import os import google.generativeai as genai from pathlib import Path import logging logger = logging.getLogger(__name__) class CoverageGenerator: def __init__(self): api_key = os.getenv("GOOGLE_API_KEY") if not api_key: raise ValueError("GOOGLE_API_KEY not found") genai.configure(api_key=api_key) self.model = genai.GenerativeModel('gemini-pro') self.chunk_size = 8000 def count_tokens(self, text: str) -> int: """Estimate token count using simple word-based estimation""" words = text.split() return int(len(words) * 1.3) def chunk_screenplay(self, text: str) -> list: """Split screenplay into chunks with overlap for context""" logger.info("Chunking screenplay...") scenes = text.split("\n\n") chunks = [] current_chunk = [] current_size = 0 overlap_scenes = 2 for i, scene in enumerate(scenes): scene_size = self.count_tokens(scene) if current_size + scene_size > self.chunk_size and current_chunk: overlap = current_chunk[-overlap_scenes:] if len(current_chunk) > overlap_scenes else current_chunk chunks.append("\n\n".join(current_chunk)) current_chunk = overlap + [scene] current_size = sum(self.count_tokens(s) for s in current_chunk) else: current_chunk.append(scene) current_size += scene_size if current_chunk: chunks.append("\n\n".join(current_chunk)) logger.info(f"Split screenplay into {len(chunks)} chunks with context overlap") return chunks def generate_synopsis(self, chunk: str, chunk_num: int = 1, total_chunks: int = 1) -> str: """Generate synopsis for a single chunk""" logger.debug(f"Generating synopsis for chunk {chunk_num}/{total_chunks}") prompt = f"""As an experienced script analyst, analyze this section ({chunk_num}/{total_chunks}) of the screenplay. Focus on: plot developments, character development, narrative connections, themes Screenplay section: {chunk}""" try: response = self.model.generate_content(prompt) logger.debug(f"Generated synopsis for chunk {chunk_num}") return response.text except Exception as e: logger.error(f"Error processing chunk {chunk_num}: {str(e)}") return None def generate_final_synopsis(self, chunk_synopses: list) -> str: """Combine chunk synopses into final coverage""" logger.info("Generating final synopsis") combined_text = "\n\n".join([f"Section {i+1}:\n{synopsis}" for i, synopsis in enumerate(chunk_synopses)]) prompt = f"""Synthesize these section summaries into a comprehensive coverage document with: 1. Complete narrative arc 2. Character development 3. Major themes 4. Key turning points 5. Core conflict and resolution Section summaries: {combined_text}""" try: response = self.model.generate_content(prompt) logger.info("Final synopsis generated") return response.text except Exception as e: logger.error(f"Error generating final synopsis: {str(e)}") return None def generate_coverage(self, screenplay_path: Path) -> bool: """Main method to generate coverage document""" logger.info("Starting coverage generation") try: with open(screenplay_path, 'r', encoding='utf-8') as f: screenplay_text = f.read() chunks = self.chunk_screenplay(screenplay_text) chunk_synopses = [] for i, chunk in enumerate(chunks, 1): logger.info(f"Processing chunk {i}/{len(chunks)}") synopsis = self.generate_synopsis(chunk, i, len(chunks)) if synopsis: chunk_synopses.append(synopsis) else: logger.error(f"Failed to process chunk {i}") return False final_synopsis = self.generate_final_synopsis(chunk_synopses) if not final_synopsis: return False output_path = screenplay_path.parent / "coverage.txt" with open(output_path, 'w', encoding='utf-8') as f: f.write("SCREENPLAY COVERAGE\n\n") f.write(final_synopsis) logger.info("Coverage generation complete") return True except Exception as e: logger.error(f"Error in coverage generation: {str(e)}") return False