Update Workflow.py

src/Workflow.py

@@ -1,13 +1,6 @@
 import streamlit as st
 from src.workflow.WorkflowManager import WorkflowManager
 
-# for result section:
-from pathlib import Path
-import pandas as pd
-import plotly.express as px
-from src.common.common import show_fig
-
-
 class Workflow(WorkflowManager):
     # Setup pages for upload, parameter, execution and results.
     # For layout use any streamlit components such as tabs (as shown in example), columns, or even expanders.
@@ -16,108 +9,13 @@ def __init__(self) -> None:
         super().__init__("TOPP Workflow", st.session_state["workspace"])
 
     def upload(self) -> None:
-        t = st.tabs(["MS data"])
-        with t[0]:
-            # Use the upload method from StreamlitUI to handle mzML file uploads.
-            self.ui.upload_widget(
-                key="mzML-files",
-                name="MS data",
-                file_types="mzML",
-                fallback=[str(f) for f in Path("example-data", "mzML").glob("*.mzML")],
-            )
+        pass
 
-    @st.fragment
     def configure(self) -> None:
-        # Allow users to select mzML files for the analysis.
-        self.ui.select_input_file("mzML-files", multiple=True)
-
-        # Create tabs for different analysis steps.
-        t = st.tabs(
-            ["**Feature Detection**", "**Feature Linking**", "**Python Custom Tool**"]
-        )
-        with t[0]:
-            # Parameters for FeatureFinderMetabo TOPP tool.
-            self.ui.input_TOPP(
-                "FeatureFinderMetabo",
-                custom_defaults={"algorithm:common:noise_threshold_int": 1000.0},
-            )
-        with t[1]:
-            # Paramters for MetaboliteAdductDecharger TOPP tool.
-            self.ui.input_TOPP("FeatureLinkerUnlabeledKD")
-        with t[2]:
-            # A single checkbox widget for workflow logic.
-            self.ui.input_widget("run-python-script", False, "Run custom Python script")
-            # Generate input widgets for a custom Python tool, located at src/python-tools.
-            # Parameters are specified within the file in the DEFAULTS dictionary.
-            self.ui.input_python("example")
+        pass
 
     def execution(self) -> None:
-        # Any parameter checks, here simply checking if mzML files are selected
-        if not self.params["mzML-files"]:
-            self.logger.log("ERROR: No mzML files selected.")
-            return
-
-        # Get mzML files with FileManager
-        in_mzML = self.file_manager.get_files(self.params["mzML-files"])
-
-        # Log any messages.
-        self.logger.log(f"Number of input mzML files: {len(in_mzML)}")
+        pass
 
-        # Prepare output files for feature detection.
-        out_ffm = self.file_manager.get_files(
-            in_mzML, "featureXML", "feature-detection"
-        )
-
-        # Run FeatureFinderMetabo tool with input and output files.
-        self.logger.log("Detecting features...")
-        self.executor.run_topp(
-            "FeatureFinderMetabo", input_output={"in": in_mzML, "out": out_ffm}
-        )
-
-        # Prepare input and output files for feature linking
-        in_fl = self.file_manager.get_files(out_ffm, collect=True)
-        out_fl = self.file_manager.get_files(
-            "feature_matrix.consensusXML", set_results_dir="feature-linking"
-        )
-
-        # Run FeatureLinkerUnlabaeledKD with all feature maps passed at once
-        self.logger.log("Linking features...")
-        self.executor.run_topp(
-            "FeatureLinkerUnlabeledKD", input_output={"in": in_fl, "out": out_fl}
-        )
-        self.logger.log("Exporting consensus features to pandas DataFrame...")
-        self.executor.run_python(
-            "export_consensus_feature_df", input_output={"in": out_fl[0]}
-        )
-        # Check if adduct detection should be run.
-        if self.params["run-python-script"]:
-            # Example for a custom Python tool, which is located in src/python-tools.
-            self.executor.run_python("example", {"in": in_mzML})
-
-    @st.fragment
     def results(self) -> None:
-        @st.fragment
-        def show_consensus_features():
-            df = pd.read_csv(file, sep="\t", index_col=0)
-            st.metric("number of consensus features", df.shape[0])
-            c1, c2 = st.columns(2)
-            rows = c1.dataframe(df, selection_mode="multi-row", on_select="rerun")[
-                "selection"
-            ]["rows"]
-            if rows:
-                df = df.iloc[rows, 4:]
-                fig = px.bar(df, barmode="group", labels={"value": "intensity"})
-                with c2:
-                    show_fig(fig, "consensus-feature-intensities")
-            else:
-                st.info(
-                    "💡 Select one ore more rows in the table to show a barplot with intensities."
-                )
-
-        file = Path(
-            self.workflow_dir, "results", "feature-linking", "feature_matrix.tsv"
-        )
-        if file.exists():
-            show_consensus_features()
-        else:
-            st.warning("No consensus feature file found. Please run workflow first.")
+        pass