import streamlit as st
import io

# 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()

from model import *
download_files()
sample_images = get_samples()
v, il = VirTexModel(), ImageLoader()

for s in sample_images:
    subreddit, caption = v.predict(il.load(s))
    print("=====================")
    print(subreddit)
    print(caption)