mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-29 19:09:32 +01:00
541 lines
33 KiB
Python
541 lines
33 KiB
Python
import io
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import json
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import re
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import sys
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import time
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import zipfile
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from datetime import datetime
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from pathlib import Path
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import gradio as gr
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from PIL import Image
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import modules.extensions as extensions_module
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from modules import chat, shared, training, ui
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from modules.html_generator import generate_chat_html
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from modules.LoRA import add_lora_to_model
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from modules.models import load_model, load_soft_prompt
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from modules.text_generation import (clear_torch_cache, generate_reply,
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stop_everything_event)
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# Loading custom settings
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settings_file = None
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if shared.args.settings is not None and Path(shared.args.settings).exists():
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settings_file = Path(shared.args.settings)
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elif Path('settings.json').exists():
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settings_file = Path('settings.json')
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if settings_file is not None:
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print(f"Loading settings from {settings_file}...")
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new_settings = json.loads(open(settings_file, 'r').read())
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for item in new_settings:
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shared.settings[item] = new_settings[item]
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def get_available_models():
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if shared.args.flexgen:
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return sorted([re.sub('-np$', '', item.name) for item in list(Path(f'{shared.args.model_dir}/').glob('*')) if item.name.endswith('-np')], key=str.lower)
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else:
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return sorted([re.sub('.pth$', '', item.name) for item in list(Path(f'{shared.args.model_dir}/').glob('*')) if not item.name.endswith(('.txt', '-np', '.pt', '.json'))], key=str.lower)
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def get_available_presets():
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return sorted(set((k.stem for k in Path('presets').glob('*.txt'))), key=str.lower)
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def get_available_prompts():
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prompts = []
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prompts += sorted(set((k.stem for k in Path('prompts').glob('[0-9]*.txt'))), key=str.lower, reverse=True)
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prompts += sorted(set((k.stem for k in Path('prompts').glob('*.txt'))), key=str.lower)
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prompts += ['None']
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return prompts
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def get_available_characters():
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paths = (x for x in Path('characters').iterdir() if x.suffix in ('.json', '.yaml', '.yml'))
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return ['None'] + sorted(set((k.stem for k in paths)), key=str.lower)
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def get_available_extensions():
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return sorted(set(map(lambda x : x.parts[1], Path('extensions').glob('*/script.py'))), key=str.lower)
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def get_available_softprompts():
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return ['None'] + sorted(set((k.stem for k in Path('softprompts').glob('*.zip'))), key=str.lower)
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def get_available_loras():
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return ['None'] + sorted([item.name for item in list(Path(shared.args.lora_dir).glob('*')) if not item.name.endswith(('.txt', '-np', '.pt', '.json'))], key=str.lower)
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def unload_model():
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shared.model = shared.tokenizer = None
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clear_torch_cache()
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def load_model_wrapper(selected_model):
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if selected_model != shared.model_name:
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shared.model_name = selected_model
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unload_model()
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if selected_model != '':
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shared.model, shared.tokenizer = load_model(shared.model_name)
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return selected_model
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def load_lora_wrapper(selected_lora):
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add_lora_to_model(selected_lora)
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return selected_lora
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def load_preset_values(preset_menu, return_dict=False):
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generate_params = {
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'do_sample': True,
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'temperature': 1,
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'top_p': 1,
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'typical_p': 1,
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'repetition_penalty': 1,
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'encoder_repetition_penalty': 1,
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'top_k': 50,
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'num_beams': 1,
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'penalty_alpha': 0,
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'min_length': 0,
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'length_penalty': 1,
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'no_repeat_ngram_size': 0,
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'early_stopping': False,
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}
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with open(Path(f'presets/{preset_menu}.txt'), 'r') as infile:
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preset = infile.read()
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for i in preset.splitlines():
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i = i.rstrip(',').strip().split('=')
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if len(i) == 2 and i[0].strip() != 'tokens':
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generate_params[i[0].strip()] = eval(i[1].strip())
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generate_params['temperature'] = min(1.99, generate_params['temperature'])
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if return_dict:
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return generate_params
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else:
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return generate_params['do_sample'], generate_params['temperature'], generate_params['top_p'], generate_params['typical_p'], generate_params['repetition_penalty'], generate_params['encoder_repetition_penalty'], generate_params['top_k'], generate_params['min_length'], generate_params['no_repeat_ngram_size'], generate_params['num_beams'], generate_params['penalty_alpha'], generate_params['length_penalty'], generate_params['early_stopping']
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def upload_soft_prompt(file):
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with zipfile.ZipFile(io.BytesIO(file)) as zf:
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zf.extract('meta.json')
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j = json.loads(open('meta.json', 'r').read())
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name = j['name']
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Path('meta.json').unlink()
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with open(Path(f'softprompts/{name}.zip'), 'wb') as f:
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f.write(file)
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return name
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def create_model_and_preset_menus():
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with gr.Row():
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with gr.Column():
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with gr.Row():
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shared.gradio['model_menu'] = gr.Dropdown(choices=available_models, value=shared.model_name, label='Model')
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ui.create_refresh_button(shared.gradio['model_menu'], lambda : None, lambda : {'choices': get_available_models()}, 'refresh-button')
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with gr.Column():
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with gr.Row():
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shared.gradio['preset_menu'] = gr.Dropdown(choices=available_presets, value=default_preset if not shared.args.flexgen else 'Naive', label='Generation parameters preset')
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ui.create_refresh_button(shared.gradio['preset_menu'], lambda : None, lambda : {'choices': get_available_presets()}, 'refresh-button')
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def save_prompt(text):
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fname = f"{datetime.now().strftime('%Y-%m-%d-%H:%M:%S')}.txt"
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with open(Path(f'prompts/{fname}'), 'w', encoding='utf-8') as f:
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f.write(text)
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return f"Saved to prompts/{fname}"
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def load_prompt(fname):
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if fname in ['None', '']:
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return ''
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else:
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with open(Path(f'prompts/{fname}.txt'), 'r', encoding='utf-8') as f:
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text = f.read()
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if text[-1] == '\n':
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text = text[:-1]
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return text
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def create_prompt_menus():
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with gr.Row():
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with gr.Column():
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with gr.Row():
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shared.gradio['prompt_menu'] = gr.Dropdown(choices=get_available_prompts(), value='None', label='Prompt')
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ui.create_refresh_button(shared.gradio['prompt_menu'], lambda : None, lambda : {'choices': get_available_prompts()}, 'refresh-button')
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with gr.Column():
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with gr.Column():
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shared.gradio['save_prompt'] = gr.Button('Save prompt')
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shared.gradio['status'] = gr.Markdown('Ready')
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shared.gradio['prompt_menu'].change(load_prompt, [shared.gradio['prompt_menu']], [shared.gradio['textbox']], show_progress=False)
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shared.gradio['save_prompt'].click(save_prompt, [shared.gradio['textbox']], [shared.gradio['status']], show_progress=False)
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def create_settings_menus(default_preset):
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generate_params = load_preset_values(default_preset if not shared.args.flexgen else 'Naive', return_dict=True)
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with gr.Row():
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with gr.Column():
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create_model_and_preset_menus()
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with gr.Column():
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shared.gradio['seed'] = gr.Number(value=shared.settings['seed'], label='Seed (-1 for random)')
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with gr.Row():
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with gr.Column():
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with gr.Box():
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gr.Markdown('Custom generation parameters ([reference](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.GenerationConfig))')
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with gr.Row():
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with gr.Column():
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shared.gradio['temperature'] = gr.Slider(0.01, 1.99, value=generate_params['temperature'], step=0.01, label='temperature')
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shared.gradio['top_p'] = gr.Slider(0.0,1.0,value=generate_params['top_p'],step=0.01,label='top_p')
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shared.gradio['top_k'] = gr.Slider(0,200,value=generate_params['top_k'],step=1,label='top_k')
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shared.gradio['typical_p'] = gr.Slider(0.0,1.0,value=generate_params['typical_p'],step=0.01,label='typical_p')
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with gr.Column():
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shared.gradio['repetition_penalty'] = gr.Slider(1.0, 1.5, value=generate_params['repetition_penalty'],step=0.01,label='repetition_penalty')
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shared.gradio['encoder_repetition_penalty'] = gr.Slider(0.8, 1.5, value=generate_params['encoder_repetition_penalty'],step=0.01,label='encoder_repetition_penalty')
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shared.gradio['no_repeat_ngram_size'] = gr.Slider(0, 20, step=1, value=generate_params['no_repeat_ngram_size'], label='no_repeat_ngram_size')
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shared.gradio['min_length'] = gr.Slider(0, 2000, step=1, value=generate_params['min_length'] if shared.args.no_stream else 0, label='min_length', interactive=shared.args.no_stream)
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shared.gradio['do_sample'] = gr.Checkbox(value=generate_params['do_sample'], label='do_sample')
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with gr.Column():
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with gr.Box():
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gr.Markdown('Contrastive search')
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shared.gradio['penalty_alpha'] = gr.Slider(0, 5, value=generate_params['penalty_alpha'], label='penalty_alpha')
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with gr.Box():
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gr.Markdown('Beam search (uses a lot of VRAM)')
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with gr.Row():
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with gr.Column():
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shared.gradio['num_beams'] = gr.Slider(1, 20, step=1, value=generate_params['num_beams'], label='num_beams')
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with gr.Column():
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shared.gradio['length_penalty'] = gr.Slider(-5, 5, value=generate_params['length_penalty'], label='length_penalty')
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shared.gradio['early_stopping'] = gr.Checkbox(value=generate_params['early_stopping'], label='early_stopping')
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with gr.Row():
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shared.gradio['lora_menu'] = gr.Dropdown(choices=available_loras, value=shared.lora_name, label='LoRA')
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ui.create_refresh_button(shared.gradio['lora_menu'], lambda : None, lambda : {'choices': get_available_loras()}, 'refresh-button')
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with gr.Accordion('Soft prompt', open=False):
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with gr.Row():
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shared.gradio['softprompts_menu'] = gr.Dropdown(choices=available_softprompts, value='None', label='Soft prompt')
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ui.create_refresh_button(shared.gradio['softprompts_menu'], lambda : None, lambda : {'choices': get_available_softprompts()}, 'refresh-button')
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gr.Markdown('Upload a soft prompt (.zip format):')
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with gr.Row():
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shared.gradio['upload_softprompt'] = gr.File(type='binary', file_types=['.zip'])
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shared.gradio['model_menu'].change(load_model_wrapper, [shared.gradio['model_menu']], [shared.gradio['model_menu']], show_progress=True)
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shared.gradio['preset_menu'].change(load_preset_values, [shared.gradio['preset_menu']], [shared.gradio[k] for k in ['do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping']])
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shared.gradio['lora_menu'].change(load_lora_wrapper, [shared.gradio['lora_menu']], [shared.gradio['lora_menu']], show_progress=True)
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shared.gradio['softprompts_menu'].change(load_soft_prompt, [shared.gradio['softprompts_menu']], [shared.gradio['softprompts_menu']], show_progress=True)
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shared.gradio['upload_softprompt'].upload(upload_soft_prompt, [shared.gradio['upload_softprompt']], [shared.gradio['softprompts_menu']])
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def set_interface_arguments(interface_mode, extensions, bool_active):
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modes = ["default", "notebook", "chat", "cai_chat"]
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cmd_list = vars(shared.args)
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bool_list = [k for k in cmd_list if type(cmd_list[k]) is bool and k not in modes]
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#int_list = [k for k in cmd_list if type(k) is int]
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shared.args.extensions = extensions
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for k in modes[1:]:
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exec(f"shared.args.{k} = False")
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if interface_mode != "default":
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exec(f"shared.args.{interface_mode} = True")
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for k in bool_list:
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exec(f"shared.args.{k} = False")
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for k in bool_active:
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exec(f"shared.args.{k} = True")
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shared.need_restart = True
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available_models = get_available_models()
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available_presets = get_available_presets()
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available_characters = get_available_characters()
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available_softprompts = get_available_softprompts()
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available_loras = get_available_loras()
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# Default extensions
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extensions_module.available_extensions = get_available_extensions()
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if shared.is_chat():
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for extension in shared.settings['chat_default_extensions']:
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shared.args.extensions = shared.args.extensions or []
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if extension not in shared.args.extensions:
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shared.args.extensions.append(extension)
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else:
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for extension in shared.settings['default_extensions']:
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shared.args.extensions = shared.args.extensions or []
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if extension not in shared.args.extensions:
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shared.args.extensions.append(extension)
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# Default model
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if shared.args.model is not None:
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shared.model_name = shared.args.model
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else:
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if len(available_models) == 0:
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print('No models are available! Please download at least one.')
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sys.exit(0)
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elif len(available_models) == 1:
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i = 0
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else:
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print('The following models are available:\n')
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for i, model in enumerate(available_models):
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print(f'{i+1}. {model}')
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print(f'\nWhich one do you want to load? 1-{len(available_models)}\n')
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i = int(input())-1
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print()
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shared.model_name = available_models[i]
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shared.model, shared.tokenizer = load_model(shared.model_name)
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if shared.args.lora:
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add_lora_to_model(shared.args.lora)
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# Default UI settings
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default_preset = shared.settings['presets'][next((k for k in shared.settings['presets'] if re.match(k.lower(), shared.model_name.lower())), 'default')]
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if shared.lora_name != "None":
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default_text = load_prompt(shared.settings['lora_prompts'][next((k for k in shared.settings['lora_prompts'] if re.match(k.lower(), shared.lora_name.lower())), 'default')])
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else:
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default_text = load_prompt(shared.settings['prompts'][next((k for k in shared.settings['prompts'] if re.match(k.lower(), shared.model_name.lower())), 'default')])
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title ='Text generation web UI'
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def create_interface():
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gen_events = []
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if shared.args.extensions is not None and len(shared.args.extensions) > 0:
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extensions_module.load_extensions()
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with gr.Blocks(css=ui.css if not shared.is_chat() else ui.css+ui.chat_css, analytics_enabled=False, title=title) as shared.gradio['interface']:
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if shared.is_chat():
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shared.gradio['Chat input'] = gr.State()
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with gr.Tab("Text generation", elem_id="main"):
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if shared.args.cai_chat:
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shared.gradio['display'] = gr.HTML(value=generate_chat_html(shared.history['visible'], shared.settings['name1'], shared.settings['name2']))
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else:
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shared.gradio['display'] = gr.Chatbot(value=shared.history['visible'], elem_id="gradio-chatbot")
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shared.gradio['textbox'] = gr.Textbox(label='Input')
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with gr.Row():
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shared.gradio['Generate'] = gr.Button('Generate')
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shared.gradio['Stop'] = gr.Button('Stop', elem_id="stop")
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with gr.Row():
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shared.gradio['Impersonate'] = gr.Button('Impersonate')
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shared.gradio['Regenerate'] = gr.Button('Regenerate')
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with gr.Row():
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shared.gradio['Copy last reply'] = gr.Button('Copy last reply')
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shared.gradio['Replace last reply'] = gr.Button('Replace last reply')
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shared.gradio['Remove last'] = gr.Button('Remove last')
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shared.gradio['Clear history'] = gr.Button('Clear history')
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shared.gradio['Clear history-confirm'] = gr.Button('Confirm', variant="stop", visible=False)
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shared.gradio['Clear history-cancel'] = gr.Button('Cancel', visible=False)
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with gr.Tab("Character", elem_id="chat-settings"):
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with gr.Row():
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with gr.Column(scale=8):
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shared.gradio['name1'] = gr.Textbox(value=shared.settings['name1'], lines=1, label='Your name')
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shared.gradio['name2'] = gr.Textbox(value=shared.settings['name2'], lines=1, label='Character\'s name')
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shared.gradio['greeting'] = gr.Textbox(value=shared.settings['greeting'], lines=2, label='Greeting')
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shared.gradio['context'] = gr.Textbox(value=shared.settings['context'], lines=8, label='Context')
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with gr.Column(scale=1):
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shared.gradio['character_picture'] = gr.Image(label='Character picture', type="pil")
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shared.gradio['your_picture'] = gr.Image(label='Your picture', type="pil", value=Image.open(Path("cache/pfp_me.png")) if Path("cache/pfp_me.png").exists() else None)
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with gr.Row():
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shared.gradio['character_menu'] = gr.Dropdown(choices=available_characters, value='None', label='Character', elem_id='character-menu')
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ui.create_refresh_button(shared.gradio['character_menu'], lambda : None, lambda : {'choices': get_available_characters()}, 'refresh-button')
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with gr.Row():
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with gr.Tab('Chat history'):
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with gr.Row():
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with gr.Column():
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gr.Markdown('Upload')
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shared.gradio['upload_chat_history'] = gr.File(type='binary', file_types=['.json', '.txt'])
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with gr.Column():
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gr.Markdown('Download')
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shared.gradio['download'] = gr.File()
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shared.gradio['download_button'] = gr.Button(value='Click me')
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with gr.Tab('Upload character'):
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gr.Markdown("# JSON format")
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with gr.Row():
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with gr.Column():
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gr.Markdown('1. Select the JSON file')
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shared.gradio['upload_json'] = gr.File(type='binary', file_types=['.json'])
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with gr.Column():
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gr.Markdown('2. Select your character\'s profile picture (optional)')
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shared.gradio['upload_img_bot'] = gr.File(type='binary', file_types=['image'])
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shared.gradio['Upload character'] = gr.Button(value='Submit')
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gr.Markdown("# TavernAI PNG format")
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shared.gradio['upload_img_tavern'] = gr.File(type='binary', file_types=['image'])
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with gr.Tab("Parameters", elem_id="parameters"):
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with gr.Box():
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gr.Markdown("Chat parameters")
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with gr.Row():
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with gr.Column():
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shared.gradio['max_new_tokens'] = gr.Slider(minimum=shared.settings['max_new_tokens_min'], maximum=shared.settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=shared.settings['max_new_tokens'])
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shared.gradio['chat_prompt_size_slider'] = gr.Slider(minimum=shared.settings['chat_prompt_size_min'], maximum=shared.settings['chat_prompt_size_max'], step=1, label='Maximum prompt size in tokens', value=shared.settings['chat_prompt_size'])
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with gr.Column():
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shared.gradio['chat_generation_attempts'] = gr.Slider(minimum=shared.settings['chat_generation_attempts_min'], maximum=shared.settings['chat_generation_attempts_max'], value=shared.settings['chat_generation_attempts'], step=1, label='Generation attempts (for longer replies)')
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shared.gradio['check'] = gr.Checkbox(value=shared.settings['stop_at_newline'], label='Stop generating at new line character?')
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|
|
|
create_settings_menus(default_preset)
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|
|
|
function_call = 'chat.cai_chatbot_wrapper' if shared.args.cai_chat else 'chat.chatbot_wrapper'
|
|
shared.input_params = [shared.gradio[k] for k in ['Chat input', 'max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'seed', 'name1', 'name2', 'context', 'check', 'chat_prompt_size_slider', 'chat_generation_attempts']]
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|
|
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def set_chat_input(textbox):
|
|
return textbox, ""
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|
|
|
gen_events.append(shared.gradio['Generate'].click(set_chat_input, shared.gradio['textbox'], [shared.gradio['Chat input'], shared.gradio['textbox']], show_progress=False))
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gen_events.append(shared.gradio['Generate'].click(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream))
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|
gen_events.append(shared.gradio['textbox'].submit(set_chat_input, shared.gradio['textbox'], [shared.gradio['Chat input'], shared.gradio['textbox']], show_progress=False))
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|
gen_events.append(shared.gradio['textbox'].submit(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream))
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gen_events.append(shared.gradio['Regenerate'].click(chat.regenerate_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream))
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gen_events.append(shared.gradio['Impersonate'].click(chat.impersonate_wrapper, shared.input_params, shared.gradio['textbox'], show_progress=shared.args.no_stream))
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shared.gradio['Stop'].click(stop_everything_event, [], [], queue=False, cancels=gen_events if shared.args.no_stream else None)
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shared.gradio['Copy last reply'].click(chat.send_last_reply_to_input, [], shared.gradio['textbox'], show_progress=shared.args.no_stream)
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shared.gradio['Replace last reply'].click(chat.replace_last_reply, [shared.gradio['textbox'], shared.gradio['name1'], shared.gradio['name2']], shared.gradio['display'], show_progress=shared.args.no_stream)
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|
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# Clear history with confirmation
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clear_arr = [shared.gradio[k] for k in ['Clear history-confirm', 'Clear history', 'Clear history-cancel']]
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shared.gradio['Clear history'].click(lambda :[gr.update(visible=True), gr.update(visible=False), gr.update(visible=True)], None, clear_arr)
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shared.gradio['Clear history-confirm'].click(lambda :[gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, clear_arr)
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shared.gradio['Clear history-confirm'].click(chat.clear_chat_log, [shared.gradio['name1'], shared.gradio['name2'], shared.gradio['greeting']], shared.gradio['display'])
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shared.gradio['Clear history-cancel'].click(lambda :[gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, clear_arr)
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shared.gradio['Remove last'].click(chat.remove_last_message, [shared.gradio['name1'], shared.gradio['name2']], [shared.gradio['display'], shared.gradio['textbox']], show_progress=False)
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shared.gradio['download_button'].click(chat.save_history, inputs=[], outputs=[shared.gradio['download']])
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shared.gradio['Upload character'].click(chat.upload_character, [shared.gradio['upload_json'], shared.gradio['upload_img_bot']], [shared.gradio['character_menu']])
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|
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# Clearing stuff and saving the history
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|
for i in ['Generate', 'Regenerate', 'Replace last reply']:
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shared.gradio[i].click(lambda x: '', shared.gradio['textbox'], shared.gradio['textbox'], show_progress=False)
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shared.gradio[i].click(lambda : chat.save_history(timestamp=False), [], [], show_progress=False)
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|
shared.gradio['Clear history-confirm'].click(lambda : chat.save_history(timestamp=False), [], [], show_progress=False)
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|
shared.gradio['textbox'].submit(lambda x: '', shared.gradio['textbox'], shared.gradio['textbox'], show_progress=False)
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|
shared.gradio['textbox'].submit(lambda : chat.save_history(timestamp=False), [], [], show_progress=False)
|
|
|
|
shared.gradio['character_menu'].change(chat.load_character, [shared.gradio[k] for k in ['character_menu', 'name1', 'name2']], [shared.gradio[k] for k in ['name1', 'name2', 'character_picture', 'greeting', 'context', 'display']])
|
|
shared.gradio['upload_chat_history'].upload(chat.load_history, [shared.gradio['upload_chat_history'], shared.gradio['name1'], shared.gradio['name2']], [])
|
|
shared.gradio['upload_img_tavern'].upload(chat.upload_tavern_character, [shared.gradio['upload_img_tavern'], shared.gradio['name1'], shared.gradio['name2']], [shared.gradio['character_menu']])
|
|
shared.gradio['your_picture'].change(chat.upload_your_profile_picture, shared.gradio['your_picture'], None)
|
|
|
|
reload_func = chat.redraw_html if shared.args.cai_chat else lambda : shared.history['visible']
|
|
reload_inputs = [shared.gradio['name1'], shared.gradio['name2']] if shared.args.cai_chat else []
|
|
shared.gradio['upload_chat_history'].upload(reload_func, reload_inputs, [shared.gradio['display']])
|
|
shared.gradio['your_picture'].change(reload_func, reload_inputs, [shared.gradio['display']])
|
|
shared.gradio['Stop'].click(reload_func, reload_inputs, [shared.gradio['display']])
|
|
|
|
shared.gradio['interface'].load(None, None, None, _js=f"() => {{{ui.main_js+ui.chat_js}}}")
|
|
shared.gradio['interface'].load(lambda : chat.load_default_history(shared.settings['name1'], shared.settings['name2']), None, None)
|
|
shared.gradio['interface'].load(reload_func, reload_inputs, [shared.gradio['display']], show_progress=True)
|
|
|
|
elif shared.args.notebook:
|
|
with gr.Tab("Text generation", elem_id="main"):
|
|
with gr.Row():
|
|
with gr.Column(scale=4):
|
|
with gr.Tab('Raw'):
|
|
shared.gradio['textbox'] = gr.Textbox(value=default_text, elem_id="textbox", lines=27)
|
|
with gr.Tab('Markdown'):
|
|
shared.gradio['markdown'] = gr.Markdown()
|
|
with gr.Tab('HTML'):
|
|
shared.gradio['html'] = gr.HTML()
|
|
|
|
with gr.Row():
|
|
with gr.Column():
|
|
with gr.Row():
|
|
shared.gradio['Generate'] = gr.Button('Generate')
|
|
shared.gradio['Stop'] = gr.Button('Stop')
|
|
with gr.Column():
|
|
pass
|
|
|
|
with gr.Column(scale=1):
|
|
gr.HTML('<div style="padding-bottom: 13px"></div>')
|
|
shared.gradio['max_new_tokens'] = gr.Slider(minimum=shared.settings['max_new_tokens_min'], maximum=shared.settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=shared.settings['max_new_tokens'])
|
|
|
|
create_prompt_menus()
|
|
|
|
with gr.Tab("Parameters", elem_id="parameters"):
|
|
create_settings_menus(default_preset)
|
|
|
|
shared.input_params = [shared.gradio[k] for k in ['textbox', 'max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'seed']]
|
|
output_params = [shared.gradio[k] for k in ['textbox', 'markdown', 'html']]
|
|
gen_events.append(shared.gradio['Generate'].click(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream, api_name='textgen'))
|
|
gen_events.append(shared.gradio['textbox'].submit(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream))
|
|
shared.gradio['Stop'].click(stop_everything_event, [], [], queue=False, cancels=gen_events if shared.args.no_stream else None)
|
|
shared.gradio['interface'].load(None, None, None, _js=f"() => {{{ui.main_js}}}")
|
|
|
|
else:
|
|
with gr.Tab("Text generation", elem_id="main"):
|
|
with gr.Row():
|
|
with gr.Column():
|
|
shared.gradio['textbox'] = gr.Textbox(value=default_text, lines=21, label='Input')
|
|
shared.gradio['max_new_tokens'] = gr.Slider(minimum=shared.settings['max_new_tokens_min'], maximum=shared.settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=shared.settings['max_new_tokens'])
|
|
shared.gradio['Generate'] = gr.Button('Generate')
|
|
with gr.Row():
|
|
with gr.Column():
|
|
shared.gradio['Continue'] = gr.Button('Continue')
|
|
with gr.Column():
|
|
shared.gradio['Stop'] = gr.Button('Stop')
|
|
|
|
create_prompt_menus()
|
|
|
|
with gr.Column():
|
|
with gr.Tab('Raw'):
|
|
shared.gradio['output_textbox'] = gr.Textbox(lines=27, label='Output')
|
|
with gr.Tab('Markdown'):
|
|
shared.gradio['markdown'] = gr.Markdown()
|
|
with gr.Tab('HTML'):
|
|
shared.gradio['html'] = gr.HTML()
|
|
|
|
with gr.Tab("Parameters", elem_id="parameters"):
|
|
create_settings_menus(default_preset)
|
|
|
|
shared.input_params = [shared.gradio[k] for k in ['textbox', 'max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'seed']]
|
|
output_params = [shared.gradio[k] for k in ['output_textbox', 'markdown', 'html']]
|
|
gen_events.append(shared.gradio['Generate'].click(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream, api_name='textgen'))
|
|
gen_events.append(shared.gradio['textbox'].submit(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream))
|
|
gen_events.append(shared.gradio['Continue'].click(generate_reply, [shared.gradio['output_textbox']] + shared.input_params[1:], output_params, show_progress=shared.args.no_stream))
|
|
shared.gradio['Stop'].click(stop_everything_event, [], [], queue=False, cancels=gen_events if shared.args.no_stream else None)
|
|
shared.gradio['interface'].load(None, None, None, _js=f"() => {{{ui.main_js}}}")
|
|
|
|
with gr.Tab("Training", elem_id="training-tab"):
|
|
training.create_train_interface()
|
|
|
|
with gr.Tab("Interface mode", elem_id="interface-mode"):
|
|
modes = ["default", "notebook", "chat", "cai_chat"]
|
|
current_mode = "default"
|
|
for mode in modes[1:]:
|
|
if eval(f"shared.args.{mode}"):
|
|
current_mode = mode
|
|
break
|
|
cmd_list = vars(shared.args)
|
|
bool_list = [k for k in cmd_list if type(cmd_list[k]) is bool and k not in modes]
|
|
bool_active = [k for k in bool_list if vars(shared.args)[k]]
|
|
#int_list = [k for k in cmd_list if type(k) is int]
|
|
|
|
gr.Markdown("*Experimental*")
|
|
shared.gradio['interface_modes_menu'] = gr.Dropdown(choices=modes, value=current_mode, label="Mode")
|
|
shared.gradio['extensions_menu'] = gr.CheckboxGroup(choices=get_available_extensions(), value=shared.args.extensions, label="Available extensions")
|
|
shared.gradio['bool_menu'] = gr.CheckboxGroup(choices=bool_list, value=bool_active, label="Boolean command-line flags")
|
|
shared.gradio['reset_interface'] = gr.Button("Apply and restart the interface", type="primary")
|
|
|
|
shared.gradio['reset_interface'].click(set_interface_arguments, [shared.gradio[k] for k in ['interface_modes_menu', 'extensions_menu', 'bool_menu']], None)
|
|
shared.gradio['reset_interface'].click(lambda : None, None, None, _js='() => {document.body.innerHTML=\'<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>\'; setTimeout(function(){location.reload()},2500); return []}')
|
|
|
|
if shared.args.extensions is not None:
|
|
extensions_module.create_extensions_block()
|
|
|
|
# Authentication
|
|
auth = None
|
|
if shared.args.gradio_auth_path is not None:
|
|
gradio_auth_creds = []
|
|
with open(shared.args.gradio_auth_path, 'r', encoding="utf8") as file:
|
|
for line in file.readlines():
|
|
gradio_auth_creds += [x.strip() for x in line.split(',') if x.strip()]
|
|
auth = [tuple(cred.split(':')) for cred in gradio_auth_creds]
|
|
|
|
# Launch the interface
|
|
shared.gradio['interface'].queue()
|
|
if shared.args.listen:
|
|
shared.gradio['interface'].launch(prevent_thread_lock=True, share=shared.args.share, server_name='0.0.0.0', server_port=shared.args.listen_port, inbrowser=shared.args.auto_launch, auth=auth)
|
|
else:
|
|
shared.gradio['interface'].launch(prevent_thread_lock=True, share=shared.args.share, server_port=shared.args.listen_port, inbrowser=shared.args.auto_launch, auth=auth)
|
|
|
|
create_interface()
|
|
|
|
while True:
|
|
time.sleep(0.5)
|
|
if shared.need_restart:
|
|
shared.need_restart = False
|
|
shared.gradio['interface'].close()
|
|
create_interface()
|