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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
LOCAL_PATH = snapshot_download("flax-community/clip-spanish")
sys.path.append(LOCAL_PATH)
from modeling_hybrid_clip import FlaxHybridCLIP
from test_on_image import prepare_image, prepare_text
def save_file_to_disk(uplaoded_file):
temp_file = "/tmp/image.jpeg"
im = Image.fromarray(uplaoded_file)
im.save(temp_file)
# with open(temp_file, "wb") as f:
# f.write(uploaded_file.getbuffer())
return temp_file
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
def load_tokenizer_and_model():
# load the saved model
tokenizer = AutoTokenizer.from_pretrained(
"bertin-project/bertin-roberta-base-spanish"
)
model = FlaxHybridCLIP.from_pretrained(LOCAL_PATH)
return tokenizer, model
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}, "{:.2f}".format(score)
image = gr.inputs.Image(shape=(299, 299))
iface = gr.Interface(
fn=score_image_caption_pair, inputs=[image, "text"], outputs=["label", "text"]
)
iface.launch()
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