Spaces:
Running
Running
File size: 2,854 Bytes
185fa42 2085a8b 185fa42 741fc4c 185fa42 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 |
import streamlit as st
from transcript_extractor import get_transcript
import logging
import sys
# Configure logging for stdout only
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
stream=sys.stdout
)
logger = logging.getLogger(__name__)
def process_single_video(db_handler, data_processor, video_id, embedding_model):
"""Process a single video for indexing"""
try:
# Check for existing index
existing_index = db_handler.get_elasticsearch_index_by_youtube_id(video_id)
if existing_index:
logger.info(f"Video {video_id} already processed. Using existing index.")
return existing_index
# Get transcript data
transcript_data = get_transcript(video_id)
if not transcript_data:
logger.error(f"Failed to retrieve transcript for video {video_id}")
return None
# Process transcript
processed_data = data_processor.process_transcript(video_id, transcript_data)
if not processed_data:
logger.error(f"Failed to process transcript for video {video_id}")
return None
# Prepare video data
video_data = {
'video_id': video_id,
'title': transcript_data['metadata'].get('title', 'Unknown Title'),
'author': transcript_data['metadata'].get('author', 'Unknown Author'),
'upload_date': transcript_data['metadata'].get('upload_date', 'Unknown Date'),
'view_count': int(transcript_data['metadata'].get('view_count', 0)),
'like_count': int(transcript_data['metadata'].get('like_count', 0)),
'comment_count': int(transcript_data['metadata'].get('comment_count', 0)),
'video_duration': transcript_data['metadata'].get('duration', 'Unknown Duration'),
'transcript_content': processed_data['content']
}
# Save to database
db_handler.add_video(video_data)
# Build index
index_name = f"video_{video_id}_{embedding_model}".lower()
index_name = data_processor.build_index(index_name)
if index_name:
# Save index information
embedding_model_id = db_handler.add_embedding_model(embedding_model, "Description of the model")
video_record = db_handler.get_video_by_youtube_id(video_id)
if video_record:
db_handler.add_elasticsearch_index(video_record[0], index_name, embedding_model_id)
logger.info(f"Successfully processed video: {video_data['title']}")
return index_name
logger.error(f"Failed to process video {video_id}")
return None
except Exception as e:
logger.error(f"Error processing video {video_id}: {str(e)}")
return None |