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import datetime
from pathlib import Path
import inference
import plotly.express as px
import streamlit as st
import polars as pl
from utils import time_slice
from utils.movie_clips import build_video, get_vid_path
@st.cache_data
def st_run_inference(
model_path: Path,
image_folder: Path,
aggregate_duration: int = 30,
fps: int = 3,
) -> pl.DataFrame:
return inference.run_inference(model_path, image_folder, aggregate_duration, fps)
def inference_page():
with st.form("random"):
selected_file = st.selectbox(
"Select File", [str(p) for p in Path("converted").glob("*") if p.is_dir()]
)
selected_model = st.selectbox(
"Select Model", [str(p) for p in Path("ckpts").rglob("*.ckpt")]
)
st.form_submit_button("Extract Highlights!")
df_out = st_run_inference(
Path(selected_model),
Path(selected_file),
aggregate_duration=10,
)
chart_container = st.container()
cut_off = st.slider(
"Y-Cutoff Highlight",
min_value=df_out["preds"].min() + 1,
max_value=df_out["preds"].max() + 1,
)
with st.expander("Advanced Options"):
st.write("Non available right now.")
fig = px.line(df_out, x="timestamp", y="preds", line_shape="hv")
fig.add_hline(cut_off, line_color="red", line_dash="dash")
with chart_container:
st.plotly_chart(fig)
df = time_slice.create_start_end_time(df_out, cut_off)
times_dict = time_slice.merge_overlaps_into_dict(df)
# event: datetime.time = st.select_slider(
# "Validate event", options=[x["start"] for x in times_dict]
# )
higlight_vid = get_vid_path(
f"{selected_file.replace('converted', 'downloaded')}.mp4",
times_dict,
Path("highlights"),
)
if st.button("Create highlight Video"):
with st.spinner("Creating video..."):
build_video(
f"{selected_file.replace('converted', 'downloaded')}.mp4",
times_dict,
higlight_vid,
)
if higlight_vid.exists():
st.video(str(higlight_vid))
st.info("Right Click to Download", icon="ℹ️")
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