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import sys
import gradio as gr
import jax
from huggingface_hub import snapshot_download
from PIL import Image
from transformers import AutoTokenizer
import torch
from torchvision.io import ImageReadMode, read_image
LOCAL_PATH = snapshot_download("flax-community/medclip")
sys.path.append(LOCAL_PATH)
from src.modeling_medclip import FlaxMedCLIP
from run_medclip import Transform
def prepare_image(image_path, model):
image = read_image(image_path, mode=ImageReadMode.RGB)
preprocess = Transform(model.config.vision_config.image_size)
preprocess = torch.jit.script(preprocess)
preprocessed_image = preprocess(image)
pixel_values = torch.stack([preprocessed_image]).permute(0, 2, 3, 1).numpy()
return pixel_values
def prepare_text(text, tokenizer):
return tokenizer(text, return_tensors="np")
def save_file_to_disk(uplaoded_file):
temp_file = "/tmp/image.jpeg"
im = Image.fromarray(uplaoded_file)
im.save(temp_file)
return temp_file
def load_tokenizer_and_model():
# load the saved model
tokenizer = AutoTokenizer.from_pretrained("allenai/scibert_scivocab_uncased")
model = FlaxMedCLIP.from_pretrained(LOCAL_PATH)
return tokenizer, model
def run_inference(image_path, text, model, tokenizer):
pixel_values = prepare_image(image_path, model)
input_text = prepare_text(text, tokenizer)
model_output = model(
input_text["input_ids"],
pixel_values,
attention_mask=input_text["attention_mask"],
train=False,
return_dict=True,
)
logits = model_output["logits_per_image"]
score = jax.nn.sigmoid(logits)[0][0]
return score
tokenizer, model = load_tokenizer_and_model()
def score_image_caption_pair(uploaded_file, text_input):
local_image_path = save_file_to_disk(uploaded_file)
score = run_inference(
local_image_path, text_input, model, tokenizer).tolist()
return {"Score": score}
image = gr.inputs.Image(shape=(299, 299))
iface = gr.Interface(
fn=score_image_caption_pair, inputs=[image, "text"], outputs=["label"], allow_flagging=False, allow_screenshot=False,
title="Your personal TA",
description="""
The purpose of this demo is to help medical students measure their diagnostic capabilities in purely academic settings.
Under no circumstances should it be used to make a self-diagnosis or confront a real doctor.
"""
)
iface.launch()
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