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import streamlit as st
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)
import whale_viewer as viewer
from hf_push_observations import push_observations
from utils.grid_maker import gridder
from utils.metadata_handler import metadata2md
def cetacean_classify(cetacean_classifier):
"""Cetacean classifier using the saving-willy model from Saving Willy Hugging Face space.
For each image in the session state, classify the image and display the top 3 predictions.
Args:
cetacean_classifier ([type]): saving-willy model from Saving Willy Hugging Face space
"""
images = st.session_state.images
observations = st.session_state.observations
hashes = st.session_state.image_hashes
batch_size, row_size, page = gridder(hashes)
grid = st.columns(row_size)
col = 0
o=1
for hash in hashes:
image = images[hash]
with grid[col]:
st.image(image, use_column_width=True)
observation = observations[hash].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", viewer.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 = viewer.WHALE_CLASSES.index(pred1) if pred1 in viewer.WHALE_CLASSES else None
selected_class = st.selectbox(f"Species for observation {str(o)}", viewer.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 {str(o)} to THE INTERNET!", on_click=push_observations)
# TODO: the metadata only fills properly if `validate` was clicked.
st.markdown(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)
st.markdown(f"Top 3 Predictions for observation {str(o)}")
for i in range(len(whale_classes)):
viewer.display_whale(whale_classes, i)
o += 1
col = (col + 1) % row_size |