import logging import os import pandas as pd import streamlit as st import folium from streamlit_folium import st_folium from transformers import pipeline from transformers import AutoModelForImageClassification from maps.obs_map import add_obs_map_header from classifier.classifier_image import add_classifier_header from datasets import disable_caching disable_caching() import whale_gallery as gallery import whale_viewer as viewer from input.input_handling import setup_input, check_inputs_are_set from input.input_handling import init_input_container_states, add_input_UI_elements, init_input_data_session_states from input.input_handling import dbg_show_observation_hashes from maps.alps_map import present_alps_map from maps.obs_map import present_obs_map from utils.st_logs import parse_log_buffer, init_logging_session_states from utils.workflow_ui import refresh_progress_display, init_workflow_viz, init_workflow_session_states from hf_push_observations import push_all_observations from classifier.classifier_image import cetacean_just_classify, cetacean_show_results_and_review, cetacean_show_results, init_classifier_session_states from classifier.classifier_hotdog import hotdog_classify # setup for the ML model on huggingface (our wrapper) os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python" #classifier_revision = '0f9c15e2db4d64e7f622ade518854b488d8d35e6' classifier_revision = 'main' # default/latest version # and the dataset of observations (hf dataset in our space) dataset_id = "Saving-Willy/temp_dataset" data_files = "data/train-00000-of-00001.parquet" USE_BASIC_MAP = False DEV_SIDEBAR_LIB = True # one toggle for all the extra debug text if "MODE_DEV_STATEFUL" not in st.session_state: st.session_state.MODE_DEV_STATEFUL = False # get a global var for logger accessor in this module LOG_LEVEL = logging.DEBUG g_logger = logging.getLogger(__name__) g_logger.setLevel(LOG_LEVEL) st.set_page_config(layout="wide") # initialise various session state variables init_logging_session_states() # logging init should be early init_workflow_session_states() init_input_data_session_states() init_input_container_states() init_workflow_viz() init_classifier_session_states() def main() -> None: """ Main entry point to set up the streamlit UI and run the application. The organisation is as follows: 1. observation input (a new observations) is handled in the sidebar 2. the rest of the interface is organised in tabs: - cetean classifier - hotdog classifier - map to present the obersvations - table of recent log entries - gallery of whale images The majority of the tabs are instantiated from modules. Currently the two classifiers are still in-line here. """ g_logger.info("App started.") g_logger.warning(f"[D] Streamlit version: {st.__version__}. Python version: {os.sys.version}") #g_logger.debug("debug message") #g_logger.info("info message") #g_logger.warning("warning message") # Streamlit app tab_inference, tab_hotdogs, tab_map, tab_coords, tab_log, tab_gallery = \ st.tabs(["Cetecean classifier", "Hotdog classifier", "Map", "*:gray[Dev:coordinates]*", "Log", "Beautiful cetaceans"]) # put this early so the progress indicator is at the top (also refreshed at end) refresh_progress_display() # create a sidebar, and parse all the input (returned as `observations` object) with st.sidebar: # layout handling add_input_UI_elements() # input elements (file upload, text input, etc) setup_input() with tab_map: # visual structure: a couple of toggles at the top, then the map inlcuding a # dropdown for tileset selection. add_obs_map_header() tab_map_ui_cols = st.columns(2) with tab_map_ui_cols[0]: show_db_points = st.toggle("Show Points from DB", True) with tab_map_ui_cols[1]: dbg_show_extra = st.toggle("Show Extra points (test)", False) if show_db_points: # show a nicer map, observations marked, tileset selectable. st_observation = present_obs_map( dataset_id=dataset_id, data_files=data_files, dbg_show_extra=dbg_show_extra) else: # development map. st_observation = present_alps_map() with tab_log: handler = st.session_state['handler'] if handler is not None: records = parse_log_buffer(handler.buffer) st.dataframe(records[::-1], use_container_width=True,) st.info(f"Length of records: {len(records)}") else: st.error("⚠️ No log handler found!") with tab_coords: # the goal of this tab is to allow selection of the new obsvation's location by map click/adjust. st.markdown("Coming later! :construction:") st.markdown( """*The goal is to allow interactive definition for the coordinates of a new observation, by click/drag points on the map.*""") st.write("Click on the map to capture a location.") #m = folium.Map(location=visp_loc, zoom_start=7) mm = folium.Map(location=[39.949610, -75.150282], zoom_start=16) folium.Marker( [39.949610, -75.150282], popup="Liberty Bell", tooltip="Liberty Bell" ).add_to(mm) st_data2 = st_folium(mm, width=725) st.write("below the map...") if st_data2['last_clicked'] is not None: print(st_data2) st.info(st_data2['last_clicked']) with tab_gallery: # here we make a container to allow filtering css properties # specific to the gallery (otherwise we get side effects) tg_cont = st.container(key="swgallery") with tg_cont: gallery.render_whale_gallery(n_cols=4) # state handling re data_entry phases # 0. no data entered yet -> display the file uploader thing # 1. we have some images, but not all the metadata fields are done -> validate button shown, disabled # 2. all data entered -> validate button enabled # 3. validation button pressed, validation done -> enable the inference button. # - at this point do we also want to disable changes to the metadata selectors? # anyway, simple first. if st.session_state.workflow_fsm.is_in_state('doing_data_entry'): # can we advance state? - only when all inputs are set for all uploaded files all_inputs_set = check_inputs_are_set(debug=True, empty_ok=False) if all_inputs_set: st.session_state.workflow_fsm.complete_current_state() # -> data_entry_complete else: # button, disabled; no state change yet. st.sidebar.button(":gray[*Validate*]", disabled=True, help="Please fill in all fields.") if st.session_state.workflow_fsm.is_in_state('data_entry_complete'): # can we advance state? - only when the validate button is pressed if st.sidebar.button(":white_check_mark:[**Validate**]"): # create a dictionary with the submitted observation tab_log.info(f"{st.session_state.observations}") df = pd.DataFrame([obs.to_dict() for obs in st.session_state.observations.values()]) #df = pd.DataFrame(st.session_state.observations, index=[0]) with tab_coords: st.table(df) # there doesn't seem to be any actual validation here?? TODO: find validator function (each element is validated by the input box, but is there something at the whole image level?) # hmm, maybe it should actually just be "I'm done with data entry" st.session_state.workflow_fsm.complete_current_state() # -> data_entry_validated # state handling re inference phases (tab_inference) # 3. validation button pressed, validation done -> enable the inference button. # 4. inference button pressed -> ML started. | let's cut this one out, since it would only # make sense if we did it as an async action # 5. ML done -> show results, and manual validation options # 6. manual validation done -> enable the upload buttons # with tab_inference: # inside the inference tab, on button press we call the model (on huggingface hub) # which will be run locally. # - the model predicts the top 3 most likely species from the input image # - these species are shown # - the user can override the species prediction using the dropdown # - an observation is uploaded if the user chooses. if st.session_state.MODE_DEV_STATEFUL: dbg_show_observation_hashes() add_classifier_header() # if we are before data_entry_validated, show the button, disabled. if not st.session_state.workflow_fsm.is_in_state_or_beyond('data_entry_validated'): tab_inference.button(":gray[*Identify with cetacean classifier*]", disabled=True, help="Please validate inputs before proceeding", key="button_infer_ceteans") if st.session_state.workflow_fsm.is_in_state('data_entry_validated'): # show the button, enabled. If pressed, we start the ML model (And advance state) if tab_inference.button("Identify with cetacean classifier", key="button_infer_ceteans"): cetacean_classifier = AutoModelForImageClassification.from_pretrained( "Saving-Willy/cetacean-classifier", revision=classifier_revision, trust_remote_code=True) cetacean_just_classify(cetacean_classifier) st.session_state.workflow_fsm.complete_current_state() # trigger a refresh too (refreshhing the prog indicator means the script reruns and # we can enter the next state - visualising the results / review) # ok it doesn't if done programmatically. maybe interacting with teh button? check docs. refresh_progress_display() #TODO: validate this doesn't harm performance adversely. st.rerun() elif st.session_state.workflow_fsm.is_in_state('ml_classification_completed'): # show the results, and allow manual validation st.markdown("""### Inference results and manual validation/adjustment """) if st.session_state.MODE_DEV_STATEFUL: s = "" for k, v in st.session_state.whale_prediction1.items(): s += f"* Image {k}: {v}\n" st.markdown(s) # add a button to advance the state if st.button("Confirm species predictions", help="Confirm that all species are selected correctly"): st.session_state.workflow_fsm.complete_current_state() # -> manual_inspection_completed st.rerun() cetacean_show_results_and_review() elif st.session_state.workflow_fsm.is_in_state('manual_inspection_completed'): # show the ML results, and allow the user to upload the observation st.markdown("""### Inference Results (after manual validation) """) if st.button("Upload all observations to THE INTERNET!"): # let this go through to the push_all func, since it just reports to log for now. push_all_observations(enable_push=False) st.session_state.workflow_fsm.complete_current_state() # -> data_uploaded st.rerun() cetacean_show_results() elif st.session_state.workflow_fsm.is_in_state('data_uploaded'): # the data has been sent. Lets show the observations again # but no buttons to upload (or greyed out ok) st.markdown("""### Observation(s) uploaded - thank you!""") cetacean_show_results() st.divider() #df = pd.DataFrame(st.session_state.observations, index=[0]) df = pd.DataFrame([obs.to_dict() for obs in st.session_state.observations.values()]) st.table(df) # didn't decide what the next state is here - I think we are in the terminal state. #st.session_state.workflow_fsm.complete_current_state() # inside the hotdog tab, on button press we call a 2nd model (totally unrelated at present, just for demo # purposes, an hotdog image classifier) which will be run locally. # - this model predicts if the image is a hotdog or not, and returns probabilities # - the input image is the same as for the ceteacean classifier - defined in the sidebar tab_hotdogs.title("Hot Dog? Or Not?") tab_hotdogs.write(""" *Run alternative classifer on input images. Here we are using a binary classifier - hotdog or not - from huggingface.co/julien-c/hotdog-not-hotdog.*""") if tab_hotdogs.button("Get Hotdog Prediction"): pipeline_hot_dog = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog") if st.session_state.image is None: st.info("Please upload an image first.") #st.info(str(observations.to_dict())) else: hotdog_classify(pipeline_hot_dog, tab_hotdogs) # after all other processing, we can show the stage/state refresh_progress_display() if __name__ == "__main__": main()