from grid.model.perception.vlm.video_llava import VideoLLaVA
car = AirGenCar()

# We will be capturing an image from the AirGen simulator 
# and run model inference on it.

img =  car.getImage("front_center", "rgb").data

model = VideoLLaVA(use_local = True)
result = model.run(rgbimage=img, prompt=<prompt>)
print(result)
The VideoLLaVA class provides a wrapper for the Video-LLaVA model, which answers questions about visual media (videos).
​
class VideoLLaVA()
​
use_local
boolean
default:"True"
If True, inference call is run on the local VM, else offloaded onto GRID-Cortex. Defaults to True.
This model is currently not available via Cortex.
​
def run()
​
image
np.ndarray
The input RGB image of shape (M,N,3)(M,N,3).
​
video
str
The path to the input video.
​
prompt
str
required
The question to answer about the media.
​
Returns
str
The response to the prompt.
from grid.model.perception.vlm.video_llava import VideoLLaVA
car = AirGenCar()

# We will be capturing an image from the AirGen simulator 
# and run model inference on it.

img =  car.getImage("front_center", "rgb").data

model = VideoLLaVA(use_local = True)
result = model.run(rgbimage=img, prompt=<prompt>)
print(result)
This code is licensed under the Apache 2.0 License.