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| import re | |
| from bot.byte_semantics_base import compact_message, format_chat_reply, normalize_text_for_scene | |
| from bot.byte_semantics_constants import ( | |
| MAX_CHAT_MESSAGE_LENGTH, | |
| MOVIE_TITLE_QUOTE_PATTERN, | |
| MULTIPART_SEPARATOR, | |
| ) | |
| def split_text_for_chat( | |
| text: str, max_len: int = MAX_CHAT_MESSAGE_LENGTH, max_parts: int = 2 | |
| ) -> list[str]: | |
| clean_text = (text or "").strip() | |
| if not clean_text: | |
| return [] | |
| if len(clean_text) <= max_len: | |
| return [clean_text] | |
| parts: list[str] = [] | |
| remaining = clean_text | |
| while remaining and len(parts) < max_parts: | |
| if len(remaining) <= max_len: | |
| parts.append(remaining.strip()) | |
| remaining = "" | |
| break | |
| cut = remaining.rfind("\n", 0, max_len + 1) | |
| if cut < int(max_len * 0.5): | |
| sentence_cuts = [ | |
| remaining.rfind(symbol, 0, max_len + 1) for symbol in (". ", "? ", "! ") | |
| ] | |
| cut = max(sentence_cuts) | |
| if cut < int(max_len * 0.5): | |
| cut = remaining.rfind(" ", 0, max_len + 1) | |
| if cut <= 0: | |
| cut = max_len | |
| chunk = remaining[:cut].strip() | |
| if chunk: | |
| parts.append(chunk) | |
| remaining = remaining[cut:].strip() | |
| if remaining: | |
| if parts: | |
| parts[-1] = compact_message(f"{parts[-1]} {remaining}", max_len=max_len) | |
| else: | |
| parts.append(compact_message(remaining, max_len=max_len)) | |
| return [part for part in parts if part][:max_parts] | |
| def extract_multi_reply_parts(answer_text: str, max_parts: int = 2) -> list[str]: | |
| if not answer_text: | |
| return [] | |
| raw_parts = [ | |
| segment.strip() for segment in answer_text.split(MULTIPART_SEPARATOR) if segment.strip() | |
| ] | |
| if len(raw_parts) >= 2: | |
| normalized = [format_chat_reply(part) for part in raw_parts[:max_parts]] | |
| return [part for part in normalized if part] | |
| chunks = split_text_for_chat(answer_text, max_len=MAX_CHAT_MESSAGE_LENGTH, max_parts=max_parts) | |
| normalized = [format_chat_reply(chunk) for chunk in chunks] | |
| return [part for part in normalized if part] | |
| def extract_movie_title(prompt: str) -> str: | |
| quote_match = MOVIE_TITLE_QUOTE_PATTERN.search(prompt or "") | |
| if quote_match: | |
| return normalize_text_for_scene(quote_match.group("title"), max_len=80) | |
| candidate_match = re.search( | |
| r"ficha\s*t[eé]cnica(?:\s+(?:do|da|de)\s+filme)?(?:\s+(?:do|da|de))?\s+(?P<title>.+)$", | |
| prompt or "", | |
| re.IGNORECASE, | |
| ) | |
| if not candidate_match: | |
| return "" | |
| candidate = candidate_match.group("title").strip(" ?!.:,;") | |
| if not candidate: | |
| return "" | |
| lowered = candidate.lower() | |
| generic_fragments = ( | |
| "que estamos vendo", | |
| "que estamos assistindo", | |
| "que estamos assistindo hoje", | |
| "agora", | |
| ) | |
| if any(fragment in lowered for fragment in generic_fragments): | |
| return "" | |
| if lowered in {"filme", "hoje", "agora"}: | |
| return "" | |
| return normalize_text_for_scene(candidate, max_len=80) | |
| def build_movie_fact_sheet_query(movie_title: str) -> str: | |
| return ( | |
| f"Monte uma ficha tecnica objetiva do filme '{movie_title}' para chat Twitch. " | |
| "Responda em no maximo 4 linhas, sem markdown, com alta densidade e sem enrolacao. " | |
| "Formato obrigatorio: " | |
| "1) Titulo | Ano | Pais. " | |
| "2) Direcao | Genero | Duracao. " | |
| "3) Elenco principal (ate 3 nomes). " | |
| "4) Nota media | Premios-chave (ou N/D). " | |
| "Se nao houver confianca em algum dado, escreva N/D." | |
| ) | |