|
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 |