import streamlit as st
from lib.utils.model import get_model, get_similarities 
from PIL import Image 

st.title('IRRA Text-To-Image-Retrival')
st.markdown('A text-to-image retrieval model implemented from [arXiv: Cross-Modal Implicit Relation Reasoning and Aligning for Text-to-Image Person Retrieval](https://arxiv.org/abs/2303.12501)')

st.header('Inputs')
caption = st.text_input('Description Input')

images = st.file_uploader('Upload images', accept_multiple_files=True)
if images is not None:
    st.image(images) # type: ignore

st.header('Options')
st.subheader('Ranks', help='How many predictions the model is allowed to make')

ranks = st.slider('slider_ranks', min_value=1, max_value=10, label_visibility='collapsed',value=5)

button = st.button('Match most similar', disabled=len(images) == 0 or caption == '')

if button:
    st.header('Results')
    with st.spinner('Loading model'):
        model = get_model()

    st.text(f'IRRA model loaded with {sum(p.numel() for p in model.parameters()) / 1e6:.0f}M parameters')
    
    with st.spinner('Computing and ranking similarities'):
        similarities = get_similarities(caption, images, model).squeeze(0)

    indices = similarities.argsort(descending=True).cpu().tolist()[:ranks]
    
    for i, idx in enumerate(indices):
        c1, c2, c3 = st.columns(3)
        with c1:
            st.text(f'Rank {i + 1}')
        with c2:
            st.image(images[idx])
        with c3:
            st.text(f'Cosine sim {similarities[idx].cpu():.2f}')