Add help

src/viewer/pages/prep_sMRI_dicomtonifti.py

@@ -45,18 +45,24 @@ def panel_wdir() -> None:
             )
             st.session_state.flags["dir_out"] = True
 
-        @st.dialog("Data organization")  # type:ignore
-        def help_input_data():
+        @st.dialog("Working Directory")  # type:ignore
+        def help_working_dir():
             st.markdown(
                 """
-                ### Working Directory:
-                This is a .
+                - A NiChart pipeline executes a series of steps, with input/output files organized in a predefined folder structure, the **"working directory"**.
+
+                - Set an **"output path"** (desktop app only) and a **"dataset name"** to define the **working directory** for your analysis. You only need to set the working directory once.
+
+                - The **dataset name** can be any identifier that describes your analysis or data; it does not need to match the input study or data folder name.
+
+                - On the desktop app, you can initiate a NiChart pipeline by selecting the **working directory** from a previously completed task.
+
+                - On the cloud app, the results are deleted in regular intervals, so they may not be available.
                 """
             )
-            st.write('Example MUSE data file:')
 
-        if st.button('I need help 🤔'):
-            help_input_data()
+        if st.button('Get help 🤔', key='key_btn_working_dir'):
+            help_working_dir()
 
 
 def panel_indicoms() -> None:
@@ -108,6 +114,22 @@ def panel_indicoms() -> None:
                     icon=":material/thumb_up:",
                 )
 
+        @st.dialog("DICOM Data")  # type:ignore
+        def help_in_dicoms():
+            st.markdown(
+                """
+                - Upload or select the input DICOM folder containing all DICOM files. Nested folders are supported.
+
+                - On the desktop app, a symbolic link named **"Dicoms"** will be created in the **working directory**, pointing to your input DICOM folder.
+
+                - On the cloud platform, you can directly drag and drop your DICOM files or folders and they will be uploaded to the **"Dicoms"** folder within the **working directory**.
+
+                - On the cloud, **we strongly recommend** compressing your DICOM data into a single ZIP archive before uploading. The system will automatically extract the contents of the ZIP file into the **"Dicoms"** folder upon upload.
+                """
+            )
+
+        if st.button('Get help 🤔', key='key_btn_help_in_dicoms'):
+            help_in_dicoms()
 
 def panel_detect() -> None:
     """

src/viewer/pages/workflow_sMRI_MLScores.py

@@ -156,8 +156,7 @@ def help_input_data():
             st.write('Example demographic data file:')
             st.dataframe(df_demog)
 
-
-        if st.button('I need help 🤔'):
+        if st.button('Get help 🤔'):
             help_input_data()
 
 # Panel for running MLScore

src/viewer/utils/utils_st.py

@@ -189,11 +189,11 @@ def util_panel_workingdir(app_type: str) -> None:
     # Read output folder
     if app_type == "desktop":
         # Read output folder from the user
-        helpmsg = "Results will be saved to the output folder.\n\nChoose the path by typing it into the text field or using the file browser to browse and select it"
+        helpmsg = "Results will be saved to a dedicated folder at the output path.\n\nChoose the path by typing it into the text field or using the file browser to browse and select it"
         dir_out = user_input_foldername(
             "Select folder",
             "btn_sel_dir_out",
-            "Output folder",
+            "Output path",
             st.session_state.paths["file_search_dir"],
             st.session_state.paths["dir_out"],
             helpmsg,
@@ -204,10 +204,10 @@ def util_panel_workingdir(app_type: str) -> None:
 
     # Read dataset name (used to create a folder where all results will be saved)
     helpmsg = (
-        "Please provide a name for your task.\n\n A dedicated folder with this name will be created to store all input and output data associated with the analysis.\n\n The identifier can be any descriptive label; it doesn't need to match the input study or dataset name.\n\n Switch between tasks to run different pipelines or analyze new datasets."
+        "Please provide a unique name for your data/analysis (example: MyStudy1).\n\n A dedicated folder with this name will be created to store all input and output data associated with the analysis."
     )
     st.session_state.dset = user_input_textfield(
-        "Task Identifier", st.session_state.dset, helpmsg, False
+        "Dataset Name", st.session_state.dset, helpmsg, False
     )
 
     # Create results folder