slide-deck-ai / chat_app.py
barunsaha's picture
Update chat history in prompts, segregate the prompts, add retry to HF API call, and update configs
e690364
import datetime
import logging
import pathlib
import random
import tempfile
from typing import List
import json5
import streamlit as st
from langchain_community.chat_message_histories import (
StreamlitChatMessageHistory
)
from langchain_core.messages import HumanMessage
from langchain_core.prompts import ChatPromptTemplate
from transformers import AutoTokenizer
from global_config import GlobalConfig
from helpers import llm_helper, pptx_helper
@st.cache_data
def _load_strings() -> dict:
"""
Load various strings to be displayed in the app.
:return: The dictionary of strings.
"""
with open(GlobalConfig.APP_STRINGS_FILE, 'r', encoding='utf-8') as in_file:
return json5.loads(in_file.read())
@st.cache_data
def _get_prompt_template(is_refinement: bool) -> str:
"""
Return a prompt template.
:param is_refinement: Whether this is the initial or refinement prompt.
:return: The prompt template as f-string.
"""
if is_refinement:
with open(GlobalConfig.REFINEMENT_PROMPT_TEMPLATE, 'r', encoding='utf-8') as in_file:
template = in_file.read()
else:
with open(GlobalConfig.INITIAL_PROMPT_TEMPLATE, 'r', encoding='utf-8') as in_file:
template = in_file.read()
return template
@st.cache_resource
def _get_tokenizer() -> AutoTokenizer:
"""
Get Mistral tokenizer for counting tokens.
:return: The tokenizer.
"""
return AutoTokenizer.from_pretrained(
pretrained_model_name_or_path=GlobalConfig.HF_LLM_MODEL_NAME
)
APP_TEXT = _load_strings()
# Session variables
CHAT_MESSAGES = 'chat_messages'
DOWNLOAD_FILE_KEY = 'download_file_name'
IS_IT_REFINEMENT = 'is_it_refinement'
logger = logging.getLogger(__name__)
progress_bar = st.progress(0, text='Setting up SlideDeck AI...')
texts = list(GlobalConfig.PPTX_TEMPLATE_FILES.keys())
captions = [GlobalConfig.PPTX_TEMPLATE_FILES[x]['caption'] for x in texts]
pptx_template = st.sidebar.radio(
'Select a presentation template:',
texts,
captions=captions,
horizontal=True
)
def display_page_header_content():
"""
Display content in the page header.
"""
st.title(APP_TEXT['app_name'])
st.subheader(APP_TEXT['caption'])
# st.markdown(
# '![Visitors](https://api.visitorbadge.io/api/visitors?path=https%3A%2F%2Fhuggingface.co%2Fspaces%2Fbarunsaha%2Fslide-deck-ai&countColor=%23263759)' # noqa: E501
# )
def display_page_footer_content():
"""
Display content in the page footer.
"""
st.text(APP_TEXT['tos'] + '\n\n' + APP_TEXT['tos2'])
def build_ui():
"""
Display the input elements for content generation.
"""
display_page_header_content()
with st.expander('Usage Policies and Limitations'):
display_page_footer_content()
progress_bar.progress(50, text='Setting up chat interface...')
set_up_chat_ui()
def set_up_chat_ui():
"""
Prepare the chat interface and related functionality.
"""
with st.expander('Usage Instructions'):
st.write(GlobalConfig.CHAT_USAGE_INSTRUCTIONS)
st.markdown(
'SlideDeck AI is powered by'
' [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)'
)
# view_messages = st.expander('View the messages in the session state')
st.chat_message('ai').write(
random.choice(APP_TEXT['ai_greetings'])
)
progress_bar.progress(100, text='Done!')
progress_bar.empty()
history = StreamlitChatMessageHistory(key=CHAT_MESSAGES)
if _is_it_refinement():
template = _get_prompt_template(is_refinement=True)
logger.debug('Getting refinement template')
else:
template = _get_prompt_template(is_refinement=False)
logger.debug('Getting initial template')
prompt_template = ChatPromptTemplate.from_template(template)
# Since Streamlit app reloads at every interaction, display the chat history
# from the save session state
for msg in history.messages:
msg_type = msg.type
if msg_type == 'user':
st.chat_message(msg_type).write(msg.content)
else:
st.chat_message(msg_type).code(msg.content, language='json')
if prompt := st.chat_input(
placeholder=APP_TEXT['chat_placeholder'],
max_chars=GlobalConfig.LLM_MODEL_MAX_INPUT_LENGTH
):
progress_bar_pptx = st.progress(0, 'Preparing to run...')
if not _is_valid_prompt(prompt):
return
logger.info('User input: %s', prompt)
st.chat_message('user').write(prompt)
user_messages = _get_user_messages()
user_messages.append(prompt)
list_of_msgs = [
f'{idx + 1}. {msg}' for idx, msg in enumerate(user_messages)
]
list_of_msgs = '\n'.join(list_of_msgs)
if _is_it_refinement():
formatted_template = prompt_template.format(
**{
'instructions': list_of_msgs,
'previous_content': _get_last_response()
}
)
else:
formatted_template = prompt_template.format(
**{
'question': prompt,
}
)
progress_bar_pptx.progress(5, 'Calling LLM...will retry if connection times out...')
response: dict = llm_helper.hf_api_query({
'inputs': formatted_template,
'parameters': {
'temperature': GlobalConfig.LLM_MODEL_TEMPERATURE,
'min_length': GlobalConfig.LLM_MODEL_MIN_OUTPUT_LENGTH,
'max_length': GlobalConfig.LLM_MODEL_MAX_OUTPUT_LENGTH,
'max_new_tokens': GlobalConfig.LLM_MODEL_MAX_OUTPUT_LENGTH,
'num_return_sequences': 1,
'return_full_text': False,
# "repetition_penalty": 0.0001
},
'options': {
'wait_for_model': True,
'use_cache': True
}
})
if len(response) > 0 and 'generated_text' in response[0]:
response: str = response[0]['generated_text'].strip()
st.chat_message('ai').code(response, language='json')
history.add_user_message(prompt)
history.add_ai_message(response)
if GlobalConfig.COUNT_TOKENS:
tokenizer = _get_tokenizer()
tokens_count_in = len(tokenizer.tokenize(formatted_template))
tokens_count_out = len(tokenizer.tokenize(response))
logger.debug(
'Tokens count:: input: %d, output: %d',
tokens_count_in, tokens_count_out
)
# _display_messages_history(view_messages)
# The content has been generated as JSON
# There maybe trailing ``` at the end of the response -- remove them
# To be careful: ``` may be part of the content as well when code is generated
progress_bar_pptx.progress(50, 'Analyzing response...')
response_cleaned = _clean_json(response)
# Now create the PPT file
progress_bar_pptx.progress(75, 'Creating the slide deck...give it a moment')
generate_slide_deck(response_cleaned)
progress_bar_pptx.progress(100, text='Done!')
def generate_slide_deck(json_str: str):
"""
Create a slide deck.
:param json_str: The content in *valid* JSON format.
"""
if DOWNLOAD_FILE_KEY in st.session_state:
path = pathlib.Path(st.session_state[DOWNLOAD_FILE_KEY])
logger.debug('DOWNLOAD_FILE_KEY found in session')
else:
temp = tempfile.NamedTemporaryFile(delete=False, suffix='.pptx')
path = pathlib.Path(temp.name)
st.session_state[DOWNLOAD_FILE_KEY] = str(path)
logger.debug('DOWNLOAD_FILE_KEY not found in session')
logger.debug('Creating PPTX file: %s...', st.session_state[DOWNLOAD_FILE_KEY])
try:
pptx_helper.generate_powerpoint_presentation(
json_str,
slides_template=pptx_template,
output_file_path=path
)
_display_download_button(path)
except ValueError as ve:
st.error(APP_TEXT['json_parsing_error'])
logger.error('%s', APP_TEXT['json_parsing_error'])
logger.error('Additional error info: %s', str(ve))
except Exception as ex:
st.error(APP_TEXT['content_generation_error'])
logger.error('Caught a generic exception: %s', str(ex))
def _is_valid_prompt(prompt: str) -> bool:
"""
Verify whether user input satisfies the concerned constraints.
:param prompt: The user input text.
:return: True if all criteria are satisfied; False otherwise.
"""
if len(prompt) < 5 or ' ' not in prompt:
st.error(
'Not enough information provided!'
' Please be a little more descriptive and type a few words with a few characters :)'
)
return False
return True
def _is_it_refinement() -> bool:
"""
Whether it is the initial prompt or a refinement.
:return: True if it is the initial prompt; False otherwise.
"""
if IS_IT_REFINEMENT in st.session_state:
return True
if len(st.session_state[CHAT_MESSAGES]) >= 2:
# Prepare for the next call
st.session_state[IS_IT_REFINEMENT] = True
return True
return False
def _get_user_messages() -> List[str]:
"""
Get a list of user messages submitted until now from the session state.
:return: The list of user messages.
"""
return [
msg.content for msg in st.session_state[CHAT_MESSAGES] if isinstance(msg, HumanMessage)
]
def _get_last_response() -> str:
"""
Get the last response generated by AI.
:return: The response text.
"""
return st.session_state[CHAT_MESSAGES][-1].content
def _display_messages_history(view_messages: st.expander):
"""
Display the history of messages.
:param view_messages: The list of AI and Human messages.
"""
with view_messages:
view_messages.json(st.session_state[CHAT_MESSAGES])
def _clean_json(json_str: str) -> str:
"""
Attempt to clean a JSON response string from the LLM by removing the trailing ```
and any text beyond that. May not be always accurate.
:param json_str: The input string in JSON format.
:return: The "cleaned" JSON string.
"""
str_len = len(json_str)
response_cleaned = json_str
try:
idx = json_str.rindex('```')
logger.debug(
'Fixing JSON response: str_len: %d, idx of ```: %d',
str_len, idx
)
if idx + 3 == str_len:
# The response ends with ``` -- most likely the end of JSON response string
response_cleaned = json_str[:idx]
elif idx + 3 < str_len:
# Looks like there are some more content beyond the last ```
# In the best case, it would be some additional plain-text response from the LLM
# and is unlikely to contain } or ] that are present in JSON
if '}' not in json_str[idx + 3:]: # the remainder of the text
response_cleaned = json_str[:idx]
except ValueError:
# No ``` found
pass
return response_cleaned
def _display_download_button(file_path: pathlib.Path):
"""
Display a download button to download a slide deck.
:param file_path: The path of the .pptx file.
"""
with open(file_path, 'rb') as download_file:
st.download_button(
'Download PPTX file ⬇️',
data=download_file,
file_name='Presentation.pptx',
key=datetime.datetime.now()
)
def main():
"""
Trigger application run.
"""
build_ui()
if __name__ == '__main__':
main()