import streamlit as st

from utils import get_dataset_config_names, get_sequence_names, pretrained_models_dict, full_ref_metrics, no_ref_metrics

# st.header("EVREAL  - Event-based Video Reconstruction Evaluation and Analysis Library")
#
# paper_link = "https://arxiv.org/abs/2305.00434"
# code_link = "https://github.com/ercanburak/EVREAL"
# page_link = "https://ercanburak.github.io/evreal.html"
#
# st.markdown("Paper: " + paper_link, unsafe_allow_html=True)
# st.markdown("Code: " + paper_link, unsafe_allow_html=True)
# st.markdown("Page: " + paper_link, unsafe_allow_html=True)

st.title("Result Analysis Tool")

datasets = get_dataset_config_names("data")
dataset_sequences_dict = {}
for dataset in datasets:
    dataset_sequences_dict[dataset] = get_sequence_names("data", dataset)
datasets = list(dataset_sequences_dict.keys())

pretrained_models = list(pretrained_models_dict.keys())

selected_pretrained_model_names = st.multiselect(
     'Select multiple methods to compare',
     pretrained_models)

col1, col2, col3 = st.columns(3)

# A selectbox on the sidebar, for selecting dataset:
with col1:
    selected_dataset = st.selectbox(
        'Select dataset',
        options=datasets
    )

# A selectbox on the sidebar, for selecting sequence:
with col2:
    selected_sequence = st.selectbox(
        'Select sequence',
        options=dataset_sequences_dict[selected_dataset]
    )

metric_names = no_ref_metrics if selected_dataset in ["ECD_FAST", "MVSEC_NIGHT", "HDR"] else full_ref_metrics
metric_names = [name.upper() for name in metric_names]

selected_metric_names = st.multiselect('Select metrics to display', metric_names)

other_visualizations = ["ground truth frames", "event_rates", "event_images", "histograms"]

selected_viz = st.multiselect('Select other visualizations to display', other_visualizations)

if not st.button('Get Results'):
    st.stop()