NameError: name 'CaptionOntology' is not defined

[shashi-netra]

I am trying to use this tool and run a simple script to test it out, but get this error. Am i I missing an import or something?
Sorry if this is some oversight, but could use some help.

from autodistill_llava import LLaVA #, CaptionOntology

ontology=CaptionOntology(
NameError: name 'CaptionOntology' is not defined

[Samuel5106]

@shashi-netra from autodistill.detection import CaptionOntology (just include this in your code)

[shashi-mit]

Would be great to add this to the Quickstart. Also now getting a new error: 'LLaVA' object is not callable. Would be great to do a refresh on the quickstart & documentation since these syntax issues continue to be an issue.

[Samuel5106]

@shashi-mit To help me understand where the error is specifically occurring, could you please give the code and error screenshot?

[shashi-netra]

This is my code and I get this error 'LLaVA' object is not callable

from autodistill_llava import LLaVA#, CaptionOntology
import pprint as pp
from autodistill.detection import CaptionOntology
import requests
import av
import time

llava_model = LLaVA(
    ontology=CaptionOntology(
        {
            "people fighting or assaulting": "violence",
            "person smoking": "smoking",
            "person running": "running",
            "person falling": "falling",
            "person vandalizing": "vandalizing",
        }
    )
)

def read_video(video_url):
    frame_images = []
    with av.open(video_url) as container:
        stream = container.streams.video[0]
        for frame in container.decode(stream):
            frame_images.append(frame.to_image())
    return frame_images


def run_llava(video_file):
    frame_images = read_video(video_file)
    preds = llava_model(frame_images)
    return preds


def process_videos():
    video_files = [...]
    for video_file in video_files:
        try:
            print("[+] Processing video: ", video_file)
            start_time = time.time()
            # Run the model
            preds = run_llava(video_file)
            pp.pprint(preds)

            duration = time.time() - start_time
            print(f"Duration: {duration}")
        except Exception as e:
            print(e)
            continue

[shashi-mit]

@Samuel5106 can you please help with this error

[Samuel5106]

@Samuel5106 can you please help with this error

It is incorrect to utilize the object llava_model as a function in your code, as suggested by the error 'LLaVA' object is not callable. This usually occurs when you attempt to call an object method instead of the object itself.

To remedy this, locate the appropriate technique to apply for predicting the frames by consulting the LLaVA class's implementation or documentation. If a predict or comparable method exists, you ought to use it rather than calling the object directly.

The run_llava function can be changed as follows to invoke the appropriate method on the llava_model object:

`from autodistill_llava import LLaVA
import pprint as pp
from autodistill.detection import CaptionOntology
import requests
import av
import time

llava_model = LLaVA(
ontology=CaptionOntology(
{
"people fighting or assaulting": "violence",
"person smoking": "smoking",
"person running": "running",
"person falling": "falling",
"person vandalizing": "vandalizing",
}
)
)

def read_video(video_url):
frame_images = []
with av.open(video_url) as container:
stream = container.streams.video[0]
for frame in container.decode(stream):
frame_images.append(frame.to_image())
return frame_images

def run_llava(video_file):
frame_images = read_video(video_file)
# Use the correct method for making predictions, e.g., predict
preds = llava_model.predict(frame_images)
return preds

def process_videos():
video_files = ["video1.mp4", "video2.mp4"] # Replace with actual video file paths
for video_file in video_files:
try:
print("[+] Processing video: ", video_file)
start_time = time.time()
# Run the model
preds = run_llava(video_file)
pp.pprint(preds)

        duration = time.time() - start_time
        print(f"Duration: {duration}")
    except Exception as e:
        print(e)
        continue

`