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"""Streamlit app"""
from os.path import join, exists, dirname, abspath
from glob import glob
import numpy as np
import pandas as pd
import torch
import torchvision
import streamlit as st
import warnings
warnings.simplefilter(action='ignore')
curr_filepath = abspath(__file__)
repo_path = dirname(curr_filepath)
temporal_terms = [
"then",
"before",
"after",
"followed by",
"preceded by",
]
def make_grid(cols,rows):
grid = [0]*cols
for i in range(cols):
with st.container():
grid[i] = st.columns(rows)
return grid
if __name__ == "__main__":
# Streamlit app code
st.set_page_config(layout="wide")
st.title("Clips from AudioCaps (possibly of temporal nature) 🎬")
# load data
if "df" not in st.session_state:
splits = ["train.csv", "val.csv", "test.csv"]
dfs = [pd.read_csv(join(repo_path, "data", split)) for split in splits]
df = pd.concat(dfs, axis=0)
# Filter df based on whether the temporal term is in the sentence
indices = df.caption.apply(lambda x: any([term in x for term in temporal_terms]))
df = df[indices]
st.session_state.df = df
else:
df = st.session_state.df
st.markdown(f"**Total number of relevant clips**: {len(df)}", unsafe_allow_html=True)
reload_button = st.button("Reload")
NUM = 9
indices = np.random.randint(0, len(st.session_state.df), NUM)
if reload_button:
indices = np.random.randint(0, len(st.session_state.df), NUM)
grid = make_grid(3, 3)
per_video_width = 360
per_video_height = 240
for i, idx in enumerate(indices):
row = i // 3
col = i % 3
video_id = df.iloc[idx].youtube_id
start = df.iloc[idx].start_time
end = start + 10.
url = f"https://www.youtube.com/embed/{video_id}?start={int(start)}&end={int(end)}"
html_code = f"""
<iframe height="{per_video_height}" width="{per_video_width}" src="{url}" frameborder="0" allowfullscreen></iframe>
"""
grid[row][col].markdown(html_code, unsafe_allow_html=True)
grid[row][col].markdown(f"**Caption**: {df.iloc[idx].caption}", unsafe_allow_html=True)
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