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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, 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", 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 {file.name}", 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 for {file.name} 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) | |
| #with tab_inference: | |
| st.markdown(f"Top 3 Predictions for {file.name}") | |
| for i in range(len(whale_classes)): | |
| viewer.display_whale(whale_classes, i) | |
| col = (col + 1) % row_size |