v0.14.0

发布新节点splitImage & gridoutput，用于分割图片和随机摆放元素
修复若干bug

README.md

@@ -173,7 +173,7 @@ An improvement has been made to directly redirect to GitHub to search for missin
 
 [Download Salesforce/blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base), move to : models/clip_interrogator/Salesforce/blip-image-captioning-base
 
-[Download succinctly/text2image-prompt-generator](https://huggingface.co/succinctly/text2image-prompt-generator/tree/main),move to:text_generator/text2image-prompt-generator
+[Download succinctly/text2image-prompt-generator](https://huggingface.co/succinctly/text2image-prompt-generator/tree/main),move to:prompt_generator/text2image-prompt-generator
 
 [Download Helsinki-NLP/opus-mt-zh-en](https://huggingface.co/Helsinki-NLP/opus-mt-zh-en/tree/main),move to:prompt_generator/opus-mt-zh-en
 

__init__.py

@@ -192,7 +192,7 @@ def read_workflow_json_files(folder_path ):
 def get_workflows():
     # print("#####path::", current_path)
     workflow_path=os.path.join(current_path, "workflow")
-    print('workflow_path: ',workflow_path)
+    # print('workflow_path: ',workflow_path)
     if not os.path.exists(workflow_path):
         # 使用mkdir()方法创建新目录
         os.mkdir(workflow_path)
@@ -231,8 +231,14 @@ def get_my_workflow_for_app(filename="my_workflow_app.json",category="",is_all=F
                     })
                 else:
                     category=''
+                    input=None
+                    output=None
                     if 'category' in x['app']:
                         category=x['app']['category']
+                    if 'input' in x['app']:
+                        input=x['app']['input']
+                    if 'output' in x['app']:
+                        output=x['app']['output']
                     apps.append({
                         "filename":item["filename"],
                         "category":category,
@@ -244,6 +250,8 @@ def get_my_workflow_for_app(filename="my_workflow_app.json",category="",is_all=F
                                 "icon":(x['app']['icon'] if 'icon' in x['app'] else None),
                                 "name":x['app']['name'],
                                 "version":x['app']['version'],
+                                "input":input,
+                                "output":output
                             }
                         },
                         "date":item["date"]
@@ -271,8 +279,14 @@ def get_my_workflow_for_app(filename="my_workflow_app.json",category="",is_all=F
                 # print(apps[0]['filename'] ,item["filename"])
                 if apps[0]['filename']!=item["filename"]:
                     category=''
+                    input=None
+                    output=None
                     if 'category' in x['app']:
                         category=x['app']['category']
+                    if 'input' in x['app']:
+                        input=x['app']['input']
+                    if 'output' in x['app']:
+                        output=x['app']['output']
                     apps.append({
                         "filename":item["filename"],
                         # "category":category,
@@ -284,6 +298,8 @@ def get_my_workflow_for_app(filename="my_workflow_app.json",category="",is_all=F
                                 "icon":(x['app']['icon'] if 'icon' in x['app'] else None),
                                 "name":x['app']['name'],
                                 "version":x['app']['version'],
+                                "input":input,
+                                "output":output
                             }
                         },
                         "date":item["date"]
@@ -534,15 +550,15 @@ PromptServer.add_routes=new_add_routes
 
 
 # 导入节点
-from .nodes.PromptNode import EmbeddingPrompt,RandomPrompt,PromptSlide,PromptSimplification,PromptImage
-from .nodes.ImageNode import GetImageSize_,MirroredImage,ImageColorTransfer,NoiseImage,TransparentImage,GradientImage,LoadImagesFromPath,LoadImagesFromURL,ResizeImage,TextImage,SvgImage,Image3D,ShowLayer,NewLayer,MergeLayers,AreaToMask,SmoothMask,FeatheredMask,SplitLongMask,ImageCropByAlpha,EnhanceImage,FaceToMask
+from .nodes.PromptNode import EmbeddingPrompt,RandomPrompt,PromptSlide,PromptSimplification,PromptImage,JoinWithDelimiter
+from .nodes.ImageNode import SplitImage,GridOutput,GetImageSize_,MirroredImage,ImageColorTransfer,NoiseImage,TransparentImage,GradientImage,LoadImagesFromPath,LoadImagesFromURL,ResizeImage,TextImage,SvgImage,Image3D,ShowLayer,NewLayer,MergeLayers,AreaToMask,SmoothMask,SplitLongMask,ImageCropByAlpha,EnhanceImage,FaceToMask
 from .nodes.Vae import VAELoader,VAEDecode
 from .nodes.ScreenShareNode import ScreenShareNode,FloatingVideo
 from .nodes.Clipseg import CLIPSeg,CombineMasks
 from .nodes.ChatGPT import ChatGPTNode,ShowTextForGPT,CharacterInText
 from .nodes.Audio import GamePal,SpeechRecognition,SpeechSynthesis
 from .nodes.Utils import TESTNODE_,AppInfo,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,MultiplicationNode
-from .nodes.Mask import OutlineMask
+from .nodes.Mask import OutlineMask,FeatheredMask
 
 
 # 要导出的所有节点及其名称的字典
@@ -569,6 +585,8 @@ NODE_CLASS_MAPPINGS = {
     "ImageColorTransfer":ImageColorTransfer,
     "ShowLayer":ShowLayer,
     "NewLayer":NewLayer,
+    "SplitImage":SplitImage,
+    "GridOutput":GridOutput,
     "MergeLayers":MergeLayers,
     "SplitLongMask":SplitLongMask,
     "FeatheredMask":FeatheredMask,
@@ -598,7 +616,8 @@ NODE_CLASS_MAPPINGS = {
     "GetImageSize_":GetImageSize_,
     "SwitchByIndex":SwitchByIndex,
     "LimitNumber":LimitNumber, 
-    "OutlineMask":OutlineMask
+    "OutlineMask":OutlineMask,
+    "JoinWithDelimiter":JoinWithDelimiter
     # "LaMaInpainting":LaMaInpainting
     # "GamePal":GamePal
 }

data/extension-node-map.json

@@ -4761,7 +4761,10 @@
     ],
     "https://github.com/shadowcz007/comfyui-mixlab-nodes": [
         [
+            "GridOutput",
+            "SplitImage",
             "PromptGenerate_Mix",
+            "JoinWithDelimiter",
             "ChinesePrompt_Mix",
             "3DImage",
             "AppInfo",

nodes/ClipInterrogator.py

@@ -24,6 +24,7 @@ def is_installed(package):
         return False
     return spec is not None
 
+
 try:
     if is_installed('clip_interrogator')==False:
         import subprocess
@@ -36,20 +37,25 @@ try:
         #检查命令执行结果
         if result.returncode == 0:
             print("#install success")
-            from transformers import AutoProcessor, BlipForConditionalGeneration
             from clip_interrogator import Config, Interrogator
             _available=True
         else:
             print("#install error")
         
     else:
-        from transformers import AutoProcessor, BlipForConditionalGeneration
         from clip_interrogator import Config, Interrogator
         _available=True
 
 except:
     _available=False
 
+try:
+    from transformers import AutoProcessor, BlipForConditionalGeneration
+except:
+    _available=False
+    print('pls check  transformers.__version__>=4.36.0:: AutoProcessor, BlipForConditionalGeneration')
+
+
 
 def load_caption_model(model_path,config,t='blip-base'):
     dtype=torch.float16 if config.device == 'cuda' else torch.float32

nodes/ImageNode.py

@@ -847,79 +847,6 @@ class SmoothMask:
 
 
 
-class FeatheredMask:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-                "required": {
-                                "mask": ("MASK",),
-                                "start_offset":("INT", {"default": 1, 
-                                                        "min": -150, 
-                                                        "max": 150, 
-                                                        "step": 1,
-                                                        "display": "slider"}),
-                                "feathering_weight":("FLOAT", {"default": 0.1,
-                                                                "min": 0.0,
-                                                                "max": 1,
-                                                                "step": 0.1,
-                                                                "display": "slider"})
-                            }
-            }
-    
-    RETURN_TYPES = ('MASK',)
-
-    FUNCTION = "run"
-
-    CATEGORY = "♾️Mixlab/Mask"
-
-    OUTPUT_IS_LIST = (False,)
-  
-    # 运行的函数
-    def run(self,mask,start_offset, feathering_weight):
-        # print(mask.shape,mask.size())
-        
-        image=tensor2pil(mask)
-
-        # Open the image using PIL
-        image = image.convert("L")
-        if start_offset>0:
-            image=ImageOps.invert(image)
-
-        # Convert the image to a numpy array
-        image_np = np.array(image)
-
-        # Use Canny edge detection to get black contours
-        edges = cv2.Canny(image_np, 30, 150)
-
-        for i in range(0,abs(start_offset)):
-            # int(100*feathering_weight)
-            a=int(abs(start_offset)*0.1*i)
-            # Dilate the black contours to make them wider
-            kernel = np.ones((a, a), np.uint8)
-
-            dilated_edges = cv2.dilate(edges, kernel, iterations=1)
-            # dilated_edges = cv2.erode(edges, kernel, iterations=1)
-            # Smooth the dilated edges using Gaussian blur
-            smoothed_edges = cv2.GaussianBlur(dilated_edges, (5, 5), 0)
-
-            # Adjust the feathering weight
-            feathering_weight = max(0, min(feathering_weight, 1))
-
-            # Blend the smoothed edges with the original image to achieve feathering effect
-            image_np = cv2.addWeighted(image_np, 1, smoothed_edges, feathering_weight, feathering_weight)
-
-        # Convert the result back to PIL image
-        result_image = Image.fromarray(np.uint8(image_np))
-        result_image=result_image.convert("L")
-
-        if start_offset>0:
-            result_image=ImageOps.invert(result_image)
-        
-        mask=pil2tensor(result_image)
-        # print(mask.shape,mask.size())
-        return mask
-
-
 
 
 class SplitLongMask:
@@ -1671,6 +1598,112 @@ class NewLayer:
 
         return (layer_n,)
 
+
+def splitImage(image, num):
+    width, height = image.size
+
+    num_rows = int(num ** 0.5)
+    num_cols = int(num / num_rows)
+    
+    grid_width = width // num_cols
+    grid_height = height // num_rows
+
+    grid_coordinates = []
+    for i in range(num_rows):
+        for j in range(num_cols):
+            x = j * grid_width
+            y = i * grid_height
+            grid_coordinates.append((x, y, grid_width, grid_height))
+
+    return grid_coordinates
+
+# # 读取图片
+# image = Image.open("path_to_your_image.jpg")
+
+# # 定义要切割的区域数量
+# num = 9
+
+# # 切割图片
+# grid_coordinates = splitImage(image, num)
+
+# # 输出切割区域坐标
+# for i, coordinates in enumerate(grid_coordinates):
+#     print(f"Region {i + 1}: x={coordinates[0]}, y={coordinates[1]}, width={coordinates[2]}, height={coordinates[3]}")
+
+
+class SplitImage:
+    @classmethod
+    def INPUT_TYPES(s):
+        return { 
+            "required": { 
+                "image": ("IMAGE",), 
+                "num": ("INT",{
+                    "default": 4, 
+                    "min": 1, #Minimum value
+                    "max": 500, #Maximum value
+                    "step": 1, #Slider's step
+                    "display": "number" # Cosmetic only: display as "number" or "slider"
+                }),
+                "seed": ("INT",{
+                    "default": 4, 
+                    "min": 1, #Minimum value
+                    "max": 500, #Maximum value
+                    "step": 1, #Slider's step
+                    "display": "number" # Cosmetic only: display as "number" or "slider"
+                }),
+            }
+                }
+    
+    RETURN_TYPES = ("_GRID","_GRID",)
+    RETURN_NAMES = ("grids","grid")
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Layer"
+
+    INPUT_IS_LIST = False
+    # OUTPUT_IS_LIST = (True,)
+
+    def run(self,image,num,seed):
+        image=tensor2pil(image)
+
+        grids=splitImage(image,num)
+
+        if seed>=num:
+            num=int(seed / 500 * num)-1
+        else:
+            num=seed-1
+
+        num=max(0,num) 
+
+        g=grids[num]
+
+        return (grids,g,)
+
+
+class GridOutput:
+    @classmethod
+    def INPUT_TYPES(s):
+        return { 
+            "required": {
+                "grid": ("_GRID",)
+            }
+                }
+    
+    RETURN_TYPES = ("INT","INT","INT","INT",)
+    RETURN_NAMES = ("x","y","width","height",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Layer"
+
+    INPUT_IS_LIST = False
+    # OUTPUT_IS_LIST = (True,)
+
+    def run(self,grid):
+        x,y,w,h=grid
+        return (x,y,w,h,)
+
 class ShowLayer:
     @classmethod
     def INPUT_TYPES(s):

nodes/Mask.py

@@ -1,10 +1,26 @@
-import numpy as np
+ 
 import scipy.ndimage
 import torch
-import comfy.utils
 
 from nodes import MAX_RESOLUTION
 
+import numpy as np 
+# from PIL import Image, ImageDraw
+from PIL import Image, ImageOps 
+
+from comfy.cli_args import args
+import cv2
+
+
+
+# Tensor to PIL
+def tensor2pil(image):
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+# Convert PIL to Tensor
+def pil2tensor(image):
+    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
+
 
 def grow(mask, expand, tapered_corners):
     c = 0 if tapered_corners else 1
@@ -68,4 +84,88 @@ class OutlineMask:
 
         m3=combine(m1,m2,0,0)
 
-        return (m3,)
+        return (m3,)
+    
+
+
+
+class FeatheredMask:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+                "required": {
+                                "mask": ("MASK",),
+                                "start_offset":("INT", {"default": 1, 
+                                                        "min": -150, 
+                                                        "max": 150, 
+                                                        "step": 1,
+                                                        "display": "slider"}),
+                                "feathering_weight":("FLOAT", {"default": 0.1,
+                                                                "min": 0.0,
+                                                                "max": 1,
+                                                                "step": 0.1,
+                                                                "display": "slider"})
+                            }
+            }
+    
+    RETURN_TYPES = ('MASK',)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Mask"
+
+    OUTPUT_IS_LIST = (True,)
+  
+    # 运行的函数
+    def run(self,mask,start_offset, feathering_weight):
+        # print(mask.shape,mask.size())
+        
+        num,_,_=mask.size()
+
+        masks=[]
+
+        for i in range(num):
+            mm=mask[i]
+            image=tensor2pil(mm)
+
+            # Open the image using PIL
+            image = image.convert("L")
+            if start_offset>0:
+                image=ImageOps.invert(image)
+
+            # Convert the image to a numpy array
+            image_np = np.array(image)
+
+            # Use Canny edge detection to get black contours
+            edges = cv2.Canny(image_np, 30, 150)
+
+            for i in range(0,abs(start_offset)):
+                # int(100*feathering_weight)
+                a=int(abs(start_offset)*0.1*i)
+                # Dilate the black contours to make them wider
+                kernel = np.ones((a, a), np.uint8)
+
+                dilated_edges = cv2.dilate(edges, kernel, iterations=1)
+                # dilated_edges = cv2.erode(edges, kernel, iterations=1)
+                # Smooth the dilated edges using Gaussian blur
+                smoothed_edges = cv2.GaussianBlur(dilated_edges, (5, 5), 0)
+
+                # Adjust the feathering weight
+                feathering_weight = max(0, min(feathering_weight, 1))
+
+                # Blend the smoothed edges with the original image to achieve feathering effect
+                image_np = cv2.addWeighted(image_np, 1, smoothed_edges, feathering_weight, feathering_weight)
+
+            # Convert the result back to PIL image
+            result_image = Image.fromarray(np.uint8(image_np))
+            result_image=result_image.convert("L")
+
+            if start_offset>0:
+                result_image=ImageOps.invert(result_image)
+            
+            result_image=result_image.convert("L")
+            mt=pil2tensor(result_image)
+            masks.append(mt)
+         
+            # print( mt.size())
+        return (masks,)

nodes/PromptNode.py

@@ -23,6 +23,19 @@ def get_files_with_extension(directory, extension):
                 file_list.append(file_name)
     return file_list
 
+def join_with_(text_list,delimiter):
+    joined_text = delimiter.join(text_list)
+    return joined_text
+
+
+class AnyType(str):
+  """A special class that is always equal in not equal comparisons. Credit to pythongosssss"""
+
+  def __ne__(self, __value: object) -> bool:
+    return False
+
+any_type = AnyType("*")
+
 
 default_prompt1='''Swing
                                                 Slide
@@ -65,7 +78,7 @@ def addWeight(text, weight=1):
     if weight == 1:
         return text
     else:
-        return f"({text}:{round(weight,2)})"
+        return f"({text}:{round(weight,3)})"
 
 def prompt_delete_words(sentence, new_words_length):
     # 使用逗号分割句子，并去除空格
@@ -308,7 +321,9 @@ class RandomPrompt:
                             "default": 'sticker, Cartoon, ``'
                           }),
                 "random_sample": (["enable", "disable"],),
-                }
+                # "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "step": 1}),
+                },
+           
             }
     
     
@@ -394,6 +409,7 @@ class EmbeddingPrompt:
 
     # 运行的函数
     def run(self,embedding,weight):
+        weight = round(weight, 3)
         prompt='embedding:'+embedding
         if weight!=1:
             prompt='('+prompt+':'+str(weight)+')'
@@ -401,3 +417,33 @@ class EmbeddingPrompt:
         # return (new_prompt)
         return (prompt,)
 
+RETURN_TYPES = (any_type,)
+
+class JoinWithDelimiter:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "text_list": (any_type,),
+                    "delimiter":(["newline","comma"],),
+                             },
+                }
+    
+    RETURN_TYPES = ("STRING",) 
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Prompt"
+
+    INPUT_IS_LIST = True # 当true的时候，输入时list，当false的时候，如果输入是list，则会自动包一层for循环调用
+    OUTPUT_IS_LIST = (False,)
+
+    def run(self,text_list,delimiter):
+        delimiter=delimiter[0]
+        if delimiter =='newline':
+            delimiter='\n'
+        elif delimiter=='comma':
+            delimiter=','
+        t=''
+        if isinstance(text_list, list):
+            t=join_with_(text_list,delimiter)
+        return (t,)

nodes/Utils.py

@@ -341,11 +341,18 @@ class MultiplicationNode:
     def INPUT_TYPES(s):
         return {"required": {
                     "numberA":(any_type,),
-                    "numberB":("FLOAT", {
+                    "multiply_by":("FLOAT", {
                         "default": 0, 
-                        "min": -1, #Minimum value
+                        "min": -2, #Minimum value
                         "max": 0xffffffffffffffff,
-                        "step": 0.1, #Slider's step
+                        "step": 0.01, #Slider's step
+                        "display": "number" # Cosmetic only: display as "number" or "slider"
+                    }),
+                     "add_by":("FLOAT", {
+                        "default": 0, 
+                        "min": -2000, #Minimum value
+                        "max": 0xffffffffffffffff,
+                        "step": 0.01, #Slider's step
                         "display": "number" # Cosmetic only: display as "number" or "slider"
                     })
                              },
@@ -360,9 +367,9 @@ class MultiplicationNode:
     INPUT_IS_LIST = False
     OUTPUT_IS_LIST = (False,False,)
 
-    def run(self,numberA,numberB):
-        b=int(numberA*numberB)
-        a=float(numberA*numberB)
+    def run(self,numberA,multiply_by,add_by):
+        b=int(numberA*multiply_by+add_by)
+        a=float(numberA*multiply_by+add_by)
         return (a,b,) 
 
 class TextInput:

requirements.txt

@@ -4,5 +4,5 @@ watchdog
 opencv-python-headless
 matplotlib
 openai
-# simple-lama-inpainting
-# clip-interrogator==0.6.0
+simple-lama-inpainting
+clip-interrogator==0.6.0

web/index.html

@@ -391,14 +391,14 @@
             <a class="link" href="https://www.mixcomfy.com" target="_blank">ComfyUI中文爱好者社区推荐</a>
         </div>
 
-        <a target="_blank" href="https://github.com/shadowcz007/comfyui-mixlab-nodes" style="text-decoration: none;
+        <a target="_blank" href="https://www.mixcomfy.com/blog/" style="text-decoration: none;
         color: black;font-size:12px">
             <svg height="32" aria-hidden="true" viewBox="0 0 16 16" version="1.1" width="32" data-view-component="true"
                 class="octicon octicon-mark-github v-align-middle color-fg-default">
                 <path
                     d="M8 0c4.42 0 8 3.58 8 8a8.013 8.013 0 0 1-5.45 7.59c-.4.08-.55-.17-.55-.38 0-.27.01-1.13.01-2.2 0-.75-.25-1.23-.54-1.48 1.78-.2 3.65-.88 3.65-3.95 0-.88-.31-1.59-.82-2.15.08-.2.36-1.02-.08-2.12 0 0-.67-.22-2.2.82-.64-.18-1.32-.27-2-.27-.68 0-1.36.09-2 .27-1.53-1.03-2.2-.82-2.2-.82-.44 1.1-.16 1.92-.08 2.12-.51.56-.82 1.28-.82 2.15 0 3.06 1.86 3.75 3.64 3.95-.23.2-.44.55-.51 1.07-.46.21-1.61.55-2.33-.66-.15-.24-.6-.83-1.23-.82-.67.01-.27.38.01.53.34.19.73.9.82 1.13.16.45.68 1.31 2.69.94 0 .67.01 1.3.01 1.49 0 .21-.15.45-.55.38A7.995 7.995 0 0 1 0 8c0-4.42 3.58-8 8-8Z">
                 </path>
-            </svg> Code
+            </svg> Community
 
         </a>
     </div>

web/javascript/checkVersion_mixlab.js

@@ -3,7 +3,7 @@ import { app } from '../../../scripts/app.js'
 const repoOwner = 'shadowcz007' // 替换为仓库的所有者
 const repoName = 'comfyui-mixlab-nodes' // 替换为仓库的名称
 
-const version = 'v0.13.0'
+const version = 'v0.14.0'
 
 fetch(`https://api.github.com/repos/${repoOwner}/${repoName}/releases/latest`)
   .then(response => response.json())

web/javascript/main_mixlab.js

@@ -1331,7 +1331,13 @@ app.registerExtension({
           let w = 360,
             s = widget.preview.videoWidth / widget.preview.videoHeight,
             h = w / s || w
-          console.log(h)
+          // console.log(h)
+
+          if (!window.documentPictureInPicture) {
+            window.alert(
+              'This feature is available only in secure contexts (HTTPS), in some or all supporting browsers. https://developer.mozilla.org/en-US/docs/Web/API/Document_Picture-in-Picture_API'
+            )
+          }
 
           const pipWindow = await documentPictureInPicture.requestWindow({
             width: w,
@@ -2137,8 +2143,8 @@ const node = {
   loadedGraphNode (node, app) {
     if (node.type === 'RandomPrompt') {
       try {
-        let max_count = node.widgets.filter(w => w.name === "max_count")[0];
-        max_count.value= node.widgets_values[0]
+        let max_count = node.widgets.filter(w => w.name === 'max_count')[0]
+        max_count.value = node.widgets_values[0]
         // console.log('RandomPrompt',max_count,node.widgets_values[0])
       } catch (error) {
         console.log(error)

web/javascript/prompt_mixlab.js

@@ -266,16 +266,7 @@ app.registerExtension({
   },
   async loadedGraphNode (node, app) {
     if (node.type === 'RandomPrompt') {
-      // try {
-      //   let mutable_prompt = node.widgets.filter(w => w.name === 'mutable_prompt')[0]
-      //   // let ks = getLocalData(`_mixlab_PromptSlide`)
-      //   let uploadWidget = node.widgets.filter(w => w.name == 'upload')[0]
-      //   // console.log('##widget', uploadWidget.value)
-      //   let keywords = JSON.parse(uploadWidget.value)
-      //   if (keywords && keywords[0]) {
-      //     mutable_prompt.value=keywords.join('\n')
-      //   }
-      // } catch (error) {}
+     
     }
   }
 })