Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,102 Bytes
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# 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
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