Style improvements (#1957)

extensions/api/blocking_api.py

@@ -2,11 +2,10 @@ import json
 from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
 from threading import Thread
 
+from extensions.api.util import build_parameters, try_start_cloudflared
 from modules import shared
 from modules.text_generation import encode, generate_reply
 
-from extensions.api.util import build_parameters, try_start_cloudflared
-
 
 class Handler(BaseHTTPRequestHandler):
     def do_GET(self):

extensions/api/script.py

@@ -5,6 +5,7 @@ from modules import shared
 BLOCKING_PORT = 5000
 STREAMING_PORT = 5005
 
+
 def setup():
     blocking_api.start_server(BLOCKING_PORT, share=shared.args.public_api)
     streaming_api.start_server(STREAMING_PORT, share=shared.args.public_api)

extensions/api/streaming_api.py

@@ -1,12 +1,12 @@
-import json
 import asyncio
-from websockets.server import serve
+import json
 from threading import Thread
 
-from modules import shared
-from modules.text_generation import generate_reply
+from websockets.server import serve
 
 from extensions.api.util import build_parameters, try_start_cloudflared
+from modules import shared
+from modules.text_generation import generate_reply
 
 PATH = '/api/v1/stream'
 

extensions/character_bias/script.py

@@ -1,6 +1,7 @@
-import gradio as gr
 import os
 
+import gradio as gr
+
 # get the current directory of the script
 current_dir = os.path.dirname(os.path.abspath(__file__))
 

extensions/llava/script.py

@@ -1,6 +1,8 @@
-import gradio as gr
 import logging
 
+import gradio as gr
+
+
 def ui():
     gr.Markdown("### This extension is deprecated, use \"multimodal\" extension instead")
     logging.error("LLaVA extension is deprecated, use \"multimodal\" extension instead")

extensions/multimodal/multimodal_embedder.py

@@ -6,10 +6,11 @@ from io import BytesIO
 from typing import Any, List, Optional
 
 import torch
+from PIL import Image
+
 from extensions.multimodal.pipeline_loader import load_pipeline
 from modules import shared
 from modules.text_generation import encode, get_max_prompt_length
-from PIL import Image
 
 
 @dataclass

extensions/multimodal/script.py

@@ -7,6 +7,7 @@ from io import BytesIO
 
 import gradio as gr
 import torch
+
 from extensions.multimodal.multimodal_embedder import MultimodalEmbedder
 from modules import shared
 

extensions/openai/script.py

@@ -1,11 +1,12 @@
 import base64
 import json
-import numpy as np
 import os
 import time
 from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
 from threading import Thread
 
+import numpy as np
+
 from modules import shared
 from modules.text_generation import encode, generate_reply
 
@@ -61,6 +62,7 @@ def float_list_to_base64(float_list):
     ascii_string = encoded_bytes.decode('ascii')
     return ascii_string
 
+
 class Handler(BaseHTTPRequestHandler):
     def do_GET(self):
         if self.path.startswith('/v1/models'):
@@ -387,8 +389,8 @@ class Handler(BaseHTTPRequestHandler):
                     "created": created_time,
                     "model": model,  # TODO: add Lora info?
                     resp_list: [{
-                            "index": 0,
-                            "finish_reason": "stop",
+                        "index": 0,
+                        "finish_reason": "stop",
                     }],
                     "usage": {
                         "prompt_tokens": token_count,

extensions/sd_api_pictures/script.py

@@ -6,12 +6,13 @@ from datetime import date
 from pathlib import Path
 
 import gradio as gr
-import modules.shared as shared
 import requests
 import torch
-from modules.models import reload_model, unload_model
 from PIL import Image
 
+import modules.shared as shared
+from modules.models import reload_model, unload_model
+
 torch._C._jit_set_profiling_mode(False)
 
 # parameters which can be customized in settings.json of webui
@@ -77,6 +78,7 @@ SD_models = ['NeverEndingDream']  # TODO: get with http://{address}}/sdapi/v1/sd
 
 picture_response = False  # specifies if the next model response should appear as a picture
 
+
 def remove_surrounded_chars(string):
     # this expression matches to 'as few symbols as possible (0 upwards) between any asterisks' OR
     # 'as few symbols as possible (0 upwards) between an asterisk and the end of the string'
@@ -122,7 +124,6 @@ def input_modifier(string):
 
 # Get and save the Stable Diffusion-generated picture
 def get_SD_pictures(description):
-
     global params
 
     if params['manage_VRAM']:
@@ -259,6 +260,7 @@ def SD_api_address_update(address):
 
     return gr.Textbox.update(label=msg)
 
+
 def ui():
 
     # Gradio elements
@@ -290,12 +292,11 @@ def ui():
                 cfg_scale = gr.Number(label="CFG Scale", value=params['cfg_scale'], elem_id="cfg_box")
                 with gr.Column() as hr_options:
                     restore_faces = gr.Checkbox(value=params['restore_faces'], label='Restore faces')
-                    enable_hr = gr.Checkbox(value=params['enable_hr'], label='Hires. fix')                    
+                    enable_hr = gr.Checkbox(value=params['enable_hr'], label='Hires. fix')
             with gr.Row(visible=params['enable_hr'], elem_classes="hires_opts") as hr_options:
-                    hr_scale = gr.Slider(1, 4, value=params['hr_scale'], step=0.1, label='Upscale by')
-                    denoising_strength = gr.Slider(0, 1, value=params['denoising_strength'], step=0.01, label='Denoising strength')
-                    hr_upscaler = gr.Textbox(placeholder=params['hr_upscaler'], value=params['hr_upscaler'], label='Upscaler')                    
-
+                hr_scale = gr.Slider(1, 4, value=params['hr_scale'], step=0.1, label='Upscale by')
+                denoising_strength = gr.Slider(0, 1, value=params['denoising_strength'], step=0.01, label='Denoising strength')
+                hr_upscaler = gr.Textbox(placeholder=params['hr_upscaler'], value=params['hr_upscaler'], label='Upscaler')
 
     # Event functions to update the parameters in the backend
     address.change(lambda x: params.update({"address": filter_address(x)}), address, None)

extensions/silero_tts/script.py

@@ -4,6 +4,7 @@ from pathlib import Path
 
 import gradio as gr
 import torch
+
 from extensions.silero_tts import tts_preprocessor
 from modules import chat, shared
 from modules.html_generator import chat_html_wrapper
@@ -216,4 +217,4 @@ def ui():
 
     # Play preview
     preview_text.submit(voice_preview, preview_text, preview_audio)
-    preview_play.click(voice_preview, preview_text, preview_audio)
+    preview_play.click(voice_preview, preview_text, preview_audio)

extensions/silero_tts/test_tts.py

@@ -2,7 +2,6 @@ import time
 from pathlib import Path
 
 import torch
-
 import tts_preprocessor
 
 torch._C._jit_set_profiling_mode(False)

extensions/silero_tts/tts_preprocessor.py

@@ -69,7 +69,7 @@ def remove_surrounded_chars(string):
     # first this expression will check if there is a string nested exclusively between a alt=
     # and a style= string. This would correspond to only a the alt text of an embedded image
     # If it matches it will only keep that part as the string, and rend it for further processing
-    # Afterwards this expression matches to 'as few symbols as possible (0 upwards) between any 
+    # Afterwards this expression matches to 'as few symbols as possible (0 upwards) between any
     # asterisks' OR' as few symbols as possible (0 upwards) between an asterisk and the end of the string'
     if re.search(r'(?<=alt=)(.*)(?=style=)', string, re.DOTALL):
         m = re.search(r'(?<=alt=)(.*)(?=style=)', string, re.DOTALL)

extensions/superbooga/script.py

@@ -59,7 +59,7 @@ class ChromaCollector(Collecter):
     def get_ids(self, search_strings: list[str], n_results: int) -> list[str]:
         n_results = min(len(self.ids), n_results)
         result = self.collection.query(query_texts=search_strings, n_results=n_results, include=['documents'])['ids'][0]
-        return list(map(lambda x : int(x[2:]), result))
+        return list(map(lambda x: int(x[2:]), result))
 
     def clear(self):
         self.collection.delete(ids=self.ids)
@@ -162,13 +162,13 @@ def input_modifier(string):
 def custom_generate_chat_prompt(user_input, state, **kwargs):
     if len(shared.history['internal']) > 2 and user_input != '':
         chunks = []
-        for i in range(len(shared.history['internal'])-1):
+        for i in range(len(shared.history['internal']) - 1):
             chunks.append('\n'.join(shared.history['internal'][i]))
 
         add_chunks_to_collector(chunks)
         query = '\n'.join(shared.history['internal'][-1] + [user_input])
         try:
-            best_ids = collector.get_ids(query, n_results=len(shared.history['internal'])-1)
+            best_ids = collector.get_ids(query, n_results=len(shared.history['internal']) - 1)
 
             # Sort the history by relevance instead of by chronological order,
             # except for the latest message
@@ -226,7 +226,7 @@ def ui():
 
         ## Chat mode
 
-        In chat mode, the extension automatically sorts the history by relevance instead of chronologically, except for the very latest input/reply pair. 
+        In chat mode, the extension automatically sorts the history by relevance instead of chronologically, except for the very latest input/reply pair.
 
         That is, the prompt will include (starting from the end):
 

extensions/whisper_stt/script.py

@@ -1,5 +1,6 @@
 import gradio as gr
 import speech_recognition as sr
+
 from modules import shared
 
 input_hijack = {