classification_with_text/calculate_embeddings.py

import json
import pandas as pd
from sentence_transformers import SentenceTransformer
from pathlib import Path
from tqdm import tqdm

def extract_caption(text_block):
    for line in text_block.splitlines():
        if "CAPTION:" in line.upper():
            return line.split("CAPTION:")[-1].strip()
    return ""

def load_captions_from_files(json_files):
    all_paths = []
    all_captions = []

    for json_path in tqdm(json_files, desc="Reading files"):
        with open(json_path, 'r', encoding='utf-8') as f:
            data = json.load(f)

        for img_path, outer_list in data.items():
            if not outer_list or not outer_list[0]:
                continue
            text_block = outer_list[0][0]
            caption = extract_caption(text_block)
            if caption:
                all_paths.append(img_path)
                all_captions.append(caption)

    return all_paths, all_captions

def compute_and_save_embeddings(json_files, output_csv):
    model = SentenceTransformer('all-MiniLM-L6-v2')
    image_paths, captions = load_captions_from_files(json_files)

    if not captions:
        print("No valid captions found across input files.")
        return

    embeddings = model.encode(captions, show_progress_bar=True)
    df = pd.DataFrame(embeddings)
    df.insert(0, "image_path", image_paths)
    df.to_csv(output_csv, index=False)
    print(f"Saved {len(df)} embeddings from {len(json_files)} files to {output_csv}")

# Example usage
if __name__ == "__main__":
    import glob
    # Collect multiple JSON files
    files = glob.glob("./MBD_text/*.json")  # or provide manually
    compute_and_save_embeddings(files, "combined_caption_embeddings.csv")