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 speech_recognition as sr
import json
from nltk.tokenize import sent_tokenize
import logging
from textblob import TextBlob
import whisper


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

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-fr")  # Default to French 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)
 
    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(
                entry["translated"], method='caption', color='white', bg_color='black', size=video.size
            ).set_start(entry["start"]).set_duration(entry["end"] - entry["start"]).set_position(('bottom')).set_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}%"},
    }

# Core functionalities
def upload_and_manage(file, platform, 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"

    # 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)

    # Mock analytics generation
    analytics = mock_analytics()

    return post_message, transcrption_json, translated_json, analytics

def generate_dashboard(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
# Gradio Interface with Tabs
def build_interface():
    with gr.Blocks() as demo:
        with gr.Tab("Content Management"):
            gr.Markdown("## Integrated Content Management")
            with gr.Row():
                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")
            with gr.Row():
                post_output = gr.Textbox(label="Posting Status", interactive=False)
                transcription_output = gr.JSON(label="Transcription JSON File")
                translated_output = gr.JSON(label="Translated JSON File")
            with gr.Row():
                processed_video_output = gr.File(label="Download Processed Video", interactive=False)  # Download button
            submit_button.click(
                upload_and_manage, 
                inputs=[file_input, platform_input, language_input], 
                outputs=[post_output, transcription_output, translated_output, processed_video_output]
            )

        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, inputs=[gr.State()], outputs=[analytics_output])

    return demo

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