Redid presets to have a better method for organizing them

Add convert script for making official.presets files from excel files maintained by LightSaveUs

aiserver.py

@@ -38,6 +38,7 @@ import bisect
 import functools
 import traceback
 from collections.abc import Iterable
+from collections import OrderedDict
 from typing import Any, Callable, TypeVar, Tuple, Union, Dict, Set, List
 
 import requests
@@ -2167,21 +2168,44 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
     #Let's load the presets
     with open('settings/preset/official.presets') as f:
         presets = json.load(f)
-        to_use = {"Recommended": {"Default": koboldai_vars.default_preset}}
+        koboldai_vars.uid_presets = {x['uid']: x for x in presets}
-        #Check for 6B in title
+        #We want our data to be a 2 deep dict. Top level is "Recommended", "Same Class", "Model 1", "Model 2", etc
-        if '6B' in koboldai_vars.model or '6.7B' in koboldai_vars.model or '1.3B' in koboldai_vars.model:
+        #Next layer is "Official", "Custom"
-            to_use['Recommended'].update(presets['6B'])
+        #Then the preset name
-            for key in presets:
+        
-                if key != '6B':
+        to_use = OrderedDict()
-                    to_use[key] = presets[key]
+        
-        elif '13B' in koboldai_vars.model:
+        to_use["Recommended"] = {"Official": [], "Custom": []}
-            to_use['Recommended'].update(presets['13B'])
+        to_use["Same Class"] = {"Official": [], "Custom": []}
-            for key in presets:
+        used_ids = []
-                if key != '13B':
+        #Build recommended first:
-                    to_use[key] = presets[key]
+        for preset in presets:
-        else:
+            if preset['Model Type'] == koboldai_vars.model and preset['uid'] not in used_ids:
-            for key in presets:
+                if preset['Model Category'] == 'Custom':
-                to_use[key] = presets[key]
+                    to_use['Recommended']['Custom'].append(preset)
+                else:
+                    to_use['Recommended']['Official'].append(preset)
+                used_ids.append(preset['uid'])
+        #Build Same Class
+        for preset in presets:
+            if preset['Model Size'] == koboldai_vars.model and preset['uid'] not in used_ids:
+                if preset['Model Category'] == 'Custom':
+                    to_use['Recommended']['Custom'].append(preset)
+                else:
+                    to_use['Recommended']['Official'].append(preset)
+                used_ids.append(preset['uid'])
+        #Build the rest of the stuff
+        for preset in presets:
+            if preset['uid'] not in used_ids:
+                used_ids.append(preset['uid'])
+                if preset['Model Type'] not in to_use:
+                    to_use[preset['Model Type']] = {"Official": [], "Custom": []}
+                
+                if preset['Model Category'] == 'Custom':
+                    to_use[preset['Model Type']]['Custom'].append(preset)
+                else:
+                    to_use[preset['Model Type']]['Official'].append(preset)
+        
         koboldai_vars.presets = to_use
 
 # Set up Flask routes
@@ -6150,6 +6174,8 @@ def get_files_folders(starting_folder):
 #==================================================================#
 @socketio.on('var_change')
 def UI_2_var_change(data):
+    if 'value' not in data:
+        return
     classname = data['ID'].split("_")[0]
     name = data['ID'][len(classname)+1:]
     classname += "_settings"

convert_xls_to_json_presets.py

@@ -0,0 +1,60 @@
+import pandas as pd
+import sys
+
+output = []
+sheet_mapper = {"KAI-ADAPTED 13B": "13B", "KAI-ADAPTED 6B": "6B", 'KAI-CUSTOM': 'Custom'}
+
+for file in ['KoboldAI Settings (6B).xlsx', 'KoboldAI Settings (13B).xlsx', 'KoboldAI Settings (Custom).xlsx', 'KoboldAI Settings (Original).xlsx']:
+    presets = pd.read_excel("preset Files/{}".format(file), None)
+    for sheet in presets:
+        df = presets[sheet]
+        if sheet in sheet_mapper:
+            sheet = sheet_mapper[sheet]
+        df = df.dropna(axis=1, how='all')
+        df = df.rename(columns={"Unnamed: 0": "setting"})
+        df = pd.melt(df, id_vars=['setting'])
+        df = df.rename(columns={"variable": "preset"})
+        df['fix'] = df['value'].str.replace(" (KAI)", "", regex=False)
+        df.loc[~df['fix'].isnull(), 'value'] = df['fix']
+        df = df.drop(columns=['fix'])
+        df.loc[df['setting']=='Samplers Order', 'value'] = df['value'].str.replace("Temp", "5", regex=False)
+        df.loc[df['setting']=='Samplers Order', 'value'] = df['value'].str.replace("K", "0", regex=False)
+        df.loc[df['setting']=='Samplers Order', 'value'] = df['value'].str.replace("TFS", "3", regex=False)
+        df.loc[df['setting']=='Samplers Order', 'value'] = df['value'].str.replace("A", "1", regex=False)
+        df.loc[df['setting']=='Samplers Order', 'value'] = df['value'].str.replace("Typ", "4", regex=False)
+        df.loc[df['setting']=='Samplers Order', 'value'] = df['value'].str.replace("P", "2", regex=False)
+
+
+        settings_mapper = {'Temperature': 'temp', 'Output Length': 'genamt', 'Repetition Penalty': 'rep_pen',
+                           'Top P': 'top_p', 'Top K': 'top_k', 'Tail-Free': 'tfs', 'Repetition Penalty Range': 'rep_pen_range',
+                           'Repetition Penalty Slope': 'rep_pen_slope', 'Typical': 'typical', 'Top A': 'top_a',
+                           'Samplers Order': 'sampler_order', 'Description of settings from the author': 'description', 
+                           'Author': 'Author', 'Model Type': 'Model Type', 
+                           'Description of settings from NovelAI': 'description', 'Model Size': "Model Size"
+                          }
+        df['setting'] = df['setting'].map(settings_mapper)
+        
+        try:
+            df = df.pivot(index='preset', columns='setting', values='value')
+        except:
+            print(file)
+            display(df)
+            raise
+        
+        df['Model Type'] = df['Model Type'].str.replace(", ", ",").str.split(",")
+        
+        df.loc[:, 'Model Category'] = sheet
+        
+        output.append(df)
+
+        #output[sheet] = df.to_json(orient="index")
+
+df = pd.concat(output)
+df = df.reset_index(drop=False)
+df['uid'] = df.index
+df = df.explode("Model Type")
+df['description'] = df['description'].str.strip()
+
+        
+with open("official.presets", "w") as f:
+    f.write(df.reset_index(drop=True).to_json(orient='records'))

koboldai_settings.py

@@ -109,6 +109,7 @@ class koboldai_vars(object):
         if name[0] == "_":
             super().__setattr__(name, value)
         if name[0] != "_":
+            #Send it to the corrent _setting class
             if name in self._model_settings.__dict__:
                 setattr(self._model_settings, name, value)
             elif name in self._user_settings.__dict__:
@@ -241,6 +242,7 @@ class model_settings(settings):
         self.revision    = None
         self.presets     = []   # Holder for presets
         self.selected_preset = ""
+        self.uid_presets = []
         self.default_preset = {}
         
     #dummy class to eat the tqdm output
@@ -254,8 +256,22 @@ class model_settings(settings):
         super().__setattr__(name, value)
         #Put variable change actions here
         
+        #set preset values
+        if name == 'selected_preset' and value != "":
+            if int(value) in self.uid_presets:
+                for preset_key, preset_value in self.uid_presets[int(value)].items():
+                    if preset_key in self.__dict__:
+                        if type(getattr(self, preset_key)) == int:
+                            preset_value = int(preset_value)
+                        elif type(getattr(self, preset_key)) == float:
+                            preset_value = float(preset_value)
+                        elif type(getattr(self, preset_key)) == bool:
+                            preset_value = bool(preset_value)
+                        elif type(getattr(self, preset_key)) == str:
+                            preset_value = str(preset_value)
+                        setattr(self, preset_key, preset_value)
         #Setup TQDP for token generation
-        if name == "generated_tkns" and 'tqdm' in self.__dict__:
+        elif name == "generated_tkns" and 'tqdm' in self.__dict__:
             if value == 0:
                 self.tqdm.reset(total=self.genamt)
                 self.tqdm_progress = 0

static/koboldai.css

@@ -325,6 +325,10 @@
 	margin-right: 25px;
 }
 
+.preset_item {
+	color: white; /*#2b2a33;*/
+}
+
 .menubar1, .menubar2, .menubar3 {
   width: 21px;
   height: 3px;

static/koboldai.js

@@ -291,45 +291,29 @@ function do_presets(data) {
 	option.text="presets";
 	select.append(option);
 	presets = data.value;
+	
+	
 	for (const [key, value] of Object.entries(data.value)) {
 		var option_group = document.createElement("optgroup");
 		option_group.label = key;
-		for (const [preset, preset_value] of Object.entries(value)) {
+		for (const [group, group_value] of Object.entries(value)) {
 			var option = document.createElement("option");
-			option.value=key+"|"+preset;
+			option.text=group;
-			option.text=preset;
+			option.disabled = true;
-			option.title = preset_value.description;
+			option.classList.add("preset_group");
 			option_group.append(option);
+			for (const [preset, preset_value] of Object.entries(group_value)) {
+				var option = document.createElement("option");
+				option.value=preset;
+				option.text=preset_value.preset;
+				option.title = preset_value.description;
+				option_group.append(option);
+			}
 		}
 		select.append(option_group);
 	}
 }
 
-function selected_preset(data) {
-	
-	preset_key = data.value.split("|")[0];
-	preset = data.value.split("|")[1];
-	if ((data.value == undefined) || (presets[preset_key] == undefined)) {
-		return;
-	}
-	if (presets[preset_key][preset] == undefined) {
-		return;
-	}
-	for (const [key, value] of Object.entries(presets[preset_key][preset])) {
-		if (key.charAt(0) != '_') {
-			var elements_to_change = document.getElementsByClassName("var_sync_model_"+key);
-			for (item of elements_to_change) {
-				if (item.tagName.toLowerCase() === 'input') {
-					item.value = value;
-				} else {
-					item.textContent = fix_text(value);
-				}
-			}
-		}
-	}
-	socket.emit("var_change", {"ID": "model_selected_preset", "value": data.value});
-}
-
 function update_status_bar(data) {
 	var percent_complete = data.value;
 	var percent_bar = document.getElementsByClassName("statusbar_inner");
@@ -378,8 +362,6 @@ function var_changed(data) {
 	//Special Case for Presets
 	} else if ((data.classname == 'model') && (data.name == 'presets')) {
 		do_presets(data);
-	} else if ((data.classname == "model") && (data.name == "selected_preset")) {
-		selected_preset(data);
 	} else if ((data.classname == 'story') && (data.name == 'prompt')) {
 		do_prompt(data);
 	//Basic Data Syncing
@@ -1259,7 +1241,7 @@ function sync_to_server(item) {
 	//get value
 	value = null;
 	name = null;
-	if ((item.tagName.toLowerCase() === 'input') || (item.tagName.toLowerCase() === 'select') || (item.tagName.toLowerCase() == 'textarea')) {
+	if ((item.tagName.toLowerCase() === 'checkbox') || (item.tagName.toLowerCase() === 'input') || (item.tagName.toLowerCase() === 'select') || (item.tagName.toLowerCase() == 'textarea')) {
 		if (item.getAttribute("type") == "checkbox") {
 			value = item.checked;
 		} else {

templates/settings flyout.html

@@ -37,7 +37,7 @@
 	<div id="Model_Info">
 		<div>
 			Running Model: <span class="var_sync_model_model">ReadOnly</span>
-			<select id=presets class="var_sync_model_selected_preset settings_select" onchange='selected_preset(this)'><option>Preset</option></select>
+			<select id=presets class="var_sync_model_selected_preset settings_select" onchange='sync_to_server(this)'><option>Preset</option></select>
 		</div>
 		<div><button class="btn action_button" onclick="socket.emit('load_model_button', {});">Load Model</button></div>
 	</div>