mirror of https://github.com/sean1832/GPT-Brain
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123 lines
3.9 KiB
Python
123 lines
3.9 KiB
Python
import streamlit as st
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from modules import utilities as util
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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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st.set_page_config(
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page_title='Seanium Brain'
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)
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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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CURRENT_LOG_FILE = f'{LOG_PATH}/log_{SESSION_TIME}.log'
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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, path=LOG_PATH, seperater_text=''):
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log_file = create_log()
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if seperater_text != '':
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seperater_text = f'\n\n=============={seperater_text}==============\n'
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util.write_file(f'\n{seperater_text + 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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bytes = f.read()
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st.download_button(
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label="📥download log",
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data=bytes,
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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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# sidebar
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with st.sidebar:
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st.title('Settings')
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output_types = st.multiselect('Output Types',['Answer', 'Summary'],default=['Answer'])
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answer_model = st.selectbox('Answer Model', model_options)
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if util.contains(output_types, 'Summary'):
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summary_model = st.selectbox('Summary Model', model_options)
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temp = st.slider('Temperature', 0.0, 1.0, value=0.1)
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max_tokens = st.slider('Max Tokens', 850, 2500, value=1000)
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top_p = st.slider('Top_P', 0.0, 1.0, value=1.0)
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freq_panl = st.slider('Frequency penalty', 0.0, 1.0, value=0.0)
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pres_panl = st.slider('Presence penalty', 0.0, 1.0, value=0.0)
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chunk_size = st.slider('Chunk Size', 1500, 4500, value=4000)
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chunk_count = st.slider('Answer Count', 1, 5, value=1)
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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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with header:
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st.title('🧠Seanium 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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def execute_brain(question):
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# log question
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log(f'\n\n\n\n[{str(time.ctime())}] - QUESTION: {question}')
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if check_update.isUpdated():
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# if brain-info is updated
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brain.build(chunk_size)
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st.success('Brain rebuilded!')
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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(question, answer_model, temp, max_tokens, top_p, freq_panl, pres_panl, chunk_count=chunk_count)
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if util.contains(output_types, 'Answer'):
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# displaying results
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st.header('💬Answer')
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st.success(answer)
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log(answer, seperater_text='ANSWER')
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# thinking on summary
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if util.contains(output_types, 'Summary'):
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with st.spinner('Thinking on Summary'):
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time.sleep(2)
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summary = brain.run_summary(answer, summary_model, temp, max_tokens, top_p, freq_panl, pres_panl)
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# displaying results
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st.header('📃Summary')
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st.success(summary)
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log(summary, seperater_text='SUMMARY')
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# main
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with body:
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question = st.text_input('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) |