video-ai/app.py

from openai import OpenAI
import os
import math
from moviepy import VideoFileClip
import tempfile
from openai_client import OpenAIClient

MAX_FILE_SIZE = 26214400  # 25MB in bytes
CHUNK_SIZE = MAX_FILE_SIZE // 2  # Split into ~12MB chunks
OUTPUT_DIR = "E:\\sources\\PDC\\video_ai\\videos"

def extract_audio(video_path):
    """Extract audio from video file and return path to audio file."""
    print("Extracting audio from video file...")
    temp_audio_path = os.path.join(OUTPUT_DIR, "temp_audio.mp3")
    video = VideoFileClip(video_path)
    video.audio.write_audiofile(temp_audio_path)
    video.close()
    return temp_audio_path

def split_audio_file(file_path):
    """Split an audio file into smaller chunks using binary reading."""
    chunks_dir = os.path.join(OUTPUT_DIR, 'chunks')
    os.makedirs(chunks_dir, exist_ok=True)
    
    chunks = []
    file_size = os.path.getsize(file_path)
    total_chunks = math.ceil(file_size / CHUNK_SIZE)
    
    with open(file_path, 'rb') as source_file:
        for chunk_number in range(total_chunks):
            chunk_filename = os.path.join(chunks_dir, f"chunk_{chunk_number}.mp3")
            with open(chunk_filename, 'wb') as chunk_file:
                chunk_data = source_file.read(CHUNK_SIZE)
                chunk_file.write(chunk_data)
            chunks.append(chunk_filename)
    
    return chunks

def process_audio_file(client, file_path, response_format):
    """Process a single audio file and return its transcription."""
    with open(file_path, "rb") as audio_file:
        transcript = client.audio.transcriptions.create(
            model="whisper-1",
            file=audio_file,
            response_format=response_format
        )
    return transcript

def summarize_transcription(client, transcript):
    """Use GPT-4 to summarize the transcription."""
    response = client.completions.create(
        model="gpt-4o",
        prompt=f"Please summarize the following transcription: {transcript}",
        max_tokens=12000,
        temperature=0.5
    )
    return response.choices[0].text.strip()

def convert_video_to_audio_with_chunking(file_path):
    """Convert video to audio and handle chunking if necessary."""
    is_video = file_path.lower().endswith('.mp4')
    audio_path = extract_audio(file_path) if is_video else file_path
    
    try:
        file_size = os.path.getsize(audio_path)
        if file_size > MAX_FILE_SIZE:
            print(f"File size ({file_size} bytes) exceeds the maximum limit. Splitting into chunks...")
            return split_audio_file(audio_path), audio_path, is_video
        return [audio_path], audio_path, is_video
    except Exception as e:
        print(f"Error in video/audio conversion: {e}")
        raise

def transcribe_audio(client, audio_chunks, response_format="text"):
    """Handle the transcription of audio chunks."""
    try:
        full_transcript = ""
        for i, chunk_path in enumerate(audio_chunks, 1):
            print(f"Processing chunk {i} of {len(audio_chunks)}...")
            chunk_transcript = process_audio_file(client, chunk_path, response_format)
            print(f"Chunk {i} transcript:")
            print(chunk_transcript)
            
            if response_format == "text":
                full_transcript += chunk_transcript + " "
            else:  # vtt format
                full_transcript += chunk_transcript + "\n\n"
        
        transcript = full_transcript.strip()
        
        # Save the transcript
        transcript_filename = os.path.join(OUTPUT_DIR, "transcript.txt")
        with open(transcript_filename, "w") as f:
            f.write(transcript)
        print(f"Transcript saved to {transcript_filename}")
        
        return transcript
    except Exception as e:
        print(f"Error in transcription: {e}")
        raise

def generate_summary(openai_client, transcript):
    """Generate and save a summary of the transcript."""
    try:
        summary = summarize_transcription(openai_client, transcript)
        print("Summary:")
        print(summary)
        
        # Save the summary
        summary_filename = os.path.join(OUTPUT_DIR, "summary.txt")
        with open(summary_filename, "w") as f:
            f.write(summary)
        print(f"Summary saved to {summary_filename}")
        
        return summary
    except Exception as e:
        print(f"Error in summary generation: {e}")
        raise

def cleanup_files(audio_path, is_video, chunks):
    """Clean up temporary files."""
    try:
        # Clean up chunks
        for chunk_path in chunks:
            if os.path.exists(chunk_path):
                os.remove(chunk_path)
        
        # Clean up temporary audio file if it was extracted from video
        if is_video and os.path.exists(audio_path):
            os.remove(audio_path)
    except Exception as e:
        print(f"Error during cleanup: {e}")

def main():
    try:
        # Configuration
        file_path = 'E:\\sources\\PDC\\video_ai\\videos\\PdC - Modulo ESG-20250116_105531.mp4'
        response_format = "text"
        api_key = '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'
        
        # Initialize OpenAI clients
        client = OpenAI(api_key=api_key)
        openai_client = OpenAIClient(api_key=api_key)
        
        # Step 1: Convert video to audio and handle chunking
        audio_chunks, audio_path, is_video = convert_video_to_audio_with_chunking(file_path)
        
        try:
            # Step 2: Transcribe the audio
            transcript = transcribe_audio(client, audio_chunks, response_format)
            
            # Step 3: Generate summary
            summary = generate_summary(openai_client, transcript)
            
        finally:
            # Clean up temporary files
            cleanup_files(audio_path, is_video, audio_chunks)
            
    except Exception as e:
        print("Error:", e)

if __name__ == "__main__":
    main()