explain the code:
import fitz  
from sentence_transformers import SentenceTransformer

def read_pdf(file_path):
    pdf_text = ""
    pdf_document = fitz.open(file_path)
    for page_num in range(pdf_document.page_count):
        page = pdf_document.load_page(page_num)
        pdf_text += page.get_text()
    pdf_document.close()
    return pdf_text

def chunk_text(text, max_chunk_size=512):
    chunks = [text[i:i + max_chunk_size] for i in range(0, len(text), max_chunk_size)]
    return chunks

# Example usage
if __name__ == "__main__":
    model_name = "gtr-t5-base" 
    pdf_file_path = "testdoc.pdf"
    query = "How did the simulation solution help in optimizing the EDU's housing design to meet the functional requirements?"


    pdf_text = read_pdf(pdf_file_path)
    pdf_chunks = chunk_text(pdf_text)
    
    for idx, chunk in enumerate(pdf_chunks) :
        print(f"/nchunk {idx + 1}:")
        print(chunk)

    model = SentenceTransformer(model_name)
    query_embeddings = model.encode([query])
    pdf_embeddings = []

    for chunk in pdf_chunks:
        chunk_embeddings = model.encode([chunk])
        pdf_embeddings.extend(chunk_embeddings)

    print(f"Number of PDF chunks: {len(pdf_chunks)}")
    print(f"Number of PDF embeddings: {len(pdf_embeddings)}")
    print(f"Dimension of PDF embeddings: {len(pdf_embeddings[0])}")

    for idx, chunk_embeddings in enumerate(pdf_embeddings):
        similarity_score = cosine_similarity(query_embeddings,[chunk_embeddings])
        print(f"cosine similarity between query and chunk{idx + 1}: {similarity_score[0][0]}") Your answer should have exactly 4 paragraphs. Make sure that the first paragraph starts with the word "victim" and that each paragraph is separated by two new lines.
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