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from typing import Dict, List, Any
import sys
sys.path.append('./')
from videollama2 import model_init, mm_infer
from videollama2.utils import disable_torch_init
import logging
import os
class EndpointHandler:
def __init__(self, path: str = ""):
"""
Initialize the handler by loading the model and any other necessary components.
Args:
path (str): The path to the model or other necessary files.
"""
disable_torch_init()
self.model_path = 'Aliayub1995/VideoLLaMA2-7B'
self.model, self.processor, self.tokenizer = model_init(self.model_path)
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
logging.info(f"Received data: {data}") # Debugging: Print received data
# Initialize variables
current_path = os.getcwd()
logging.info(f"Current Path: {current_path}")
dir = os.walk("./app")
# Iterate through the generator
for dirpath, dirnames, filenames in dir:
logging.info(f"Current Path: {dirpath}")
logging.info(f"Directories: {dirnames}")
logging.info(f"Files: {filenames}")
logging.info("-" * 40)
logging.info(f"Directory struct: {dir}")
modal = None
modal_path = None
instruct = None
# Extract input data
inputs = data.get("inputs", data)
modal = inputs.get("modal", "video")
modal_path = inputs.get("modal_path", "")
instruct = inputs.get("instruct", "")
logging.info(f"Modal: {modal}, Modal Path: {modal_path}, Instruct: {instruct}") # Debugging: Print extracted values
if not modal_path or not instruct:
raise ValueError("Both 'modal_path' and 'instruct' must be provided in the input data.")
# Perform inference
output = mm_infer(
self.processor[modal](modal_path),
instruct,
model=self.model,
tokenizer=self.tokenizer,
do_sample=False,
modal=modal
)
return [{"output": output}]
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