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import streamlit as st
import torch
from joblib import load
from PIL import Image
from transformers import VisionEncoderDecoderModel
device = 'cpu'
# tokenizer = load("./pages/tokenizer_v3.joblib")
# feature_extractor = load("./pages/feature_extractor_v3.joblib")
tokenizer = load("tokenizer_v3.joblib")
feature_extractor = load("feature_extractor_v3.joblib")
model = VisionEncoderDecoderModel.from_pretrained("dumperize/movie-picture-captioning")
# model = load("model_img2txt_v3.joblib")
model.load_state_dict(torch.load("model_weights_i2t_fin.pt", map_location=torch.device('cpu')))
# model.eval()
max_length = 512
min_length = 32
num_beams = 7
gen_kwargs = {"max_length": max_length, "min_length": min_length, "num_beams": num_beams}
uploaded_file = st.file_uploader("Выберите изображение обложки книги в формате jpeg или jpg...", type=["jpg", "jpeg"])
if uploaded_file is not None:
image = Image.open(uploaded_file)
st.image(image, caption='Загруженное изображение')
image = image.resize([224,224])
if image.mode != "RGB":
image = image.convert(mode="RGB")
pixel_values = feature_extractor(images=[image], return_tensors="pt").pixel_values
pixel_values = pixel_values.to(device)
output_ids = model.generate(pixel_values, **gen_kwargs)
preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
preds = [pred.strip() for pred in preds]
st.write(preds[0])
# image = Image.open(image_path)
# image = image.resize([224,224])
# if image.mode != "RGB":
# image = image.convert(mode="RGB")
# pixel_values = feature_extractor(images=[image], return_tensors="pt").pixel_values
# pixel_values = pixel_values.to(device)
# output_ids = model.generate(pixel_values, **gen_kwargs)
# preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
# print([pred.strip() for pred in preds])
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