Merge pull request #10 from sean1832/patch-only-davinci-model-works

Patch #9.

Seanium_Brain.py

@@ -1,5 +1,4 @@
 import os
-import time
 
 import streamlit as st
 import streamlit_toggle as st_toggle
@@ -60,11 +59,16 @@ with st.sidebar:
                      help=_('What sampling temperature to use, between 0 and 1. Higher values like 0.8 will make the '
                             'output more random, while lower values like 0.2 will make it more focused and '
                             'deterministic. \n\nIt is generally recommend altering this or `top_p` but not both.'))
-    max_tokens = st.slider(_('Max Tokens'), 850, 4096, value=util.read_json_at(INFO.BRAIN_MEMO, 'max_tokens', 1000),
+    max_tokens = st.slider(_('Max Tokens'), 10, 4096, value=util.read_json_at(INFO.BRAIN_MEMO, 'max_tokens', 1000),
                            help=_("The maximum number of tokens to generate in the completion.\n\nThe token count of "
                                   "your prompt plus `max_tokens` cannot exceed the model's context length. Most "
                                   "models have a context length of 2048 tokens (except for the newest models, "
                                   "which support 4096)."))
+    chunk_size = st.slider(_('Chunk size'), 1500, 4500,
+                           value=util.read_json_at(INFO.BRAIN_MEMO, 'chunk_size', 4000),
+                           help=_("The number of tokens to consider at each step. The larger this is, the more "
+                                  "context the model has to work with, but the slower generation and expensive "
+                                  "will it be."))
 
     with st.expander(label=_('Advanced Options')):
         top_p = st.slider(_('Top_P'), 0.0, 1.0, value=util.read_json_at(INFO.BRAIN_MEMO, 'top_p', 1.0),
@@ -84,15 +88,9 @@ with st.sidebar:
                                      "new tokens based on their existing frequency in the text so far."
                                      "\n\n[See more information about frequency and presence penalties.]"
                                      "(https://platform.openai.com/docs/api-reference/parameter-details)"))
-
-        chunk_size = st.slider(_('Chunk size'), 1500, 4500,
-                               value=util.read_json_at(INFO.BRAIN_MEMO, 'chunk_size', 4000),
-                               help=_("The number of tokens to consider at each step. The larger this is, the more "
-                                      "context the model has to work with, but the slower generation and expensive "
-                                      "will it be."))
         enable_stream = st_toggle.st_toggle_switch(_('Stream (experimental)'),
                                                    default_value=util.read_json_at(INFO.BRAIN_MEMO, 'enable_stream',
-                                                                                   True))
+                                                                                   False))
 
         if not enable_stream:
             chunk_count = st.slider(_('Answer count'), 1, 5, value=util.read_json_at(INFO.BRAIN_MEMO, 'chunk_count', 1),

modules/check_update.py

@@ -1,6 +1,7 @@
 import os
 import time
 import modules.utilities as util
+import modules as mod
 
 file_path = r'.user\input.txt'
 temp_file = r'.user\input_last-run.temp'
@@ -10,7 +11,7 @@ def compare_time(t1, t2):
     return t1 == t2
 
 
-def isUpdated():
+def is_input_updated():
     if os.path.exists(file_path):
         # get modification time of the file
         mod_time = os.path.getmtime(file_path)
@@ -35,3 +36,9 @@ def isUpdated():
             return True
     else:
         raise FileNotFoundError(f'File: {file_path} does not exist.')
+
+
+def is_param_updated(param_val, param_infile_key):
+    infile_val = util.read_json_at(mod.INFO.BRAIN_MEMO, param_infile_key)
+    if infile_val != param_val:
+        return True

pages/1_Configs.py

@@ -14,10 +14,6 @@ PROMPT_PATH = f'{INFO.USER_DIR}/prompt/{SESSION_LANG}/'
 
 _ = language.set_language()
 
-# st.set_page_config(
-#     page_title='Configs'
-# )
-
 body = st.container()
 
 

streamlit_toolkit/tools.py

@@ -62,15 +62,7 @@ def save(content, path, page='', json_value: dict = None):
         if page == '💽Brain Memory':
             for key, value in json_value.items():
                 util.update_json(INFO.BRAIN_MEMO, key, value)
-            #
-            #
-            # util.update_json(INFO.BRAIN_MEMO, 'delimiter', json_value['delimiter'])
-            # util.update_json(INFO.BRAIN_MEMO, 'append_mode', json_value['append_mode'])
-            # util.update_json(INFO.BRAIN_MEMO, 'force_mode', json_value['force_mode'])
-            # util.update_json(INFO.BRAIN_MEMO, 'advanced_mode', json_value['advanced_mode'])
-            # util.update_json(INFO.BRAIN_MEMO, 'filter_info', json_value['filter_info'])
-            # util.update_json(INFO.BRAIN_MEMO, 'filter_row_count', json_value['filter_row_count'])
-            # util.update_json(INFO.BRAIN_MEMO, 'exclude_dir', json_value['exclude_dir'])
+
         time.sleep(1)
         # refresh page
         st.experimental_rerun()
@@ -270,7 +262,7 @@ def execute_brain(q, params: GPT.model.param,
     # log question
     log(f'\n\n\n\n[{str(time.ctime())}] - QUESTION: {q}')
 
-    if mod.check_update.isUpdated():
+    if mod.check_update.is_input_updated() or mod.check_update.is_param_updated(params.chunk_size, 'chunk_size'):
         msg = st.warning(_('Updating Brain...'), icon="⏳")
         progress_bar = st.progress(0)
         for idx, chunk_num in GPT.query.build(params.chunk_size):