human_feedback_ui.py

# To Run: uv run streamlit run human_feedback_ui.py
import streamlit as st
import json
from pathlib import Path
from PIL import Image
import numpy as np

st.set_page_config(layout="wide")

data_dir = Path("data/animations")


# List all samples
json_files = sorted(data_dir.glob("sample-*.webp"))
sample_names = [f.stem for f in json_files]

# Find first sample without .hf.json
def find_next_unlabeled_sample(current=None):
    for name in sample_names:
        hf_path = data_dir / f"{name}.hf.json"
        if not hf_path.exists():
            if current is None or name != current:
                return name
    return sample_names[0] if sample_names else None

# Use session state to track current sample
if "sample_choice" not in st.session_state:
    st.session_state["sample_choice"] = find_next_unlabeled_sample()

sample_choice = st.selectbox("Select sample", sample_names, index=sample_names.index(st.session_state["sample_choice"]) if st.session_state["sample_choice"] in sample_names else 0)
st.session_state["sample_choice"] = sample_choice

json_path = data_dir / f"{sample_choice}.json"
webp_path = data_dir / f"{sample_choice}.webp"

# Load frames
frames = []
if webp_path.exists():
    im = Image.open(webp_path)
    try:
        while True:
            frames.append(im.convert("RGB"))
            im.seek(im.tell() + 1)
    except EOFError:
        pass
else:
    st.error(f"WebP file not found: {webp_path}")



# Load scene change indices from .hf.json if exists, else from .json
hf_path = data_dir / f"{sample_choice}.hf.json"
if hf_path.exists():
    with open(hf_path) as f:
        data = json.load(f)
    scene_change_indices = data.get("scene_change_indices", [])
else:
    with open(json_path) as f:
        data = json.load(f)
    scene_change_indices = data.get("scene_change_indices", [])

# --- Auto-save and move to next if cuts are exactly at 40 and 80 ---
if sorted(scene_change_indices) == [40, 80]:
    out_path = data_dir / f"{sample_choice}.hf.json"
    with open(out_path, "w") as f:
        json.dump({"scene_change_indices": [40, 80]}, f, indent=2)
    next_sample = find_next_unlabeled_sample(current=sample_choice)
    st.info(f"Auto-saved human feedback for {sample_choice} (cuts at 40, 80). Moving to next sample...")
    if next_sample and next_sample != sample_choice:
        st.session_state["sample_choice"] = next_sample
        st.experimental_set_query_params(sample=next_sample)
        st.stop()

st.write(f"Total frames: {len(frames)}")

# Show thumbnails with selection
selected = st.multiselect(
    "Select transition frames (scene changes)",
    options=list(range(len(frames))),
    default=scene_change_indices,
    format_func=lambda i: f"Frame {i}"
)

# Show thumbnails in a grid

# Horizontal slider view for thumbnails

import streamlit.components.v1 as components
import io
import base64
thumb_size = 64  # Slightly larger for visibility
slider_html = "<div style='overflow-x:auto; white-space:nowrap; padding:8px;'>"
for i, frame in enumerate(frames):
    buf = io.BytesIO()
    frame.resize((thumb_size, thumb_size)).save(buf, format='PNG')
    img_b64 = base64.b64encode(buf.getvalue()).decode('utf-8')
    highlight = i in selected
    border = '3px solid red' if highlight else '1px solid #ccc'
    slider_html += f"<span style='display:inline-block; margin:2px; border:{border}; border-radius:4px;'><img src='data:image/png;base64,{img_b64}' style='width:{thumb_size}px;height:{thumb_size}px;'><div style='text-align:center;font-size:10px;color:{'red' if highlight else '#333'}'>{i}</div></span>"
slider_html += "</div>"
components.html(slider_html, height=thumb_size+40)

# Save button


if st.button("Save human feedback"):
    out_path = data_dir / f"{sample_choice}.hf.json"
    with open(out_path, "w") as f:
        json.dump({"scene_change_indices": sorted(selected)}, f, indent=2)
    st.success(f"Saved to {out_path}")
    next_sample = find_next_unlabeled_sample(current=sample_choice)
    if next_sample and next_sample != sample_choice:
        st.info(f"Next sample available: {next_sample}")
        if st.button("Go to next sample"):
            st.session_state["sample_choice"] = next_sample
            # Force rerun by updating query params (Streamlit best practice)
            st.experimental_set_query_params(sample=next_sample)
            st.stop()