Case-Study-1 / blip_image_caption_large.py
Julian-Hans's picture
implemented option to use inference endpoints, implemented parameter selection, updated UI, cleaned up return formats of models
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# external imports
from transformers import pipeline
from huggingface_hub import InferenceClient
# local imports
import config
class Blip_Image_Caption_Large:
def __init__(self):
pass
def caption_image(self, image_path, use_local_caption):
if use_local_caption:
return self.caption_image_local_pipeline(image_path)
else:
return self.caption_image_api(image_path)
def caption_image_local_pipeline(self, image_path):
self.local_pipeline = pipeline("image-to-text", model=config.IMAGE_CAPTION_MODEL)
result = self.local_pipeline(image_path)[0]['generated_text']
return result
def caption_image_api(self, image_path):
client = InferenceClient(config.IMAGE_CAPTION_MODEL, token=config.HF_API_TOKEN)
result = client.image_to_text(image_path).generated_text
return result