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import os import sys import logging import google.generativeai as genai from google.generativeai.types import File from typing import Optional # Set up logger logger = logging.getLogger(__name__) GEMINI_API_KEY = None def get_gemini_api_key() -> str: """ Get Gemini API key from command line arguments or environment variable. Caches the key in memory after first read. Priority: command line argument > environment variable Returns: str: The Gemini API key. Raises: Exception: If no key is provided in command line arguments or environment. """ global GEMINI_API_KEY if GEMINI_API_KEY is None: # Try command line argument first if "--gemini-api-key" in sys.argv: token_index = sys.argv.index("--gemini-api-key") + 1 if token_index < len(sys.argv): GEMINI_API_KEY = sys.argv[token_index] logger.info(f"Using Gemini API key from command line arguments") else: raise Exception("--gemini-api-key argument provided but no key value followed it") # Try environment variable elif os.getenv("GEMINI_API_KEY"): GEMINI_API_KEY = os.getenv("GEMINI_API_KEY") logger.info(f"Using Gemini API key from environment variable") else: raise Exception("Gemini API key must be provided via '--gemini-api-key' command line argument or 'GEMINI_API_KEY' environment variable") return GEMINI_API_KEY def configure_gemini() -> genai.GenerativeModel: """ Configure Gemini API with the API key and return a model instance. Returns: genai.GenerativeModel: Configured Gemini model instance for video analysis """ api_key = get_gemini_api_key() genai.configure(api_key=api_key) # Use Gemini 2.0 Flash for video analysis (more cost-effective than Pro) model = genai.GenerativeModel('gemini-2.0-flash-exp') logger.info("Gemini API configured successfully") return model def upload_video_to_gemini(video_path: str) -> File: """ Upload a video file to Gemini File API for analysis. Args: video_path: Path to the video file to upload Returns: genai.File: The uploaded file object for use in analysis Raises: Exception: If upload fails """ try: # Upload video file video_file = genai.upload_file(path=video_path) # Wait for processing to complete import time while video_file.state.name == "PROCESSING": time.sleep(2) video_file = genai.get_file(video_file.name) if video_file.state.name == "FAILED": raise Exception(f"Video processing failed: {video_file.state}") logger.info(f"Video uploaded successfully: {video_file.name}") return video_file except Exception as e: logger.error(f"Failed to upload video to Gemini: {str(e)}") raise def analyze_video_with_gemini(model: genai.GenerativeModel, video_file: File, prompt: str) -> str: """ Analyze a video using Gemini with a custom prompt. Args: model: Configured Gemini model instance video_file: Uploaded video file from Gemini File API prompt: Analysis prompt for the video Returns: str: Analysis results from Gemini Raises: Exception: If analysis fails """ try: # Generate analysis response = model.generate_content([video_file, prompt]) if not response.text: raise Exception("Gemini returned empty response") logger.info("Video analysis completed successfully") return response.text except Exception as e: logger.error(f"Video analysis failed: {str(e)}") raise def cleanup_gemini_file(file_name: str): """ Delete a file from Gemini File API to free up storage. Args: file_name: Name of the file to delete """ try: genai.delete_file(file_name) logger.info(f"Cleaned up Gemini file: {file_name}") except Exception as e: logger.warning(f"Failed to cleanup Gemini file {file_name}: {str(e)}")