src/pipeline.py

from src.segmenter import detect_event_segments
from src.transcriber import transcribe_video
from src.event_card import parse_game_card
from src.labeler import TogetherLLMLabeler
from src.embedder import XCLIPEmbedder
from src.pinecone_store import PineconeStore
from src.utils import (
    extract_key_frames, save_frames_locally,
    generate_frame_urls, match_transcript_to_events,
    clip_video_segment
)

labeler = TogetherLLMLabeler()
embedder = XCLIPEmbedder()
pinecone = PineconeStore()

def run_pipeline(video_path, game_card_str):
    game_card = parse_game_card(game_card_str)
    transcript = transcribe_video(video_path)
    events = detect_event_segments(video_path)

    matched_events = match_transcript_to_events(events, transcript)

    results = []

    for idx, event in enumerate(matched_events):
        event_id = f"event-{idx}"

        frames = extract_key_frames(video_path, event['start_sec'], event['end_sec'])
        frame_paths = save_frames_locally(frames, event_id)
        frame_urls = generate_frame_urls(frame_paths)

        label = labeler.generate_label(
            game_card=game_card,
            transcript=event['transcript'],
            spatial_context=event['frames'],
            frame_urls=frame_urls
        )

        clip_path = clip_video_segment(video_path, event['start_sec'], event['end_sec'], event_id)

        video_vector = embedder.embed_video(clip_path)

        metadata = {
            "start_sec": event['start_sec'],
            "end_sec": event['end_sec'],
            "label": label
        }

        pinecone.upsert(f"{event_id}-video", video_vector, metadata)

        results.append(metadata)

    return {"events": results}

def search_highlights(query, top_k=5):
    query_vector = embedder.embed_text(query)
    results = pinecone.query(query_vector, filter_key="video", top_k=top_k)
    return [
        f"{r['label']} ({r['start_sec']}s - {r['end_sec']}s)" for r in results
    ]