app.py

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])