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Revert "Remove dependency on st_pages"
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,281 @@ | ||
| """Streamlit-based web interface for ProteoBench.""" | ||
|
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||
| import json | ||
| import logging | ||
| from datetime import datetime | ||
|
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| import pages.texts.proteobench_builder as pbb | ||
| import streamlit as st | ||
| import streamlit_utils | ||
| from streamlit_extras.let_it_rain import rain | ||
|
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||
| from proteobench.modules.template.module import Module | ||
| from proteobench.modules.template.parse_settings import ( | ||
| INPUT_FORMATS, | ||
| LOCAL_DEVELOPMENT, | ||
| TEMPLATE_RESULTS_PATH, | ||
| ) | ||
| from proteobench.modules.template.plot import plot_bench1, plot_bench2 | ||
|
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| logger = logging.getLogger(__name__) | ||
|
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| ## Different parts of the web application | ||
| # Data for generating the figures | ||
| # This is usually | ||
| # a) The result of the given datapoint | ||
| RESULT_PERF = "result_perf" | ||
| # b) All datapoints read from a json file or downloaded from the web | ||
| ALL_DATAPOINTS = "all_datapoints" | ||
| SUBMIT = "submit" | ||
| # Add your figures here | ||
| FIG1 = "fig1" | ||
| FIG2 = "fig2" | ||
|
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| if "submission_ready" not in st.session_state: | ||
| st.session_state["submission_ready"] = False | ||
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| class StreamlitUI: | ||
| """Proteobench Streamlit UI.""" | ||
|
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| def __init__(self): | ||
| """Proteobench Streamlit UI.""" | ||
| self.texts = WebpageTexts | ||
| self.user_input = dict() | ||
|
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| pbb.proteobench_page_config() | ||
| pbb.proteobench_sidebar() | ||
|
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| if SUBMIT not in st.session_state: | ||
| st.session_state[SUBMIT] = False | ||
| self._main_page() | ||
|
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| # Here the user can select the input file format. This is defined in the | ||
| # modules folder in the io_parse_settings folder | ||
| def generate_input_field(self, input_format: str, content: dict): | ||
| if content["type"] == "text_input": | ||
| return st.text_input(content["label"], content["value"][input_format]) | ||
| if content["type"] == "number_input": | ||
| return st.number_input( | ||
| content["label"], | ||
| value=content["value"][input_format], | ||
| format=content["format"], | ||
| ) | ||
| if content["type"] == "selectbox": | ||
| return st.selectbox( | ||
| content["label"], | ||
| content["options"], | ||
| content["options"].index(content["value"][input_format]), | ||
| ) | ||
| if content["type"] == "checkbox": | ||
| return st.checkbox(content["label"], content["value"][input_format]) | ||
|
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| def _main_page(self): | ||
| """Format main page.""" | ||
| st.title("Proteobench") | ||
| st.header("Input and configuration") | ||
|
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| with st.form(key="main_form"): | ||
| st.subheader("Input files") | ||
| self.user_input["input_csv"] = st.file_uploader( | ||
| "Search engine result file", help=self.texts.Help.input_file | ||
| ) | ||
|
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| self.user_input["input_format"] = st.selectbox( | ||
| "Search engine", INPUT_FORMATS, help=self.texts.Help.input_format | ||
| ) | ||
|
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||
| # self.user_input["pull_req"] = st.text_input( | ||
| # "Open pull request to make results available to everyone (type \"YES\" to enable)", | ||
| # "NO" | ||
| # ) | ||
|
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| with st.expander("Additional parameters"): | ||
| # Here the user enters metadata about the data analysis | ||
| with open("../webinterface/configuration/template.json") as file: | ||
| config = json.load(file) | ||
|
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| for key, value in config.items(): | ||
| self.user_input[key] = self.generate_input_field(self.user_input["input_format"], value) | ||
| submit_button = st.form_submit_button("Parse and bench", help=self.texts.Help.additional_parameters) | ||
|
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| # if st.session_state[SUBMIT]: | ||
| if FIG1 in st.session_state: | ||
| self._populate_results() | ||
|
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| if submit_button: | ||
| self._run_proteobench() | ||
|
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| def _populate_results(self): | ||
| self.generate_results("", None, None, False) | ||
|
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| def _run_proteobench(self): | ||
| # Run Proteobench | ||
| st.header("Running Proteobench") | ||
| status_placeholder = st.empty() | ||
| status_placeholder.info(":hourglass_flowing_sand: Running Proteobench...") | ||
|
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| if ALL_DATAPOINTS not in st.session_state: | ||
| st.session_state[ALL_DATAPOINTS] = None | ||
|
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| try: | ||
| result_performance, all_datapoints = Module().benchmarking( | ||
| self.user_input["input_csv"], | ||
| self.user_input["input_format"], | ||
| self.user_input, | ||
| st.session_state["all_datapoints"], | ||
| ) | ||
| st.session_state[ALL_DATAPOINTS] = all_datapoints | ||
| except Exception as e: | ||
| status_placeholder.error(":x: Proteobench ran into a problem") | ||
| st.exception(e) | ||
| else: | ||
| self.generate_results(status_placeholder, result_performance, all_datapoints, True) | ||
|
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| def generate_results(self, status_placeholder, result_performance, all_datapoints, recalculate): | ||
| time_stamp = datetime.now().strftime("%Y%m%d_%H%M%S") | ||
|
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||
| if recalculate: | ||
| status_placeholder.success(":heavy_check_mark: Finished!") | ||
|
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| # Show head of result DataFrame | ||
| st.header("Results") | ||
| st.subheader("Sample of the processed file") | ||
| if not recalculate: | ||
| result_performance = st.session_state[RESULT_PERF] | ||
| all_datapoints = st.session_state[ALL_DATAPOINTS] | ||
| st.dataframe(result_performance.head(100)) | ||
|
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| # Plot results | ||
| # set your title | ||
| st.subheader("TITLE FOR FIGURE 1") | ||
| if recalculate: | ||
| # calling the plot functions in modules/template/plot.py | ||
| fig = plot_bench1(result_performance) | ||
| else: | ||
| fig = st.session_state[FIG1] | ||
| st.plotly_chart(fig, use_container_width=True) | ||
|
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| # set your title | ||
| st.subheader("TITLE FOR FIGURE 2") | ||
| # show metadata | ||
| # st.text(all_datapoints.head(100)) | ||
|
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| if recalculate: | ||
| # calling the plot functions in modules/template/plot.py | ||
| fig2 = plot_bench1(all_datapoints) | ||
| else: | ||
| fig2 = st.session_state[FIG2] | ||
| st.plotly_chart(fig2, use_container_width=True) | ||
|
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| # Naming the sample with specifics about input and analysis | ||
| sample_name = "%s-%s-%s-%s" % ( | ||
| self.user_input["input_format"], | ||
| self.user_input["version"], | ||
| self.user_input["mbr"], | ||
| time_stamp, | ||
| ) | ||
|
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| # Download link | ||
| st.subheader("Download calculated ratios") | ||
| st.download_button( | ||
| label="Download", | ||
| data=streamlit_utils.save_dataframe(result_performance), | ||
| file_name=f"{sample_name}.csv", | ||
| mime="text/csv", | ||
| ) | ||
| st.subheader("Add results to online repository") | ||
| st.session_state[FIG1] = fig | ||
| st.session_state[FIG2] = fig2 | ||
| st.session_state[RESULT_PERF] = result_performance | ||
| st.session_state[ALL_DATAPOINTS] = all_datapoints | ||
|
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| checkbox = st.checkbox("I confirm that the metadata is correct") | ||
| if checkbox: | ||
| st.session_state["submission_ready"] = True | ||
| submit_pr = st.button("I really want to upload it") | ||
| # TODO: check if parameters are filled | ||
| # submit_pr = False | ||
| if submit_pr: | ||
| st.session_state[SUBMIT] = True | ||
| if not LOCAL_DEVELOPMENT: | ||
| try: | ||
| secret = st.secrets["gh"]["token"] | ||
| except: | ||
| secret = "" | ||
| Module().clone_pr( | ||
| st.session_state[ALL_DATAPOINTS], | ||
| secret, | ||
| username="Proteobot", | ||
| remote_git="github.com/Proteobot/Results_Module2_quant_DDA.git", | ||
| branch_name="new_branch", | ||
| ) | ||
| else: | ||
| DDA_QUANT_RESULTS_PATH = Module().write_json_local_development(st.session_state[ALL_DATAPOINTS]) | ||
| if SUBMIT in st.session_state: | ||
| if st.session_state[SUBMIT]: | ||
| # status_placeholder.success(":heavy_check_mark: Successfully uploaded data!") | ||
| st.subheader("SUCCESS") | ||
| st.session_state[SUBMIT] = False | ||
| rain(emoji="🎈", font_size=54, falling_speed=5, animation_length=1) | ||
|
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|
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| ## Inline documentation. This is the one for the DDA_Quant module | ||
| class WebpageTexts: | ||
| class Sidebar: | ||
| about = f""" | ||
| """ | ||
|
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||
| class Help: | ||
| input_file = """ | ||
| Output file of the search engine | ||
| """ | ||
|
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| pull_req = """ | ||
| It is open to the public indefinitely. | ||
| """ | ||
|
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| input_format = """ | ||
| Please select the software you used to generate the search engine results. | ||
| You can check the toml files at https://github.com/Proteobench/ProteoBench/tree/main/proteobench/modules/dda_quant/io_parse_settings | ||
| for more details. | ||
| Additionally, you can use the tab-delimited Custom format containing the following columns: | ||
| Sequence: peptide sequence | ||
| Proteins: Protein accessions according to fasta file | ||
| Charge (optional): Charge state of measured peptide | ||
| FQ_Orbitrap_DDA_Condition_A_Sample_Alpha_01: Quantitative column sample 1 | ||
| LFQ_Orbitrap_DDA_Condition_A_Sample_Alpha_02: Quantitative column sample 2 | ||
| LFQ_Orbitrap_DDA_Condition_A_Sample_Alpha_03: Quantitative column sample 3 | ||
| LFQ_Orbitrap_DDA_Condition_B_Sample_Alpha_01: Quantitative column sample 4 | ||
| LFQ_Orbitrap_DDA_Condition_B_Sample_Alpha_02: Quantitative column sample 5 | ||
| LFQ_Orbitrap_DDA_Condition_B_Sample_Alpha_03: Quantitative column sample 6 | ||
| """ | ||
|
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| additional_parameters = """ | ||
| Please provide all details about the used parameter for the database search. They will facilitate the comparison. | ||
| """ | ||
|
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||
| class Errors: | ||
| missing_peptide_csv = """ | ||
| Upload a peptide CSV file or select the _Use example data_ checkbox. | ||
| """ | ||
| missing_calibration_peptide_csv = """ | ||
| Upload a calibration peptide CSV file or select another _Calibration | ||
| peptides_ option. | ||
| """ | ||
| missing_calibration_column = """ | ||
| Upload a peptide CSV file with a `tr` column or select another _Calibration | ||
| peptides_ option. | ||
| """ | ||
| invalid_peptide_csv = """ | ||
| Uploaded peptide CSV file could not be read. Click on _Info about peptide | ||
| CSV formatting_ for more info on the correct input format. | ||
| """ | ||
| invalid_calibration_peptide_csv = """ | ||
| Uploaded calibration peptide CSV file could not be read. Click on _Info | ||
| about peptide CSV formatting_ for more info on the correct input format. | ||
| """ | ||
|
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|
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||
| if __name__ == "__main__": | ||
| StreamlitUI() |
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8 changes: 4 additions & 4 deletions
8
...face/pages/5_quant_LFQ_peptidoform_DDA.py → ...module_pages/quant_lfq_peptidoform_DDA.py
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