update

app.py

@@ -10,27 +10,31 @@ import mne
 from mne.channels import read_custom_montage
 
 quickstart = """
-# Quickstart
 
-### Raw data
+## Raw data
 1. The data need to be a two-dimensional array (channel, timepoint).
 2. Upload your EEG data in `.csv` format.
 
-### Channel locations
+## Channel locations
 Upload your data's channel locations in `.loc` format, which can be obtained using **EEGLAB**.  
 >If you cannot obtain it, we recommend you to download the standard montage <a href="">here</a>. If the channels in those files doesn't match yours, you can use **EEGLAB** to modify them to your needed montage.
 
-### Mapping
+## Mapping
 
-- Step1: Mapping result
+### Step1: Mapping result
+After clicking the mapping button, the **template montage** and the **input montage** will be displayed, with the unmatched input channels highlighted in red.
+- If your data includes all the 30 template channels, then you can proceed directly to **run the models**.
+- If some of your channels didn't match the template, you will be guided to **Step2**.
+- If all of your channels can find corresponding template channels, but the number of channels is less then 30, you will be guided to **Step3**.
 
-- Step2:
+### Step2:
+In this step, the template channels that didn't match yours will be displyed sequentially. You will then have the option to choose whether to fill some of your unmatched channels into them.
+- If you
+- 
+### Step3:
 
-- Step3:
 
-### Model
-
-### Run model
+# Run model
 
 ### Imputation
 The models was trained using the EEG signals of 30 channels, including: `Fp1, Fp2, F7, F3, Fz, F4, F8, FT7, FC3, FCz, FC4, FT8, T7, C3, Cz, C4, T8, TP7, CP3, CPz, CP4, TP8, P7, P3, Pz, P4, P8, O1, Oz, O2`.
@@ -325,7 +329,7 @@ with gr.Blocks() as demo:
 				tpl_montage : gr.Image(visible=False),
 				in_montage : gr.Image(value=None, visible=False),
 				radio : gr.Radio(choices=[], value=[], label="", visible=False),
-				in_fill_mode : gr.Dropdown(visible=False),
+				in_fill_mode : gr.Dropdown(value="mean", visible=False),
 				chkbox_group : gr.CheckboxGroup(choices=[], value=[], label="", visible=False),
 				fillmode_btn : gr.Button("OK", visible=False),
 				step2_btn : gr.Button("Next", visible=False),
@@ -342,7 +346,6 @@ with gr.Blocks() as demo:
 		filename1 = filepath+"raw_montage_"+str(random.randint(1,10000))+".png"
 		filename2 = filepath+"mapped_montage_"+str(random.randint(1,10000))+".png"
 		
-		#app_state["filenames"]["raw_montage"] = filename1
 		app_state["filenames"].update({
 		    "raw_montage" : filename1,
 		    "mapped_montage" : filename2
@@ -372,7 +375,7 @@ with gr.Blocks() as demo:
 			gr.Info('The mapping process is finished!')
 			
 			return {app_state_json : app_state,
-					desc_md : gr.Markdown("### Step1: Mapping result", visible=True),
+					desc_md : gr.Markdown("### Mapping result", visible=True),
 					tpl_montage : gr.Image(visible=True),
 					in_montage : gr.Image(value=filename2, visible=True),
 					run_btn : gr.Button(interactive=True)}
@@ -643,7 +646,7 @@ with gr.Blocks() as demo:
 	fillmode_btn.click(
 	    fn = fill_value,
 	    inputs = [app_state_json, channel_info_json, in_fill_mode],
-		outputs = [app_state_json, in_fill_mode, fillmode_btn, chkbox_group, step3_btn, next_btn, run_btn]
+		outputs = [app_state_json, desc_md, in_fill_mode, fillmode_btn, chkbox_group, step3_btn, next_btn, run_btn]
 	).success(
 	    fn = None,
 	    js = init_js,