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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 |