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 | #characters: %d', prompt, len(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') if temp: temp.close() 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()