# Necessary imports import sys from typing import Any, Dict import spaces # Local imports from src.utils.video_processing import encode_video from src.config import ( device, model_name, system_prompt, sampling, stream, top_p, top_k, temperature, repetition_penalty, max_new_tokens, ) from src.app.model import load_model_and_tokenizer from src.logger import logging from src.exception import CustomExceptionHandling # Model and tokenizer model, tokenizer = load_model_and_tokenizer(model_name, device) @spaces.GPU(duration=120) def describe_video(video: str, question: str) -> str: """ Describes a video by generating an answer to a given question. Args: - video (str): The path to the video file. - question (str): The question to be answered about the video. Returns: str: The generated answer to the question. """ try: # Encode the video frames frames = encode_video(video) # Message format for the model msgs = [{"role": "user", "content": frames + [question]}] # Set decode params for video params: Dict[str, Any] = { "use_image_id": False, "max_slice_nums": 1, # Use 1 if CUDA OOM and video resolution > 448*448 } # Generate the answer answer = model.chat( image=None, msgs=msgs, tokenizer=tokenizer, sampling=sampling, stream=stream, top_p=top_p, top_k=top_k, temperature=temperature, repetition_penalty=repetition_penalty, max_new_tokens=max_new_tokens, system_prompt=system_prompt, **params ) # Log the successful generation of the answer logging.info("Answer generated successfully.") # Return the answer return "".join(answer) # Handle exceptions that may occur during answer generation except Exception as e: # Custom exception handling raise CustomExceptionHandling(e, sys) from e