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import streamlit as st
import logging
import os
# get a global var for logger accessor in this module
LOG_LEVEL = logging.DEBUG
g_logger = logging.getLogger(__name__)
g_logger.setLevel(LOG_LEVEL)
from grid_maker import gridder
import hf_push_observations as sw_push_obs
import utils.metadata_handler as meta_handler
import whale_viewer as sw_wv
def cetacean_classify(cetacean_classifier, tab_inference):
files = st.session_state.files
images = st.session_state.images
observations = st.session_state.observations
batch_size, row_size, page = gridder(files)
grid = st.columns(row_size)
col = 0
for file in files:
image = images[file.name]
with grid[col]:
st.image(image, use_column_width=True)
observation = observations[file.name].to_dict()
# run classifier model on `image`, and persistently store the output
out = cetacean_classifier(image) # get top 3 matches
st.session_state.whale_prediction1 = out['predictions'][0]
st.session_state.classify_whale_done = True
msg = f"[D]2 classify_whale_done: {st.session_state.classify_whale_done}, whale_prediction1: {st.session_state.whale_prediction1}"
g_logger.info(msg)
# dropdown for selecting/overriding the species prediction
if not st.session_state.classify_whale_done:
selected_class = st.sidebar.selectbox("Species", sw_wv.WHALE_CLASSES,
index=None, placeholder="Species not yet identified...",
disabled=True)
else:
pred1 = st.session_state.whale_prediction1
# get index of pred1 from WHALE_CLASSES, none if not present
print(f"[D] pred1: {pred1}")
ix = sw_wv.WHALE_CLASSES.index(pred1) if pred1 in sw_wv.WHALE_CLASSES else None
selected_class = tab_inference.selectbox("Species", sw_wv.WHALE_CLASSES, index=ix)
observation['predicted_class'] = selected_class
if selected_class != st.session_state.whale_prediction1:
observation['class_overriden'] = selected_class
st.session_state.public_observation = observation
st.button(f"Upload observation for {file.name} to THE INTERNET!", on_click=sw_push_obs.push_observations)
# TODO: the metadata only fills properly if `validate` was clicked.
st.markdown(meta_handler.metadata2md())
msg = f"[D] full observation after inference: {observation}"
g_logger.debug(msg)
print(msg)
# TODO: add a link to more info on the model, next to the button.
whale_classes = out['predictions'][:]
# render images for the top 3 (that is what the model api returns)
#with tab_inference:
st.title(f"Species detected for {file.name}")
for i in range(len(whale_classes)):
sw_wv.display_whale(whale_classes, i)
col = (col + 1) % row_size |