"""Streamlit-based web interface for ProteoBench."""
import copy
import glob
import json
import logging
import os
import tempfile
import uuid
import zipfile
from datetime import datetime
from pprint import pformat
from typing import Any, Dict, Optional
import pages.texts.proteobench_builder as pbb
import pandas as pd
import plotly.graph_objects as go
import streamlit as st
import streamlit_utils
from pages.pages_variables.DeNovo.DDA_HCD_variables import VariablesDDADeNovo
from streamlit_extras.let_it_rain import rain
from proteobench.exceptions import DatasetAlreadyExistsOnServerError
from proteobench.github.gh import get_submission_source, is_official_server
from proteobench.io.params import ProteoBenchParameters
from proteobench.io.parsing.parse_settings import ParseSettingsBuilder
from proteobench.io.parsing.utils import add_maxquant_fixed_modifications
from proteobench.modules.denovo.denovo_DDA_HCD import DDAHCDDeNovoModule as IonModule
from proteobench.utils.server_io import dataset_folder_exists
from .base import BaseUIModule
from .tabs import tab1_view_public_results as tab1
from .tabs import tab2_upload_results as tab2
from .tabs import tab2_upload_results as tab2_quant
from .tabs import tab4_view_public_and_new_results as tab4
from .tabs import tab5_compare_results
from .tabs import tab6_submit_results as tab5_quant
logger: logging.Logger = logging.getLogger(__name__)
[docs]
class DeNovoUIObjects(BaseUIModule):
"""
Main class for the Streamlit interface of ProteoBench de novo identification.
This class handles the creation of the Streamlit UI elements, including the main page layout,
input forms, results display, and data submission elements.
Parameters
----------
variables : VariablesDDAQuant
The variables for the quantification module.
ionmodule : IonModule
The quantification module.
parsesettingsbuilder : ParseSettingsBuilder
The parse settings builder.
"""
def __init__(
self,
variables: VariablesDDADeNovo,
ionmodule: IonModule,
parsesettingsbuilder: ParseSettingsBuilder,
page_name: str = "/",
) -> None:
"""
Initialize the Streamlit UI objects for the de novo modules.
Parameters
----------
variables : VariablesDDADeNovo
The variables for the de novo module.
ionmodule : IonModule
The de novo module.
parsesettingsbuilder : ParseSettingsBuilder
The parse settings builder.
"""
super().__init__(
variables=variables, ionmodule=ionmodule, parsesettingsbuilder=parsesettingsbuilder, page_name=page_name
)
# Specific to the 'de novo' module.
self.level_mapping = {"Precision": "precision", "Recall": "recall"}
self.level_mapping_submitted = {"Precision": "precision", "Recall": "recall"}
self.evaluation_type_mapping = {"Exact": "exact", "Mass-based": "mass"}
[docs]
@st.fragment
def display_all_data_results_main(self):
"""Display the results for all data in Tab 1."""
st.title("Results (All Data)")
# Initialize selectbox
tab1.initialize_main_selectbox(
selectbox_id_uuid=self.variables.selectbox_id_uuid,
default_value="None",
)
# Radio for level (Precision or Recall)
tab1.initialize_radio(
radio_id_uuid=self.variables.radio_level_id_uuid, default_value=self.variables.default_level
)
# Radio for evaluation type (Exact or Mass-Based)
tab1.initialize_radio(
radio_id_uuid=self.variables.radio_evaluation_id_uuid, default_value=self.variables.default_evaluation
)
# Define callbacks for plot options
def render_selectbox():
tab1.generate_main_selectbox(self.variables, selectbox_id_uuid=self.variables.selectbox_id_uuid)
def render_level_radio():
tab1.generate_main_radio(
radio_id_uuid=self.variables.radio_level_id_uuid,
description="Select the classification metric",
options=["Precision", "Recall"],
help=self.variables.texts.Help.radio_level,
)
def render_evaluation_radio():
tab1.generate_main_radio(
radio_id_uuid=self.variables.radio_evaluation_id_uuid,
description="Select the stringency of evaluation",
options=["Exact", "Mass-based"],
help=self.variables.texts.Help.radio_evaluation,
)
def render_colorblind_selector():
return tab1.display_colorblindmode_selector(self.variables)
# Render plot options expander
results = self.render_plot_options_expander(
filter_callbacks=[render_selectbox],
selector_callbacks=[render_level_radio, render_evaluation_radio, render_colorblind_selector],
filter_cols_spec=1,
selector_cols_spec=[1, 1, 1, 1],
)
# Extract colorblind mode from results
colorblind_mode = results[3] if len(results) > 3 else False
tab1.display_existing_results(
variables=self.variables,
ionmodule=self.ionmodule,
plot_params={
"label": st.session_state.get(st.session_state.get(self.variables.selectbox_id_uuid, ""), "None"),
"level": self.level_mapping[
st.session_state.get(st.session_state.get(self.variables.radio_level_id_uuid, ""), "Precision")
],
"evaluation_type": self.evaluation_type_mapping[
st.session_state.get(st.session_state.get(self.variables.radio_evaluation_id_uuid, ""), "Exact")
],
"colorblind_mode": colorblind_mode,
"alpha_warning": getattr(self.variables, "alpha_warning", False),
"beta_warning": getattr(self.variables, "beta_warning", False),
},
use_slider=False,
)
# Almost entirely unique to denovo module
[docs]
def display_indepth_plots(self) -> None:
"""
Display the dataset selection dropdown and plot the selected dataset (Tab 3).
"""
if self.variables.all_datapoints_submitted not in st.session_state:
tab2.initialize_main_data_points(variables=self.variables, ionmodule=self.ionmodule)
st.session_state[self.variables.all_datapoints_submitted] = self.ionmodule.obtain_all_data_points(
all_datapoints=st.session_state[self.variables.all_datapoints]
)
if self.variables.all_datapoints_submitted not in st.session_state.keys():
st.error("No data available for plotting.", icon="🚨")
return
if st.session_state[self.variables.all_datapoints_submitted].empty:
st.error("No data available for plotting.", icon="🚨")
return
downloads_df = st.session_state[self.variables.all_datapoints_submitted][["id", "intermediate_hash"]]
downloads_df.set_index("intermediate_hash", drop=False, inplace=True)
if self.variables.placeholder_dataset_selection_container not in st.session_state.keys():
st.session_state[self.variables.placeholder_dataset_selection_container] = st.empty()
st.session_state[self.variables.dataset_selector_id_uuid] = uuid.uuid4()
st.subheader("Select dataset to plot")
dataset_options = [("Uploaded dataset", None)] + list(
zip(downloads_df["id"], downloads_df["intermediate_hash"])
)
dataset_selection = st.multiselect(
label="Select datasets",
options=dataset_options,
key=st.session_state[self.variables.dataset_selector_id_uuid],
format_func=lambda x: x[0],
default=[dataset_options[0]],
help=self.variables.texts.Help.dataset_selection_indepth,
)
# Use default values for plot rendering (no user controls on this tab)
levels = ["precision", "recall"]
evaluation_types = ["exact", "mass"]
colorblind_mode = False
modifications = [
"M-Oxidation",
"Q-Deamidation",
"N-Deamidation",
"N-term Acetylation",
"N-term Carbamylation",
"N-term Ammonia-loss",
]
feature_names = ["Missing Fragmentation Sites", "Peptide Length", "% Explained Intensity"]
# Handle dataset selection - separate uploaded data from public data
all_datapoints_df = st.session_state[self.variables.all_datapoints_submitted]
selected_dtps = pd.DataFrame()
for dtp_id, dtp_hash in dataset_selection:
if dtp_hash is None: # "Uploaded dataset" case
# Get the newly uploaded data (marked as "new")
uploaded_data = all_datapoints_df[all_datapoints_df["old_new"] == "new"]
selected_dtps = pd.concat([selected_dtps, uploaded_data], ignore_index=True)
else:
# Get public dataset by hash
public_data = all_datapoints_df[all_datapoints_df["intermediate_hash"] == dtp_hash]
selected_dtps = pd.concat([selected_dtps, public_data], ignore_index=True)
# Generate in-depth plots using plot generator
if not selected_dtps.empty:
plot_generator = self.ionmodule.get_plot_generator()
# Create kwargs with De Novo-specific parameters (now using user selections)
plot_kwargs = {
"mod_labels": modifications,
"feature": feature_names,
"level": levels,
"evaluation_type": evaluation_types,
"colorblind_mode": colorblind_mode,
}
try:
# Generate all plots
plots = plot_generator.generate_in_depth_plots(selected_dtps, **plot_kwargs)
# Display plots using layout from plot generator
layout = plot_generator.get_in_depth_plot_layout()
descriptions = plot_generator.get_in_depth_plot_descriptions()
for section in layout:
st.subheader(section.get("title", ""))
for idx, plot_name in enumerate(section["plots"]):
if plot_name in plots:
if plot_name in descriptions:
st.caption(descriptions[plot_name])
self._display_indepth_plot(
plot_name=plot_name,
figs=plots[plot_name]
)
# st.plotly_chart(plots[plot_name], width='stretch')
except Exception as e:
st.error(f"Error generating in-depth plots: {e}", icon="🚨")
import traceback
with st.expander("Error details"):
st.code(traceback.format_exc())
else:
st.info("No datasets selected for plotting.")
def _display_ptm_overview(self, figs) -> None:
# Overview PTM plot
with st.expander("Description"):
st.markdown(self.variables.texts.Description.ptm_overview)
st.plotly_chart(
figs,
width='stretch'
)
def _display_ptm_specific(self, figs) -> None:
# Specific PTM plots
with st.expander("Description"):
st.markdown(self.variables.texts.Description.ptm_specific)
modification_labels = list(figs.keys())
tabs = st.tabs(
modification_labels
)
tab_dict = {
mod_label: tab for mod_label, tab in zip(modification_labels, tabs)
}
for mod_label, tab in tab_dict.items():
with tab:
st.header(mod_label)
st.plotly_chart(
figs[mod_label],
width='stretch',
)
def _display_spectrum_features(self, figs) -> None:
feature_names = list(figs.keys())
exact_mode = st.toggle(
label='Exact evaluation mode',
value=False,
key=self.variables.evaluation_mode_toggle_tab3_features
)
if exact_mode:
evaluation_type = 'exact'
else:
evaluation_type = 'mass'
with st.expander("Description"):
st.markdown(self.variables.texts.Description.spectrum_features_overview)
tabs = st.tabs(feature_names)
tab_dict = {feature_name: tab for feature_name, tab in zip(feature_names, tabs)}
for feature_name, tab in tab_dict.items():
with tab:
st.header(feature_name)
st.plotly_chart(
figs[feature_name][evaluation_type],
width='stretch'
)
def _display_species_overview(self, figs) -> None:
with st.expander("Description"):
st.markdown(self.variables.texts.Description.species)
exact_mode = st.toggle(
label="Exact evaluation mode",
value=False,
key=self.variables.evaluation_mode_toggle_tab3_species
)
if exact_mode:
evaluation_type = "exact"
else:
evaluation_type = "mass"
st.plotly_chart(
figs[evaluation_type],
width='stretch',
key=self.variables.fig_species_overview
)
def _display_indepth_plot(self, plot_name: str, figs) -> None:
if plot_name == 'ptm_overview':
self._display_ptm_overview(figs)
elif plot_name == 'ptm_specific':
self._display_ptm_specific(figs)
elif plot_name == 'spectrum_feature':
self._display_spectrum_features(figs)
elif plot_name == 'species_overview':
self._display_species_overview(figs)
else:
raise Exception("Cannot display non-implemented in-depth plot.")
[docs]
@st.fragment
def display_all_data_results_submitted(self) -> None:
"""Display the results for all data in Tab 4."""
st.title("Results (All Data)")
# Initialize selectbox
tab1.initialize_main_selectbox(
selectbox_id_uuid=self.variables.selectbox_id_submitted_uuid,
default_value="None",
)
# Radio one for precision or recall
tab1.initialize_radio(
radio_id_uuid=self.variables.radio_level_id_submitted_uuid, default_value=self.variables.default_level
)
# Radio two for evaluation stringency
tab1.initialize_radio(
radio_id_uuid=self.variables.radio_evaluation_id_submitted_uuid,
default_value=self.variables.default_evaluation,
)
# Define callbacks for plot options
def render_selectbox():
tab1.generate_main_selectbox(
variables=self.variables, selectbox_id_uuid=self.variables.selectbox_id_submitted_uuid
)
def render_level_radio():
tab1.generate_main_radio(
radio_id_uuid=self.variables.radio_level_id_submitted_uuid,
description="Select the classification metric",
options=["Precision", "Recall"],
help=self.variables.texts.Help.radio_level,
)
def render_evaluation_radio():
tab1.generate_main_radio(
radio_id_uuid=self.variables.radio_evaluation_id_submitted_uuid,
description="Select the stringency of evaluation",
options=["Exact", "Mass-based"],
help=self.variables.texts.Help.radio_evaluation,
)
def render_colorblind_selector():
return tab1.display_colorblindmode_selector(self.variables, use_submitted=True)
# Render plot options expander
results = self.render_plot_options_expander(
filter_callbacks=[render_selectbox],
selector_callbacks=[render_level_radio, render_evaluation_radio, render_colorblind_selector],
filter_cols_spec=1,
selector_cols_spec=[1, 1, 1, 1],
)
# Extract colorblind mode from results
colorblind_mode = results[3] if len(results) > 3 else False
# Get current selections from session state
label = st.session_state.get(st.session_state.get(self.variables.selectbox_id_submitted_uuid, ""), "None")
level = self.level_mapping[
st.session_state.get(st.session_state.get(self.variables.radio_level_id_submitted_uuid, ""), "Precision")
]
evaluation_type = self.evaluation_type_mapping[
st.session_state.get(st.session_state.get(self.variables.radio_evaluation_id_submitted_uuid, ""), "Exact")
]
# Plot the datapoints
tab4.display_submitted_results(
self.variables,
self.ionmodule,
plot_params={
"label": label,
"level": level,
"evaluation_type": evaluation_type,
"colorblind_mode": colorblind_mode,
},
)
st.session_state[self.variables.table_id_uuid] = uuid.uuid4()
st.data_editor(
st.session_state[self.variables.all_datapoints_submitted],
key=st.session_state[self.variables.table_id_uuid],
on_change=self._handle_submitted_table_edits,
)
st.title("Public submission")
st.markdown(
"If you want to make this point — and the associated data — publicly available, please go to “Public Submission"
)
[docs]
def display_workflow_comparison(self) -> None:
"""Display the workflow comparison tab."""
tab5_compare_results.display_workflow_comparison(
variables=self.variables,
ionmodule=self.ionmodule,
)
[docs]
def display_public_submission_ui(self) -> None:
"""Display the public submission section of the page in Tab 5."""
submission_source = get_submission_source()
if not is_official_server():
st.warning(
"You are running ProteoBench locally. Submissions from local installs "
"will be labeled as 'local' and will NOT be merged into the public dataset. "
"To submit data for public inclusion, please use the official web server at "
"https://proteobench.cubimed.rub.de/"
)
try:
resolved_hash = st.session_state[self.variables.all_datapoints][
st.session_state[self.variables.all_datapoints][st.session_state["old_new"] == "new"]
]["intermediate_hash"].values[0]
if resolved_hash and dataset_folder_exists(resolved_hash):
st.error(
f":no_entry: This dataset was already submitted. A folder for hash '{resolved_hash}' exists on the server. Submission disabled.",
icon="🚫",
)
return
except Exception:
# Fail-soft; backend will still enforce protection
pass
# Initialize Unchecked submission box variable
if self.variables.check_submission not in st.session_state:
st.session_state[self.variables.check_submission] = False
if self.variables.first_new_plot:
self.submission_ready = tab5_quant.generate_submission_ui_elements(
variables=self.variables,
user_input=self.user_input,
)
if self.user_input[self.variables.meta_data]:
params = tab5_quant.load_user_parameters(
variables=self.variables,
ionmodule=self.ionmodule,
user_input=self.user_input,
)
st.session_state[self.variables.params_file_dict] = params.__dict__
self.params_file_dict_copy = copy.deepcopy(params.__dict__)
tab5_quant.generate_additional_parameters_fields_submission(
variables=self.variables,
user_input=self.user_input,
)
tab5_quant.generate_comments_section(
variables=self.variables,
user_input=self.user_input,
)
# ? stop_duplicating is not used?
self.stop_duplicating = tab5_quant.generate_confirmation_checkbox(
check_submission=self.variables.check_submission
)
else:
params = None
pr_url = None
if st.session_state[self.variables.check_submission] and params is not None:
get_form_values = tab5_quant.get_form_values(
variables=self.variables,
)
params = ProteoBenchParameters(**get_form_values, filename=self.variables.additional_params_json)
try:
pr_url = tab5_quant.submit_to_repository(
variables=self.variables,
ionmodule=self.ionmodule,
user_input=self.user_input,
params_from_file=self.params_file_dict_copy,
params=params,
submission_source=submission_source,
)
except DatasetAlreadyExistsOnServerError as e:
st.error(str(e), icon="🚫")
return
if not self.submission_ready:
return
if (
st.session_state[self.variables.check_submission]
and params is not None
and self.variables.submit in st.session_state
and pr_url is not None
):
tab5_quant.show_submission_success_message(
variables=self.variables,
pr_url=pr_url,
)
#####################
### TAB 4 METHODS ###
#####################
def _handle_submitted_table_edits(self) -> None:
"""Callback function for handling edits made to the data table in the UI."""
edits = st.session_state[st.session_state[self.variables.table_id_uuid]]["edited_rows"].items()
for k, v in edits:
try:
st.session_state[self.variables.all_datapoints_submitted][list(v.keys())[0]].iloc[k] = list(v.values())[
0
]
except TypeError:
return
st.session_state[self.variables.highlight_list_submitted] = list(
st.session_state[self.variables.all_datapoints_submitted]["Highlight"]
)
st.session_state[self.variables.placeholder_table] = st.session_state[self.variables.all_datapoints_submitted]
if len(st.session_state[self.variables.all_datapoints]) == 0:
st.error("No datapoints available for plotting", icon="🚨")
try:
# Get plot generator from module (following Quant pattern)
plot_generator = self.ionmodule.get_plot_generator()
# Get colorblind mode from session state
colorblind_key = self.variables.colorblind_mode_selector_uuid
if colorblind_key in st.session_state:
colorblind_mode_id = st.session_state[colorblind_key]
colorblind_mode = st.session_state.get(colorblind_mode_id, False)
else:
colorblind_mode = False
fig_metric = plot_generator.plot_main_metric(
result_df=st.session_state[self.variables.all_datapoints],
hide_annot=False,
label=st.session_state[st.session_state[self.variables.selectbox_id_uuid]],
level=self.level_mapping[st.session_state[st.session_state[self.variables.radio_level_id_uuid]]],
evaluation_type=self.evaluation_type_mapping[
st.session_state[st.session_state[self.variables.radio_evaluation_id_uuid]]
],
colorblind_mode=colorblind_mode,
)
except Exception as e:
st.error(f"Unable to plot the datapoints: {e}", icon="🚨")
st.session_state[self.variables.fig_metric] = fig_metric
