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LlamaWhisperer / backup2.app.py

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	# Imports
	import base64
	import glob
	import json
	import math
	import openai
	import os
	import pytz
	import re
	import requests
	import streamlit as st
	import textract
	import time
	import zipfile
	import huggingface_hub
	import dotenv
	from audio_recorder_streamlit import audio_recorder
	from bs4 import BeautifulSoup
	from collections import deque
	from datetime import datetime
	from dotenv import load_dotenv
	from huggingface_hub import InferenceClient
	from io import BytesIO
	from langchain.chat_models import ChatOpenAI
	from langchain.chains import ConversationalRetrievalChain
	from langchain.embeddings import OpenAIEmbeddings
	from langchain.memory import ConversationBufferMemory
	from langchain.text_splitter import CharacterTextSplitter
	from langchain.vectorstores import FAISS
	from openai import ChatCompletion
	from PyPDF2 import PdfReader
	from templates import bot_template, css, user_template
	from xml.etree import ElementTree as ET
	import streamlit.components.v1 as components # Import Streamlit Components for HTML5


	st.set_page_config(page_title="🐪Llama Whisperer🦙 Voice Chat🌟", layout="wide")


	def add_Med_Licensing_Exam_Dataset():
	import streamlit as st
	from datasets import load_dataset
	dataset = load_dataset("augtoma/usmle_step_1")['test'] # Using 'test' split
	st.title("USMLE Step 1 Dataset Viewer")
	if len(dataset) == 0:
	st.write("😢 The dataset is empty.")
	else:
	st.write("""
	🔍 Use the search box to filter questions or use the grid to scroll through the dataset.
	""")

	# 👩‍🔬 Search Box
	search_term = st.text_input("Search for a specific question:", "")

	# 🎛 Pagination
	records_per_page = 100
	num_records = len(dataset)
	num_pages = max(int(num_records / records_per_page), 1)

	# Skip generating the slider if num_pages is 1 (i.e., all records fit in one page)
	if num_pages > 1:
	page_number = st.select_slider("Select page:", options=list(range(1, num_pages + 1)))
	else:
	page_number = 1 # Only one page

	# 📊 Display Data
	start_idx = (page_number - 1) * records_per_page
	end_idx = start_idx + records_per_page

	# 🧪 Apply the Search Filter
	filtered_data = []
	for record in dataset[start_idx:end_idx]:
	if isinstance(record, dict) and 'text' in record and 'id' in record:
	if search_term:
	if search_term.lower() in record['text'].lower():
	st.markdown(record)
	filtered_data.append(record)
	else:
	filtered_data.append(record)

	# 🌐 Render the Grid
	for record in filtered_data:
	st.write(f"## Question ID: {record['id']}")
	st.write(f"### Question:")
	st.write(f"{record['text']}")
	st.write(f"### Answer:")
	st.write(f"{record['answer']}")
	st.write("---")

	st.write(f"😊 Total Records: {num_records} \| 📄 Displaying {start_idx+1} to {min(end_idx, num_records)}")

	# 1. Constants and Top Level UI Variables

	# My Inference API Copy
	# API_URL = 'https://qe55p8afio98s0u3.us-east-1.aws.endpoints.huggingface.cloud' # Dr Llama
	# Original:
	API_URL = "https://api-inference.huggingface.co/models/meta-llama/Llama-2-7b-chat-hf"
	API_KEY = os.getenv('API_KEY')
	MODEL1="meta-llama/Llama-2-7b-chat-hf"
	MODEL1URL="https://huggingface.co/meta-llama/Llama-2-7b-chat-hf"
	HF_KEY = os.getenv('HF_KEY')
	headers = {
	"Authorization": f"Bearer {HF_KEY}",
	"Content-Type": "application/json"
	}
	key = os.getenv('OPENAI_API_KEY')
	prompt = f"Write instructions to teach discharge planning along with guidelines and patient education. List entities, features and relationships to CCDA and FHIR objects in boldface."
	should_save = st.sidebar.checkbox("💾 Save", value=True, help="Save your session data.")

	# 2. Prompt label button demo for LLM
	def add_witty_humor_buttons():
	with st.expander("Wit and Humor 🤣", expanded=True):
	# Tip about the Dromedary family
	st.markdown("🔬 Fun Fact: Dromedaries, part of the camel family, have a single hump and are adapted to arid environments. Their 'superpowers' include the ability to survive without water for up to 7 days, thanks to their specialized blood cells and water storage in their hump.")

	# Define button descriptions
	descriptions = {
	"Generate Limericks 😂": "Write ten random adult limericks based on quotes that are tweet length and make you laugh 🎭",
	"Wise Quotes 🧙": "Generate ten wise quotes that are tweet length 🦉",
	"Funny Rhymes 🎤": "Create ten funny rhymes that are tweet length 🎶",
	"Medical Jokes 💉": "Create ten medical jokes that are tweet length 🏥",
	"Minnesota Humor ❄️": "Create ten jokes about Minnesota that are tweet length 🌨️",
	"Top Funny Stories 📖": "Create ten funny stories that are tweet length 📚",
	"More Funny Rhymes 🎙️": "Create ten more funny rhymes that are tweet length 🎵"
	}

	# Create columns
	col1, col2, col3 = st.columns([1, 1, 1], gap="small")

	# Add buttons to columns
	if col1.button("Wise Limericks 😂"):
	StreamLLMChatResponse(descriptions["Generate Limericks 😂"])

	if col2.button("Wise Quotes 🧙"):
	StreamLLMChatResponse(descriptions["Wise Quotes 🧙"])

	#if col3.button("Funny Rhymes 🎤"):
	# StreamLLMChatResponse(descriptions["Funny Rhymes 🎤"])

	col4, col5, col6 = st.columns([1, 1, 1], gap="small")

	if col4.button("Top Ten Funniest Clean Jokes 💉"):
	StreamLLMChatResponse(descriptions["Top Ten Funniest Clean Jokes 💉"])

	if col5.button("Minnesota Humor ❄️"):
	StreamLLMChatResponse(descriptions["Minnesota Humor ❄️"])

	if col6.button("Origins of Medical Science True Stories"):
	StreamLLMChatResponse(descriptions["Origins of Medical Science True Stories"])

	col7 = st.columns(1, gap="small")

	if col7[0].button("Top Ten Best Write a streamlit python program prompts to build AI programs. 🎙️"):
	StreamLLMChatResponse(descriptions["Top Ten Best Write a streamlit python program prompts to build AI programs. 🎙️"])

	def SpeechSynthesis(result):
	documentHTML5='''
	<!DOCTYPE html>
	<html>
	<head>
	<title>Read It Aloud</title>
	<script type="text/javascript">
	function readAloud() {
	const text = document.getElementById("textArea").value;
	const speech = new SpeechSynthesisUtterance(text);
	window.speechSynthesis.speak(speech);
	}
	</script>
	</head>
	<body>
	<h1>🔊 Read It Aloud</h1>
	<textarea id="textArea" rows="10" cols="80">
	'''
	documentHTML5 = documentHTML5 + result
	documentHTML5 = documentHTML5 + '''
	</textarea>
	<br>
	<button onclick="readAloud()">🔊 Read Aloud</button>
	</body>
	</html>
	'''

	components.html(documentHTML5, width=1280, height=1024)
	#return result


	# 3. Stream Llama Response
	# @st.cache_resource
	def StreamLLMChatResponse(prompt):
	try:
	endpoint_url = API_URL
	hf_token = API_KEY
	client = InferenceClient(endpoint_url, token=hf_token)
	gen_kwargs = dict(
	max_new_tokens=512,
	top_k=30,
	top_p=0.9,
	temperature=0.2,
	repetition_penalty=1.02,
	stop_sequences=["\nUser:", "<\|endoftext\|>", "</s>"],
	)
	stream = client.text_generation(prompt, stream=True, details=True, **gen_kwargs)
	report=[]
	res_box = st.empty()
	collected_chunks=[]
	collected_messages=[]
	allresults=''
	for r in stream:
	if r.token.special:
	continue
	if r.token.text in gen_kwargs["stop_sequences"]:
	break
	collected_chunks.append(r.token.text)
	chunk_message = r.token.text
	collected_messages.append(chunk_message)
	try:
	report.append(r.token.text)
	if len(r.token.text) > 0:
	result="".join(report).strip()
	res_box.markdown(f'{result}')

	except:
	st.write('Stream llm issue')
	SpeechSynthesis(result)
	return result
	except:
	st.write('Llama model is asleep. Starting up now on A10 - please give 5 minutes then retry as KEDA scales up from zero to activate running container(s).')

	# 4. Run query with payload
	def query(payload):
	response = requests.post(API_URL, headers=headers, json=payload)
	st.markdown(response.json())
	return response.json()
	def get_output(prompt):
	return query({"inputs": prompt})

	# 5. Auto name generated output files from time and content
	def generate_filename(prompt, file_type):
	central = pytz.timezone('US/Central')
	safe_date_time = datetime.now(central).strftime("%m%d_%H%M")
	replaced_prompt = prompt.replace(" ", "_").replace("\n", "_")
	safe_prompt = "".join(x for x in replaced_prompt if x.isalnum() or x == "_")[:255] # 255 is linux max, 260 is windows max
	#safe_prompt = "".join(x for x in replaced_prompt if x.isalnum() or x == "_")[:45]
	return f"{safe_date_time}_{safe_prompt}.{file_type}"

	# 6. Speech transcription via OpenAI service
	def transcribe_audio(openai_key, file_path, model):
	openai.api_key = openai_key
	OPENAI_API_URL = "https://api.openai.com/v1/audio/transcriptions"
	headers = {
	"Authorization": f"Bearer {openai_key}",
	}
	with open(file_path, 'rb') as f:
	data = {'file': f}
	response = requests.post(OPENAI_API_URL, headers=headers, files=data, data={'model': model})
	if response.status_code == 200:
	st.write(response.json())
	chatResponse = chat_with_model(response.json().get('text'), '') # *************************************
	transcript = response.json().get('text')
	filename = generate_filename(transcript, 'txt')
	response = chatResponse
	user_prompt = transcript
	create_file(filename, user_prompt, response, should_save)
	return transcript
	else:
	st.write(response.json())
	st.error("Error in API call.")
	return None

	# 7. Auto stop on silence audio control for recording WAV files
	def save_and_play_audio(audio_recorder):
	audio_bytes = audio_recorder(key='audio_recorder')
	if audio_bytes:
	filename = generate_filename("Recording", "wav")
	with open(filename, 'wb') as f:
	f.write(audio_bytes)
	st.audio(audio_bytes, format="audio/wav")
	return filename
	return None

	# 8. File creator that interprets type and creates output file for text, markdown and code
	def create_file(filename, prompt, response, should_save=True):
	if not should_save:
	return
	base_filename, ext = os.path.splitext(filename)
	if ext in ['.txt', '.htm', '.md']:
	with open(f"{base_filename}.md", 'w') as file:
	try:
	content = prompt.strip() + '\r\n' + response
	file.write(content)
	except:
	st.write('.')

	#has_python_code = re.search(r"```python([\s\S]*?)```", prompt.strip() + '\r\n' + response)
	#has_python_code = bool(re.search(r"```python([\s\S]*?)```", prompt.strip() + '\r\n' + response))
	#if has_python_code:
	# python_code = re.findall(r"```python([\s\S]*?)```", response)[0].strip()
	# with open(f"{base_filename}-Code.py", 'w') as file:
	# file.write(python_code)
	# with open(f"{base_filename}.md", 'w') as file:
	# content = prompt.strip() + '\r\n' + response
	# file.write(content)

	def truncate_document(document, length):
	return document[:length]
	def divide_document(document, max_length):
	return [document[i:i+max_length] for i in range(0, len(document), max_length)]

	# 9. Sidebar with UI controls to review and re-run prompts and continue responses
	@st.cache_resource
	def get_table_download_link(file_path):
	with open(file_path, 'r') as file:
	data = file.read()

	b64 = base64.b64encode(data.encode()).decode()
	file_name = os.path.basename(file_path)
	ext = os.path.splitext(file_name)[1] # get the file extension
	if ext == '.txt':
	mime_type = 'text/plain'
	elif ext == '.py':
	mime_type = 'text/plain'
	elif ext == '.xlsx':
	mime_type = 'text/plain'
	elif ext == '.csv':
	mime_type = 'text/plain'
	elif ext == '.htm':
	mime_type = 'text/html'
	elif ext == '.md':
	mime_type = 'text/markdown'
	elif ext == '.wav':
	mime_type = 'audio/wav'
	else:
	mime_type = 'application/octet-stream' # general binary data type
	href = f'<a href="data:{mime_type};base64,{b64}" target="_blank" download="{file_name}">{file_name}</a>'
	return href


	def CompressXML(xml_text):
	root = ET.fromstring(xml_text)
	for elem in list(root.iter()):
	if isinstance(elem.tag, str) and 'Comment' in elem.tag:
	elem.parent.remove(elem)
	return ET.tostring(root, encoding='unicode', method="xml")

	# 10. Read in and provide UI for past files
	@st.cache_resource
	def read_file_content(file,max_length):
	if file.type == "application/json":
	content = json.load(file)
	return str(content)
	elif file.type == "text/html" or file.type == "text/htm":
	content = BeautifulSoup(file, "html.parser")
	return content.text
	elif file.type == "application/xml" or file.type == "text/xml":
	tree = ET.parse(file)
	root = tree.getroot()
	xml = CompressXML(ET.tostring(root, encoding='unicode'))
	return xml
	elif file.type == "text/markdown" or file.type == "text/md":
	md = mistune.create_markdown()
	content = md(file.read().decode())
	return content
	elif file.type == "text/plain":
	return file.getvalue().decode()
	else:
	return ""

	# 11. Chat with GPT - Caution on quota - now favoring fastest AI pipeline STT Whisper->LLM Llama->TTS
	@st.cache_resource
	def chat_with_model(prompt, document_section, model_choice='gpt-3.5-turbo'):
	model = model_choice
	conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}]
	conversation.append({'role': 'user', 'content': prompt})
	if len(document_section)>0:
	conversation.append({'role': 'assistant', 'content': document_section})
	start_time = time.time()
	report = []
	res_box = st.empty()
	collected_chunks = []
	collected_messages = []
	for chunk in openai.ChatCompletion.create(model='gpt-3.5-turbo', messages=conversation, temperature=0.5, stream=True):
	collected_chunks.append(chunk)
	chunk_message = chunk['choices'][0]['delta']
	collected_messages.append(chunk_message)
	content=chunk["choices"][0].get("delta",{}).get("content")
	try:
	report.append(content)
	if len(content) > 0:
	result = "".join(report).strip()
	res_box.markdown(f'{result}')
	except:
	st.write(' ')
	full_reply_content = ''.join([m.get('content', '') for m in collected_messages])
	st.write("Elapsed time:")
	st.write(time.time() - start_time)
	return full_reply_content

	# 12. Embedding VectorDB for LLM query of documents to text to compress inputs and prompt together as Chat memory using Langchain
	@st.cache_resource
	def chat_with_file_contents(prompt, file_content, model_choice='gpt-3.5-turbo'):
	conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}]
	conversation.append({'role': 'user', 'content': prompt})
	if len(file_content)>0:
	conversation.append({'role': 'assistant', 'content': file_content})
	response = openai.ChatCompletion.create(model=model_choice, messages=conversation)
	return response['choices'][0]['message']['content']

	def extract_mime_type(file):
	if isinstance(file, str):
	pattern = r"type='(.*?)'"
	match = re.search(pattern, file)
	if match:
	return match.group(1)
	else:
	raise ValueError(f"Unable to extract MIME type from {file}")
	elif isinstance(file, streamlit.UploadedFile):
	return file.type
	else:
	raise TypeError("Input should be a string or a streamlit.UploadedFile object")

	def extract_file_extension(file):
	# get the file name directly from the UploadedFile object
	file_name = file.name
	pattern = r".?\.(.?)$"
	match = re.search(pattern, file_name)
	if match:
	return match.group(1)
	else:
	raise ValueError(f"Unable to extract file extension from {file_name}")

	# Normalize input as text from PDF and other formats
	@st.cache_resource
	def pdf2txt(docs):
	text = ""
	for file in docs:
	file_extension = extract_file_extension(file)
	st.write(f"File type extension: {file_extension}")
	if file_extension.lower() in ['py', 'txt', 'html', 'htm', 'xml', 'json']:
	text += file.getvalue().decode('utf-8')
	elif file_extension.lower() == 'pdf':
	from PyPDF2 import PdfReader
	pdf = PdfReader(BytesIO(file.getvalue()))
	for page in range(len(pdf.pages)):
	text += pdf.pages[page].extract_text() # new PyPDF2 syntax
	return text

	def txt2chunks(text):
	text_splitter = CharacterTextSplitter(separator="\n", chunk_size=1000, chunk_overlap=200, length_function=len)
	return text_splitter.split_text(text)

	# Vector Store using FAISS
	@st.cache_resource
	def vector_store(text_chunks):
	embeddings = OpenAIEmbeddings(openai_api_key=key)
	return FAISS.from_texts(texts=text_chunks, embedding=embeddings)

	# Memory and Retrieval chains
	@st.cache_resource
	def get_chain(vectorstore):
	llm = ChatOpenAI()
	memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
	return ConversationalRetrievalChain.from_llm(llm=llm, retriever=vectorstore.as_retriever(), memory=memory)

	def process_user_input(user_question):
	response = st.session_state.conversation({'question': user_question})
	st.session_state.chat_history = response['chat_history']
	for i, message in enumerate(st.session_state.chat_history):
	template = user_template if i % 2 == 0 else bot_template
	st.write(template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
	filename = generate_filename(user_question, 'txt')
	response = message.content
	user_prompt = user_question
	create_file(filename, user_prompt, response, should_save)

	def divide_prompt(prompt, max_length):
	words = prompt.split()
	chunks = []
	current_chunk = []
	current_length = 0
	for word in words:
	if len(word) + current_length <= max_length:
	current_length += len(word) + 1
	current_chunk.append(word)
	else:
	chunks.append(' '.join(current_chunk))
	current_chunk = [word]
	current_length = len(word)
	chunks.append(' '.join(current_chunk))
	return chunks


	# 13. Provide way of saving all and deleting all to give way of reviewing output and saving locally before clearing it

	@st.cache_resource
	def create_zip_of_files(files):
	zip_name = "all_files.zip"
	with zipfile.ZipFile(zip_name, 'w') as zipf:
	for file in files:
	zipf.write(file)
	return zip_name

	@st.cache_resource
	def get_zip_download_link(zip_file):
	with open(zip_file, 'rb') as f:
	data = f.read()
	b64 = base64.b64encode(data).decode()
	href = f'<a href="data:application/zip;base64,{b64}" download="{zip_file}">Download All</a>'
	return href

	# 14. Inference Endpoints for Whisper (best fastest STT) on NVIDIA T4 and Llama (best fastest AGI LLM) on NVIDIA A10
	# My Inference Endpoint
	API_URL_IE = f'https://tonpixzfvq3791u9.us-east-1.aws.endpoints.huggingface.cloud'
	# Original
	API_URL_IE = "https://api-inference.huggingface.co/models/openai/whisper-small.en"
	MODEL2 = "openai/whisper-small.en"
	MODEL2_URL = "https://huggingface.co/openai/whisper-small.en"
	#headers = {
	# "Authorization": "Bearer XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX",
	# "Content-Type": "audio/wav"
	#}
	# HF_KEY = os.getenv('HF_KEY')
	HF_KEY = st.secrets['HF_KEY']
	headers = {
	"Authorization": f"Bearer {HF_KEY}",
	"Content-Type": "audio/wav"
	}

	#@st.cache_resource
	def query(filename):
	with open(filename, "rb") as f:
	data = f.read()
	response = requests.post(API_URL_IE, headers=headers, data=data)
	return response.json()

	def generate_filename(prompt, file_type):
	central = pytz.timezone('US/Central')
	safe_date_time = datetime.now(central).strftime("%m%d_%H%M")
	replaced_prompt = prompt.replace(" ", "_").replace("\n", "_")
	safe_prompt = "".join(x for x in replaced_prompt if x.isalnum() or x == "_")[:90]
	return f"{safe_date_time}_{safe_prompt}.{file_type}"

	# 15. Audio recorder to Wav file
	def save_and_play_audio(audio_recorder):
	audio_bytes = audio_recorder()
	if audio_bytes:
	filename = generate_filename("Recording", "wav")
	with open(filename, 'wb') as f:
	f.write(audio_bytes)
	st.audio(audio_bytes, format="audio/wav")
	return filename

	# 16. Speech transcription to file output
	def transcribe_audio(filename):
	output = query(filename)
	return output

	def whisper_main():
	#st.title("Speech to Text")
	#st.write("Record your speech and get the text.")

	# Audio, transcribe, GPT:
	filename = save_and_play_audio(audio_recorder)
	if filename is not None:
	transcription = transcribe_audio(filename)
	try:
	transcript = transcription['text']
	st.write(transcript)
	response = StreamLLMChatResponse(transcript)
	filename_txt = generate_filename(transcript, ".txt")
	create_file(filename_txt, transcript, response, should_save)
	filename_wav = filename_txt.replace('.txt', '.wav')
	import shutil
	shutil.copyfile(filename, filename_wav)
	if os.path.exists(filename):
	os.remove(filename)
	except:
	st.write('Starting Whisper Model on GPU. Please retry in 30 seconds.')


	import streamlit as st

	# Sample function to demonstrate a response, replace with your own logic
	def StreamMedChatResponse(topic):
	st.write(f"Showing resources or questions related to: {topic}")

	def add_medical_exam_buttons():
	with st.expander("Medical Licensing Exam Topics 📚", expanded=False):
	st.markdown("🩺 Important: This section provides a variety of medical topics that are often encountered in medical licensing exams.")

	# Define medical exam terminology descriptions
	descriptions = {
	"Ultrasound with Doppler 🌊": "3 Questions and Answers with emojis about Doppler Ultrasound imaging techniques 🎥",
	"Oseltamivir 🦠": "3 Questions and Answers with emojis about the antiviral medication Oseltamivir 💊",
	"IM Epinephrine 💉": "3 Questions and Answers with emojis about intramuscular administration of epinephrine 💪",
	"Hypokalemia 🍌": "3 Questions and Answers with emojis about low potassium levels in blood 🩸",
	"Succinylcholine 💊": "3 Questions and Answers with emojis on the use and side-effects of Succinylcholine 🚑",
	"Phosphoinositol System 🧬": "3 Questions and Answers with emojis about the Phosphoinositol signalling system 🛠",
	"Ramipril 💊": "3 Questions and Answers with emojis related to the ACE inhibitor Ramipril 🩺"
	}

	# Create columns
	col1, col2, col3, col4 = st.columns([1, 1, 1, 1], gap="small")

	# Add buttons to columns
	if col1.button("Ultrasound with Doppler 🌊"):
	StreamLLMChatResponse(descriptions["Ultrasound with Doppler 🌊"])

	if col2.button("Oseltamivir 🦠"):
	StreamLLMChatResponse(descriptions["Oseltamivir 🦠"])

	if col3.button("IM Epinephrine 💉"):
	StreamLLMChatResponse(descriptions["IM Epinephrine 💉"])

	if col4.button("Hypokalemia 🍌"):
	StreamLLMChatResponse(descriptions["Hypokalemia 🍌"])

	col5, col6, col7, col8 = st.columns([1, 1, 1, 1], gap="small")

	if col5.button("Succinylcholine 💊"):
	StreamLLMChatResponse(descriptions["Succinylcholine 💊"])

	if col6.button("Phosphoinositol System 🧬"):
	StreamLLMChatResponse(descriptions["Phosphoinositol System 🧬"])

	if col7.button("Ramipril 💊"):
	StreamLLMChatResponse(descriptions["Ramipril 💊"])



	# 17. Main
	def main():

	#st.title("GAIA - Medical License Exam Testing")
	prompt = f"Write ten funny jokes that are tweet length stories that make you laugh. Show as markdown outline with emojis for each."

	# Add Wit and Humor buttons
	# add_witty_humor_buttons()
	add_medical_exam_buttons()


	with st.expander("Prompts 📚", expanded=False):

	example_input = st.text_input("Enter your prompt text for Llama:", value=prompt, help="Enter text to get a response from DromeLlama.")
	if st.button("Run Prompt With Llama model", help="Click to run the prompt."):
	try:
	response=StreamLLMChatResponse(example_input)
	create_file(filename, example_input, response, should_save)
	except:
	st.write('Llama model is asleep. Starting now on A10 GPU. Please wait one minute then retry. KEDA triggered.')

	openai.api_key = os.getenv('OPENAI_API_KEY')
	if openai.api_key == None: openai.api_key = st.secrets['OPENAI_API_KEY']

	menu = ["txt", "htm", "xlsx", "csv", "md", "py"]
	choice = st.sidebar.selectbox("Output File Type:", menu)

	model_choice = st.sidebar.radio("Select Model:", ('gpt-3.5-turbo', 'gpt-3.5-turbo-0301'))

	user_prompt = st.text_area("Enter prompts, instructions & questions:", '', height=100)
	collength, colupload = st.columns([2,3]) # adjust the ratio as needed
	with collength:
	max_length = st.slider("File section length for large files", min_value=1000, max_value=128000, value=12000, step=1000)
	with colupload:
	uploaded_file = st.file_uploader("Add a file for context:", type=["pdf", "xml", "json", "xlsx", "csv", "html", "htm", "md", "txt"])
	document_sections = deque()
	document_responses = {}
	if uploaded_file is not None:
	file_content = read_file_content(uploaded_file, max_length)
	document_sections.extend(divide_document(file_content, max_length))
	if len(document_sections) > 0:
	if st.button("👁️ View Upload"):
	st.markdown("Sections of the uploaded file:")
	for i, section in enumerate(list(document_sections)):
	st.markdown(f"Section {i+1}\n{section}")
	st.markdown("Chat with the model:")
	for i, section in enumerate(list(document_sections)):
	if i in document_responses:
	st.markdown(f"Section {i+1}\n{document_responses[i]}")
	else:
	if st.button(f"Chat about Section {i+1}"):
	st.write('Reasoning with your inputs...')
	#response = chat_with_model(user_prompt, section, model_choice)
	st.write('Response:')
	st.write(response)
	document_responses[i] = response
	filename = generate_filename(f"{user_prompt}_section_{i+1}", choice)
	create_file(filename, user_prompt, response, should_save)
	st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
	if st.button('💬 Chat'):
	st.write('Reasoning with your inputs...')
	user_prompt_sections = divide_prompt(user_prompt, max_length)
	full_response = ''
	for prompt_section in user_prompt_sections:
	response = chat_with_model(prompt_section, ''.join(list(document_sections)), model_choice)
	full_response += response + '\n' # Combine the responses
	response = full_response
	st.write('Response:')
	st.write(response)
	filename = generate_filename(user_prompt, choice)
	create_file(filename, user_prompt, response, should_save)
	#st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)

	# Compose a file sidebar of markdown md files:
	all_files = glob.glob("*.md")
	all_files = [file for file in all_files if len(os.path.splitext(file)[0]) >= 10] # exclude files with short names
	all_files.sort(key=lambda x: (os.path.splitext(x)[1], x), reverse=True) # sort by file type and file name in descending order
	if st.sidebar.button("🗑 Delete All Text"):
	for file in all_files:
	os.remove(file)
	st.experimental_rerun()
	if st.sidebar.button("⬇️ Download All"):
	zip_file = create_zip_of_files(all_files)
	st.sidebar.markdown(get_zip_download_link(zip_file), unsafe_allow_html=True)
	file_contents=''
	next_action=''
	for file in all_files:
	col1, col2, col3, col4, col5 = st.sidebar.columns([1,6,1,1,1]) # adjust the ratio as needed
	with col1:
	if st.button("🌐", key="md_"+file): # md emoji button
	with open(file, 'r') as f:
	file_contents = f.read()
	next_action='md'
	with col2:
	st.markdown(get_table_download_link(file), unsafe_allow_html=True)
	with col3:
	if st.button("📂", key="open_"+file): # open emoji button
	with open(file, 'r') as f:
	file_contents = f.read()
	next_action='open'
	with col4:
	if st.button("🔍", key="read_"+file): # search emoji button
	with open(file, 'r') as f:
	file_contents = f.read()
	next_action='search'
	with col5:
	if st.button("🗑", key="delete_"+file):
	os.remove(file)
	st.experimental_rerun()


	if len(file_contents) > 0:
	if next_action=='open':
	file_content_area = st.text_area("File Contents:", file_contents, height=500)
	if next_action=='md':
	st.markdown(file_contents)
	if next_action=='search':
	file_content_area = st.text_area("File Contents:", file_contents, height=500)
	st.write('Reasoning with your inputs...')

	# new - llama
	response = StreamLLMChatResponse(file_contents)
	filename = generate_filename(user_prompt, ".md")
	create_file(filename, file_contents, response, should_save)
	SpeechSynthesis(response)

	# old - gpt
	#response = chat_with_model(user_prompt, file_contents, model_choice)
	#filename = generate_filename(file_contents, choice)
	#create_file(filename, user_prompt, response, should_save)

	st.experimental_rerun()


	# Function to encode file to base64
	def get_base64_encoded_file(file_path):
	with open(file_path, "rb") as file:
	return base64.b64encode(file.read()).decode()

	# Function to create a download link
	def get_audio_download_link(file_path):
	base64_file = get_base64_encoded_file(file_path)
	return f'<a href="data:file/wav;base64,{base64_file}" download="{os.path.basename(file_path)}">⬇️ Download Audio</a>'

	# Compose a file sidebar of past encounters
	all_files = glob.glob("*.wav")
	all_files = [file for file in all_files if len(os.path.splitext(file)[0]) >= 10] # exclude files with short names
	all_files.sort(key=lambda x: (os.path.splitext(x)[1], x), reverse=True) # sort by file type and file name in descending order

	filekey = 'delall'
	if st.sidebar.button("🗑 Delete All Audio", key=filekey):
	for file in all_files:
	os.remove(file)
	st.experimental_rerun()

	for file in all_files:
	col1, col2 = st.sidebar.columns([6, 1]) # adjust the ratio as needed
	with col1:
	st.markdown(file)
	if st.button("🎵", key="play_" + file): # play emoji button
	audio_file = open(file, 'rb')
	audio_bytes = audio_file.read()
	st.audio(audio_bytes, format='audio/wav')
	#st.markdown(get_audio_download_link(file), unsafe_allow_html=True)
	#st.text_input(label="", value=file)
	with col2:
	if st.button("🗑", key="delete_" + file):
	os.remove(file)
	st.experimental_rerun()



	# Feedback
	# Step: Give User a Way to Upvote or Downvote
	with st.expander("Give your feedback 👍", expanded=False):

	feedback = st.radio("Step 8: Give your feedback", ("👍 Upvote", "👎 Downvote"))
	if feedback == "👍 Upvote":
	st.write("You upvoted 👍. Thank you for your feedback!")
	else:
	st.write("You downvoted 👎. Thank you for your feedback!")

	load_dotenv()
	st.write(css, unsafe_allow_html=True)
	st.header("Chat with documents :books:")
	user_question = st.text_input("Ask a question about your documents:")
	if user_question:
	process_user_input(user_question)
	with st.sidebar:
	st.subheader("Your documents")
	docs = st.file_uploader("import documents", accept_multiple_files=True)
	with st.spinner("Processing"):
	raw = pdf2txt(docs)
	if len(raw) > 0:
	length = str(len(raw))
	text_chunks = txt2chunks(raw)
	vectorstore = vector_store(text_chunks)
	st.session_state.conversation = get_chain(vectorstore)
	st.markdown('# AI Search Index of Length:' + length + ' Created.') # add timing
	filename = generate_filename(raw, 'txt')
	create_file(filename, raw, '', should_save)

	# 18. Run AI Pipeline
	if __name__ == "__main__":
	whisper_main()
	main()
	#add_Med_Licensing_Exam_Dataset()