Spaces:
Runtime error
Runtime error
| import spacy | |
| # download the En model firstly by：python -m spacy download en_core_web_sm | |
| nlp = spacy.load("en_core_web_sm") | |
| # This is only a test method for color prompt, like "a red car and a blue bench" | |
| def split_prompt(prompt): | |
| # Step 1: parse | |
| doc = nlp(prompt) | |
| chunks = list(doc.noun_chunks) | |
| # If only one chunk and there's "and", try splitting on " and " | |
| if len(chunks) < 2 and " and " in prompt: | |
| sub_prompts = [seg.strip() for seg in prompt.split(" and ")] | |
| new_chunks = [] | |
| for sub in sub_prompts: | |
| sub_doc = nlp(sub) | |
| sub_noun_chunks = list(sub_doc.noun_chunks) | |
| if not sub_noun_chunks: | |
| new_chunks.append(sub_doc[:]) | |
| else: | |
| new_chunks.extend(sub_noun_chunks) | |
| chunks = new_chunks | |
| if len(chunks) < 2: | |
| sps = [prompt] | |
| nps = [prompt] | |
| return (sps, nps) | |
| # Step 2: split on "of" within each chunk | |
| final_chunks = [] | |
| for chunk in chunks: | |
| chunk_tokens = list(chunk) | |
| of_prep_token = None | |
| for token in chunk_tokens: | |
| if token.lemma_ == "of" and token.dep_ == "prep": | |
| of_prep_token = token | |
| break | |
| if of_prep_token: | |
| of_subtree = list(of_prep_token.subtree) | |
| chunk1_tokens = [t for t in chunk_tokens if t not in of_subtree] | |
| pobj_tokens = [t for t in of_prep_token.children if t.dep_ == "pobj"] | |
| if pobj_tokens: | |
| pobj_root = pobj_tokens[0] | |
| chunk2_tokens = list(pobj_root.subtree) | |
| else: | |
| chunk2_tokens = [] | |
| if chunk1_tokens and chunk2_tokens: | |
| doc_obj = chunk1_tokens[0].doc | |
| chunk1_span = doc_obj[chunk1_tokens[0].i : chunk1_tokens[-1].i + 1] | |
| chunk2_span = doc_obj[chunk2_tokens[0].i : chunk2_tokens[-1].i + 1] | |
| final_chunks.append(chunk1_span) | |
| final_chunks.append(chunk2_span) | |
| continue | |
| else: | |
| final_chunks.append(chunk) | |
| else: | |
| final_chunks.append(chunk) | |
| if len(final_chunks) < 2: | |
| sps = [prompt] | |
| nps = [prompt] | |
| return (sps, nps) | |
| def process_chunk(ch): | |
| full = ch.text | |
| det = "" | |
| for token in ch: | |
| if token.dep_ == "det": | |
| det = token.text | |
| break | |
| head = ch.root.text | |
| stripped = (det + " " if det else "") + head | |
| return (full, stripped) | |
| processed = [process_chunk(ch) for ch in final_chunks] | |
| variants = [] | |
| for i in range(len(processed)): | |
| parts = [] | |
| for j, (full, stripped) in enumerate(processed): | |
| if j == i: | |
| parts.append(full) | |
| else: | |
| parts.append(stripped) | |
| variants.append(" and ".join(parts)) | |
| # simple_version = " and ".join(ch.root.text for ch in final_chunks) | |
| simple_version = " and ".join(stripped for full, stripped in processed) | |
| subjects = [ch.root.text for ch in final_chunks] | |
| sps = [simple_version] | |
| sps.extend(variants) | |
| nps = subjects | |
| return (sps, nps) | |