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import streamlit as st
import os
import pandas as pd
st.set_page_config(
page_title="ML Models",
page_icon="🔥",
)
from utils.st_logs import parse_log_buffer, init_logging_session_states
from transformers import pipeline
from transformers import AutoModelForImageClassification
from classifier.classifier_image import add_classifier_header
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 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"
############################################################
# TO- DO: MAKE ENV FILE
#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"
############################################################
# Streamlit app
tab_inference, tab_hotdogs= \
st.tabs(["Cetecean classifier", "Hotdog classifier"])
# 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()
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
# TODO NEED TO ADAPT to multipage
#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
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("I have looked over predictions and confirm correct species", icon= "👀",
type="primary",
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!", icon= "⬆️",
type="primary",):
# 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([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()
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