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 add_header_text() -> None: """ Add brief explainer text about cetacean classification to the tab """ st.markdown(""" *Run classifer to identify the species of cetean on the uploaded image. Once inference is complete, the top three predictions are shown. You can override the prediction by selecting a species from the dropdown.*""") # func to just run classification, store results. def cetacean_just_classify(cetacean_classifier): images = st.session_state.images observations = st.session_state.observations hashes = st.session_state.image_hashes for hash in hashes: image = images[hash] 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[hash] = out['predictions'][0] st.session_state.classify_whale_done[hash] = True st.session_state.observations[hash].set_top_predictions(out['predictions'][:]) msg = f"[D]2 classify_whale_done for {hash}: {st.session_state.classify_whale_done[hash]}, whale_prediction1: {st.session_state.whale_prediction1[hash]}" g_logger.info(msg) # TODO: what is the difference between public and regular; and why is this not array-ready? st.session_state.public_observations[hash] = observation st.write(f"*[D] Observation {hash} classified as {st.session_state.whale_prediction1[hash]}*") # func to show results and allow review def cetacean_show_results_and_review(): 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] observation = observations[hash].to_dict() with grid[col]: st.image(image, use_column_width=True) # dropdown for selecting/overriding the species prediction if not st.session_state.classify_whale_done[hash]: 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[hash] # 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[hash]: observation['class_overriden'] = selected_class # TODO: this should be boolean! st.session_state.public_observations[hash] = 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(hash)) 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 = observations[hash].top_predictions # render images for the top 3 (that is what the model api returns) n = len(whale_classes) st.markdown(f"Top {n} Predictions for observation {str(o)}") for i in range(n): viewer.display_whale(whale_classes, i) o += 1 col = (col + 1) % row_size # func to just present results def cetacean_show_results(): 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] observation = observations[hash].to_dict() with grid[col]: st.image(image, use_column_width=True) # # dropdown for selecting/overriding the species prediction # if not st.session_state.classify_whale_done[hash]: # 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[hash] # # 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[hash]: # observation['class_overriden'] = selected_class # TODO: this should be boolean! # 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(hash)) st.markdown(f"- **hash**: {hash}") 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 = observations[hash].top_predictions # render images for the top 3 (that is what the model api returns) n = len(whale_classes) st.markdown(f"Top {n} Predictions for observation {str(o)}") for i in range(n): viewer.display_whale(whale_classes, i) o += 1 col = (col + 1) % row_size # func to do all in one def cetacean_classify_show_and_review(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 """ raise DeprecationWarning("This function is deprecated. Use individual steps instead") 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[hash] = out['predictions'][0] st.session_state.classify_whale_done[hash] = True msg = f"[D]2 classify_whale_done for {hash}: {st.session_state.classify_whale_done[hash]}, whale_prediction1: {st.session_state.whale_prediction1[hash]}" g_logger.info(msg) # dropdown for selecting/overriding the species prediction if not st.session_state.classify_whale_done[hash]: 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[hash] # 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[hash]: 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