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from streamlit.delta_generator import DeltaGenerator
import streamlit as st
from huggingface_hub import HfApi
import json
import tempfile
import logging
# get a global var for logger accessor in this module
LOG_LEVEL = logging.DEBUG
g_logger = logging.getLogger(__name__)
g_logger.setLevel(LOG_LEVEL)
def push_observations(tab_log:DeltaGenerator=None):
"""
Push the observations to the Hugging Face dataset
Args:
tab_log (streamlit.container): The container to log messages to. If not provided,
log messages are in any case written to the global logger (TODO: test - didn't
push any observation since generating the logger)
"""
# we get the observation from session state: 1 is the dict 2 is the image.
# first, lets do an info display (popup)
metadata_str = json.dumps(st.session_state.public_observation)
st.toast(f"Uploading observations: {metadata_str}", icon="🦭")
tab_log = st.session_state.tab_log
if tab_log is not None:
tab_log.info(f"Uploading observations: {metadata_str}")
# get huggingface api
import os
token = os.environ.get("HF_TOKEN", None)
api = HfApi(token=token)
f = tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False)
f.write(metadata_str)
f.close()
st.info(f"temp file: {f.name} with metadata written...")
path_in_repo= f"metadata/{st.session_state.public_observation['author_email']}/{st.session_state.public_observation['image_md5']}.json"
msg = f"fname: {f.name} | path: {path_in_repo}"
print(msg)
st.warning(msg)
# rv = api.upload_file(
# path_or_fileobj=f.name,
# path_in_repo=path_in_repo,
# repo_id="Saving-Willy/temp_dataset",
# repo_type="dataset",
# )
# print(rv)
# msg = f"observation attempted tx to repo happy walrus: {rv}"
g_logger.info(msg)
st.info(msg)
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