import streamlit as st
import io

# x = st.slider("Select a value")
# st.write(x, "squared is", x * x)

st.title("Image Captioning Demo from Redcaps")
st.sidebar.markdown(
    """
    Image Captioning Model from VirTex trained on Redcaps
    """
)

with st.spinner("Loading Model"):
    from model import *
    sample_images = glob.glob("./samples/*.jpg")
    download_files()
    virtexModel = VirTexModel()
    imageLoader = ImageLoader()

random_image = get_rand_img(sample_images)

st.sidebar.title("Select a sample image")
sample_image = st.sidebar.selectbox(
    "",
    sample_images
)

if st.sidebar.button("Random Sample Image"):
    random_image = get_rand_img(sample_images)
    sample_image = None
    
uploaded_image = None
with st.sidebar.form("file-uploader-form", clear_on_submit=True):
    uploaded_file = st.file_uploader("Choose a file")
    submitted = st.form_submit_button("Submit")
    if uploaded_file is not None and submitted:
        uploaded_image = Image.open(io.BytesIO(uploaded_file.get_values()))

if uploaded_image is None and submitted:
    st.write("Please select a file to upload")

else:
    image_file = sample_image if sample_image is not None else random_image
    
    image = uploaded_image if uploaded_image is not None else Image.open()
    
    image_dict = imageLoader.transform(image)
    
    show.image(st.image(image_dict["image"]), "Target Image")
    
    with st.spinner("Generating Caption"):
        subreddit, caption = virtexModel.predict(image_dict)
        st.header("Predicted Caption:\n\n")
        st.subheader(f"Subreddit: {subreddit}\n")
        st.subheader(f"Caption: {caption}\n")
        
    image.close()