output structure
    translated_json = []

    # Translate each sentence and store it with its start time
    for entry in transcription_json:
        original_text = entry["text"]
        translated_text = translator(original_text)[0]['translation_text']
        translated_json.append({
            "start": entry["start"],
            "original": original_text,
            "translated": translated_text,
            "end": entry["end"]
        })
        # Log the components being added to translated_json
        logger.debug("Adding to translated_json: start=%s, original=%s, translated=%s, end=%s",
                     entry["start"], original_text, translated_text, entry["end"])

    # Return the translated timestamps as a JSON string
    return translated_json

def add_transcript_to_video(video_path, translated_json, output_path):
    # Load the video file
    video = VideoFileClip(video_path)

    # Create text clips based on timestamps
    text_clips = []

    logger.debug("Full translated_json: %s", translated_json)
    for entry in translated_json:
        logger.debug("Processing entry: %s", entry)

    font_path = "./NotoSansSC-Regular.ttf"

    for entry in translated_json:
        # Ensure `entry` is a dictionary with keys "start", "end", and "translated"
        if isinstance(entry, dict) and "translated" in entry:
            txt_clip = TextClip(
                text=entry["translated"], font=font_path, method='caption', color='yellow', size=video.size
            ).with_start(entry["start"]).with_duration(entry["end"] - entry["start"]).with_position(('bottom')).with_opacity(0.7)
            text_clips.append(txt_clip)
        else:
            raise ValueError(f"Invalid entry format: {entry}")
            
    # Overlay all text clips on the original video
    final_video = CompositeVideoClip([video] + text_clips)

    # Write the result to a file
    final_video.write_videofile(output_path, codec='libx264', audio_codec='aac')

# Mock functions for platform actions and analytics
def mock_post_to_platform(platform, content_title):
    return f"Content '{content_title}' successfully posted on {platform}!"

def mock_analytics():
    return {
        "YouTube": {"Views": random.randint(1000, 5000), "Engagement Rate": f"{random.uniform(5, 15):.2f}%"},
        "Instagram": {"Views": random.randint(500, 3000), "Engagement Rate": f"{random.uniform(10, 20):.2f}%"},
    }

import json

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

    try:
        # Convert the edited_table (list of lists) back to list of dictionaries
        updated_translations = [
            {
                "start": row[0],  # First column
                "original": row[1],  # Second column
                "translated": row[2],  # Third column
                "end": row[3],  # Fourth column
            }
            for row in edited_table
        ]

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

        return output_video_path

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

# Core functionalities
def upload_and_manage(file, language):
    if file is None:
        return "Please upload a video/audio file.", None, None, None

    # Define paths for audio and output files
    audio_path = "audio.wav"
    output_video_path = "output_video.mp4"

    list_available_fonts()
    # Transcribe audio from uploaded media file and get timestamps
    transcrption_json = transcribe_video(file.name)

    translated_json = translate_text(transcrption_json, language)
    
    # Add transcript to video based on timestamps
    add_transcript_to_video(file.name, translated_json, output_video_path)

    # Mock posting action (you can implement this as needed)
    # post_message = mock_post_to_platform(platform, file.name)

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

    return translated_json, editable_table, output_video_path

# def generate_dashboard():
#     # Mock analytics generation
#     analytics = mock_analytics()

#     if not analytics:
#         return "No analytics available."

#     dashboard = "Platform Analytics:\n"
#     for platform, data in analytics.items():
#         dashboard += f"\n{platform}:\n"
#         for metric, value in data.items():
#             dashboard += f"  {metric}: {value}\n"
#     return dashboard

# Gradio Interface with Tabs
def build_interface():
    with gr.Blocks() as demo:
        # with gr.Tab("Content Management"):
        gr.Markdown("## Video Localization")
        with gr.Row():
            with gr.Column(scale=4):
                file_input = gr.File(label="Upload Video/Audio File")
                # platform_input = gr.Dropdown(["YouTube", "Instagram"], label="Select Platform")
                language_input = gr.Dropdown(["en", "es", "fr", "zh"], label="Select Language")  # Language codes
                submit_button = gr.Button("Post and Process")
                editable_translations = gr.State(value=[])

            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"],
                    datatype=["number", "str", "str", "number"],
                    row_count=2,  # Initially empty
                    col_count=4,
                    interactive=[False, True, True, False],  # Control editability
                    label="Edit Translations",
                )
                save_changes_button = gr.Button("Save Changes")
                processed_video_output = gr.File(label="Download Processed Video", interactive=True)  # Download button

            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 Feeback")
                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], 
                outputs=[processed_video_output]
            )

            submit_button.click(
                upload_and_manage, 
                inputs=[file_input, language_input], 
                outputs=[editable_translations, editable_table, processed_video_output]
            )
        
            # Connect submit button to save_feedback_db function
            feedback_btn.click(
                feedback_submission, 
                inputs=[feedback_input], 
                outputs=[response_message, db_download]
            )

    # with gr.Tab("Analytics Dashboard"):
    #     gr.Markdown("## Content Performance Analytics")
    #     analytics_output = gr.Textbox(label="Dashboard", interactive=False)
    #     generate_dashboard_button = gr.Button("Generate Dashboard")

    #     generate_dashboard_button.click(generate_dashboard, outputs=[analytics_output])

    return demo

# Launch the Gradio interface
demo = build_interface()
demo.launch()