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clip_ask.py
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clip_ask.py
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from term import Atom
from pyrlang.gen.server import GenServer
from pyrlang.gen.decorators import call, cast, info
from PIL import Image
import io
import sys
from transformers import CLIPProcessor, CLIPModel
PROMPTS=[
"photo",
"dog photo",
"cat photo",
"food photo",
"meme",
"painting",
"drawing",
"selfie",
"portrait photography",
"tv capture",
"screenshot",
"terminal/ssh/console screenshot",
"twitter screenshot",
"chat log",
"4chan screenshot",
"scanned document",
"book picture"]
class ClipAsk(GenServer):
def __init__(self, node, logger) -> None:
super().__init__()
node.register_name(self, Atom('clip_ask'))
self.logger = logger
self.model = None
self.processor = None
self.ready = False
print("clipask: starting")
mypid = self.pid_
node.send_nowait(mypid, mypid, "register")
self.logger.info("initialized process: clip_ask.")
@info(0, lambda msg: msg == 'register')
def setup(self, msg):
print("clipask: doing setup")
self.logger.info("image_to_text_vit_gpt2: setup...")
self.model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
self.processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
self.logger.info("clip_ask: setup finished.")
self.ready = True
print("clipask: ready")
@call(1, lambda msg: type(msg) == tuple and msg[0] == Atom("run"))
def run(self, msg):
if self.ready:
self.logger.info("clip_ask: inference")
image = Image.open(io.BytesIO(msg[1]))
inputs = self.processor(text=PROMPTS, images=image, return_tensors="pt", padding=True)
outputs = self.model(**inputs)
logits_per_image = outputs.logits_per_image
probs = logits_per_image.softmax(dim=1)
labels_with_probs = dict(zip(PROMPTS, probs.detach().numpy()[0]))
results = dict(sorted(labels_with_probs.items(), key=lambda item: item[1], reverse=True))
return (Atom('ok'), {k: v.item() for k, v in results.items()})
else:
return (Atom('error'), Atom('not_ready'))

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