fix: tokens not display if brain is not build yet

GPT/query.py

@@ -6,6 +6,7 @@ import modules.utilities as util
 import modules.language as language
 import GPT
 import modules.INFO as INFO
+import streamlit_toolkit.tools as st_tools
 
 API_KEY = util.read_file(r'.user\API-KEYS.txt').strip()
 
@@ -65,12 +66,15 @@ def get_stream_prompt(query, prompt_file, isQuestion, info_file=None):
     openai.api_key = API_KEY
     if isQuestion:
         data = util.read_json(INFO.BRAIN_DATA)
-        result = GPT.gpt_tools.search_chunks(query, data, count=1)
-        my_info = util.read_file(info_file)
-        prompt = util.read_file(prompt_file)
-        prompt = prompt.replace('<<INFO>>', result[0]['content'])
-        prompt = prompt.replace('<<QS>>', query)
-        prompt = prompt.replace('<<MY-INFO>>', my_info)
+        if data:
+            result = GPT.gpt_tools.search_chunks(query, data, count=1)
+            my_info = util.read_file(info_file)
+            prompt = util.read_file(prompt_file)
+            prompt = prompt.replace('<<INFO>>', result[0]['content'])
+            prompt = prompt.replace('<<QS>>', query)
+            prompt = prompt.replace('<<MY-INFO>>', my_info)
+        else:
+            prompt = ''
     else:
         chunk = textwrap.wrap(query, 10000)[0]
         prompt = util.read_file(prompt_file).replace('<<DATA>>', chunk)

Seanium_Brain.py

@@ -156,8 +156,12 @@ with body:
             max_model_token = 4096
         else:
             max_model_token = 2048
-
-        st.markdown(f'Prompt token: `{st_tool.predict_token(query, prompt_core)}/{max_model_token}`')
+        tokens, isTokenZero = st_tool.predict_token(query, prompt_core)
+        token_panel = st.empty()
+        if isTokenZero:
+            token_panel.markdown('Prompt token: `Not Available`')
+        else:
+            token_panel.markdown(f'Prompt token: `{tokens}/{max_model_token}`')
         if send:
             st_tool.execute_brain(query,
                                   param,

modules/check_update.py

@@ -11,6 +11,19 @@ def compare_time(t1, t2):
     return t1 == t2
 
 
+def update_time():
+    if os.path.exists(file_path):
+        # get modification time of the file
+        mod_time = os.path.getmtime(file_path)
+
+        # convert the modification time to readable format
+        read_mod_time = time.ctime(mod_time)
+
+        util.write_file(read_mod_time, temp_file)
+    else:
+        raise FileNotFoundError(f'File: {file_path} does not exist.')
+
+
 def is_input_updated():
     if os.path.exists(file_path):
         # get modification time of the file

streamlit_toolkit/tools.py

@@ -19,13 +19,14 @@ SESSION_TIME = st.session_state['SESSION_TIME']
 CURRENT_LOG_FILE = f'{INFO.LOG_PATH}/log_{SESSION_TIME}.log'
 
 
-def predict_token(query: str, prompt_core: GPT.model.prompt_core) -> int:
+def predict_token(query: str, prompt_core: GPT.model.prompt_core) -> (int, bool):
     """predict how many tokens to generate"""
     llm = OpenAI()
-    token = llm.get_num_tokens(GPT.query.get_stream_prompt(query, prompt_file=prompt_core.question,
-                                                           isQuestion=True,
-                                                           info_file=prompt_core.my_info))
-    return token
+    prompt = GPT.query.get_stream_prompt(query, prompt_file=prompt_core.question,
+                                         isQuestion=True,
+                                         info_file=prompt_core.my_info)
+    token = llm.get_num_tokens(prompt)
+    return token, token == 0
 
 
 def create_log():
@@ -255,7 +256,8 @@ def process_response_stream(query, target_model, prompt_file: str, params: GPT.m
             break
         # error handling
         if choice['finish_reason'] == 'length':
-            st.warning("⚠️ " + _('Result cut off. max_tokens') + f' ({params.max_tokens}) ' + _('too small. Consider increasing max_tokens.'))
+            st.warning("⚠️ " + _('Result cut off. max_tokens') + f' ({params.max_tokens}) ' + _(
+                'too small. Consider increasing max_tokens.'))
             break
 
         if 'gpt-3.5-turbo' in target_model:
@@ -280,7 +282,7 @@ def rebuild_brain(chunk_size: int):
     for idx, chunk_num in GPT.query.build(chunk_size):
         progress_bar.progress((idx + 1) / chunk_num)
     msg.success(_('Brain Updated!'), icon="👍")
-    time.sleep(2)
+    time.sleep(1)
 
 
 def execute_brain(q, params: GPT.model.param,
@@ -318,7 +320,8 @@ def execute_brain(q, params: GPT.model.param,
                 break
             # error handling
             if choice['finish_reason'] == 'length':
-                st.warning("⚠️ " + _('Result cut off. max_tokens') + f' ({params.max_tokens}) ' + _('too small. Consider increasing max_tokens.'))
+                st.warning("⚠️ " + _('Result cut off. max_tokens') + f' ({params.max_tokens}) ' + _(
+                    'too small. Consider increasing max_tokens.'))
                 break
             if 'gpt-3.5-turbo' in model.question_model:
                 delta = choice['delta']