mirror of
https://github.com/sean1832/GPT-Brain
synced 2024-11-18 21:25:53 +00:00
230 lines
8.5 KiB
Python
230 lines
8.5 KiB
Python
import streamlit as st
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from modules import utilities as util
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from modules import model_data
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from modules import language
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import brain
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import check_update
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import time
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import os
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# activate session
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if 'SESSION_TIME' not in st.session_state:
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st.session_state['SESSION_TIME'] = time.strftime("%Y%m%d-%H%H%S")
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if 'SESSION_LANGUAGE' not in st.session_state:
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st.session_state['SESSION_LANGUAGE'] = util.read_json_at('.user/language.json', 'SESSION_LANGUAGE', 'en_US')
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st.set_page_config(
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page_title='GPT Brain'
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)
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util.remove_oldest_file('.user/log', 10)
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model_options = ['text-davinci-003', 'text-curie-001', 'text-babbage-001', 'text-ada-001']
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header = st.container()
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body = st.container()
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LOG_PATH = '.user/log'
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SESSION_TIME = st.session_state['SESSION_TIME']
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SESSION_LANG = st.session_state['SESSION_LANGUAGE']
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PROMPT_PATH = f'.user/prompt/{SESSION_LANG}'
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CURRENT_LOG_FILE = f'{LOG_PATH}/log_{SESSION_TIME}.log'
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BRAIN_MEMO = '.user/brain-memo.json'
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MANIFEST = '.core/manifest.json'
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def create_log():
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if not os.path.exists(CURRENT_LOG_FILE):
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util.write_file(f'Session {SESSION_TIME}\n\n', CURRENT_LOG_FILE)
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return CURRENT_LOG_FILE
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def log(content, delimiter=''):
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log_file = create_log()
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if delimiter != '':
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delimiter = f'\n\n=============={delimiter}==============\n'
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util.write_file(f'\n{delimiter + content}', log_file, 'a')
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def clear_log():
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log_file_name = f'log_{SESSION_TIME}.log'
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for root, dirs, files in os.walk(LOG_PATH):
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for file in files:
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if not file == log_file_name:
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os.remove(os.path.join(root, file))
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def save_as():
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# download log file
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with open(CURRENT_LOG_FILE, 'rb') as f:
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content = f.read()
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st.download_button(
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label=_("📥download log"),
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data=content,
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file_name=f'log_{SESSION_TIME}.txt',
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mime='text/plain'
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)
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def process_response(query, target_model, prompt_file: str, data: model_data.param):
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# check if exclude model is not target model
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file_name = util.get_file_name(prompt_file)
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print(f"{_('Processing')} {file_name}...")
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with st.spinner(f"{_('Thinking on')} {file_name}..."):
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results = brain.run(query, target_model, prompt_file,
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data.temp,
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data.max_tokens,
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data.top_p,
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data.frequency_penalty,
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data.present_penalty)
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# displaying results
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st.header(f'📃{file_name}')
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st.info(f'{results}')
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time.sleep(1)
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log(results, delimiter=f'{file_name.upper()}')
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def message(msg, condition=None):
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if condition is not None:
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if condition:
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st.warning("⚠️" + msg)
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else:
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st.warning("⚠️" + msg)
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# sidebar
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with st.sidebar:
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_ = language.set_language()
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st.title(_('Settings'))
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language.select_language()
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prompt_files = util.scan_directory(PROMPT_PATH)
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prompt_file_names = [util.get_file_name(file) for file in prompt_files]
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prompt_dictionary = dict(zip(prompt_file_names, prompt_files))
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# remove 'my-info' from prompt dictionary
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prompt_dictionary.pop(_('my-info'))
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operation_options = list(prompt_dictionary.keys())
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print(util.read_json_at(BRAIN_MEMO, f'operations_{SESSION_LANG}', operation_options[0]))
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operations = st.multiselect(_('Operations'), operation_options, default=util.read_json_at(BRAIN_MEMO, f'operations_{SESSION_LANG}',
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operation_options[0]))
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last_question_model = util.read_json_at(BRAIN_MEMO, 'question_model', model_options[0])
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# get index of last question model
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question_model_index = util.get_index(model_options, last_question_model)
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question_model = st.selectbox(_('Question Model'), model_options, index=question_model_index)
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operations_no_question = [op for op in operations if op != _('question')]
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other_models = []
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replace_tokens = []
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for operation in operations_no_question:
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last_model = util.read_json_at(BRAIN_MEMO, f'{operation}_model', model_options[0])
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# get index of last model
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model_index = util.get_index(model_options, last_model)
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model = st.selectbox(f"{operation} " + _('Model'), model_options, index=model_index)
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other_models.append(model)
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temp = st.slider(_('Temperature'), 0.0, 1.0, value=util.read_json_at(BRAIN_MEMO, 'temp', 0.1))
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max_tokens = st.slider(_('Max Tokens'), 850, 4500, value=util.read_json_at(BRAIN_MEMO, 'max_tokens', 1000))
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with st.expander(label=_('Advanced Options')):
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top_p = st.slider(_('Top_P'), 0.0, 1.0, value=util.read_json_at(BRAIN_MEMO, 'top_p', 1.0))
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freq_panl = st.slider(_('Frequency penalty'), 0.0, 1.0,
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value=util.read_json_at(BRAIN_MEMO, 'frequency_penalty', 0.0))
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pres_panl = st.slider(_('Presence penalty'), 0.0, 1.0,
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value=util.read_json_at(BRAIN_MEMO, 'present_penalty', 0.0))
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chunk_size = st.slider(_('Chunk size'), 1500, 4500, value=util.read_json_at(BRAIN_MEMO, 'chunk_size', 4000))
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chunk_count = st.slider(_('Answer count'), 1, 5, value=util.read_json_at(BRAIN_MEMO, 'chunk_count', 1))
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param = model_data.param(temp=temp,
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max_tokens=max_tokens,
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top_p=top_p,
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frequency_penalty=freq_panl,
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present_penalty=pres_panl,
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chunk_size=chunk_size,
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chunk_count=chunk_count)
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if st.button(_('Clear Log'), on_click=clear_log):
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st.success(_('Log Cleared'))
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# info
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st.markdown('---')
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st.markdown(f"# {util.read_json_at(MANIFEST, 'name')}")
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st.markdown(_('version') + f": {util.read_json_at(MANIFEST, 'version')}")
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st.markdown(_('author') + f": {util.read_json_at(MANIFEST, 'author')}")
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st.markdown("[" + _('Report bugs') + "]" + f"({util.read_json_at(MANIFEST, 'bugs')})")
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st.markdown("[" + _('Github Repo') + "]" + f"({util.read_json_at(MANIFEST, 'homepage')})")
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with header:
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st.title(_('🧠GPT-Brain'))
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st.text(_('This is my personal AI powered brain feeding my own Obsidian notes. Ask anything.'))
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isEnglish = st.session_state['SESSION_LANGUAGE'] == 'en_US'
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message(_("This is a beta version. Please [🪲report bugs](") + util.read_json_at(MANIFEST, 'bugs') + _(
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") if you find any."))
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message(_('This version does not allow for inquiries in languages other than English.'), condition=not isEnglish)
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def execute_brain(q):
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# log question
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log(f'\n\n\n\n[{str(time.ctime())}] - QUESTION: {q}')
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if check_update.isUpdated():
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st.success(_('Building Brain...'))
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# if brain-info is updated
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brain.build(chunk_size)
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st.success(_('Brain rebuild!'))
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time.sleep(2)
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# thinking on answer
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with st.spinner(_('Thinking on Answer')):
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answer = brain.run_answer(q, question_model, temp, max_tokens, top_p, freq_panl, pres_panl,
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chunk_count=chunk_count)
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if util.contains(operations, _('question')):
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# displaying results
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st.header(_('💬Answer'))
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st.info(f'{answer}')
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time.sleep(1)
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log(answer, delimiter='ANSWER')
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# thinking on other outputs
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if len(operations_no_question) > 0:
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for i in range(len(operations_no_question)):
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prompt_path = prompt_dictionary[operations_no_question[i]]
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other_model = other_models[i]
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process_response(answer, other_model, prompt_path, param)
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# convert param to dictionary
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param_dict = vars(param)
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# write param to json
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for key in param_dict:
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value = param_dict[key]
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util.update_json(BRAIN_MEMO, key, value)
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# write operation to json
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util.update_json(BRAIN_MEMO, f'operations_{SESSION_LANG}', operations)
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# write question model to json
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util.update_json(BRAIN_MEMO, 'question_model', question_model)
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# write other models to json
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for i in range(len(operations_no_question)):
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util.update_json(BRAIN_MEMO, f'{operations_no_question[i]}_model', other_models[i])
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# main
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with body:
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question = st.text_area(_('Ask Brain: '))
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col1, col2, col3, col4 = st.columns(4)
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with col1:
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send = st.button(_('📩Send'))
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with col2:
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if os.path.exists(CURRENT_LOG_FILE):
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save_as()
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# execute brain calculation
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if not question == '' and send:
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execute_brain(question)
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