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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="πͺLlama Whispererπ¦ Voice Chatπ", 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("π’ The dataset is empty.") | |
else: | |
st.write(""" | |
π Use the search box to filter questions or use the grid to scroll through the dataset. | |
""") | |
# π©βπ¬ Search Box | |
search_term = st.text_input("Search for a specific question:", "") | |
# π 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 | |
# π Display Data | |
start_idx = (page_number - 1) * records_per_page | |
end_idx = start_idx + records_per_page | |
# π§ͺ 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) | |
# π 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"π Total Records: {num_records} | π 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 anyone to write a discharge plan. List the entities, features and relationships to CCDA and FHIR objects in boldface." | |
should_save = st.sidebar.checkbox("πΎ 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 π€£", expanded=True): | |
# Tip about the Dromedary family | |
st.markdown("π¬ **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 π": "Write ten random adult limericks based on quotes that are tweet length and make you laugh π", | |
"Wise Quotes π§": "Generate ten wise quotes that are tweet length π¦", | |
"Funny Rhymes π€": "Create ten funny rhymes that are tweet length πΆ", | |
"Medical Jokes π": "Create ten medical jokes that are tweet length π₯", | |
"Minnesota Humor βοΈ": "Create ten jokes about Minnesota that are tweet length π¨οΈ", | |
"Top Funny Stories π": "Create ten funny stories that are tweet length π", | |
"More Funny Rhymes ποΈ": "Create ten more funny rhymes that are tweet length π΅" | |
} | |
# Create columns | |
col1, col2, col3 = st.columns([1, 1, 1], gap="small") | |
# Add buttons to columns | |
if col1.button("Generate Limericks π"): | |
StreamLLMChatResponse(descriptions["Generate Limericks π"]) | |
if col2.button("Wise Quotes π§"): | |
StreamLLMChatResponse(descriptions["Wise Quotes π§"]) | |
if col3.button("Funny Rhymes π€"): | |
StreamLLMChatResponse(descriptions["Funny Rhymes π€"]) | |
col4, col5, col6 = st.columns([1, 1, 1], gap="small") | |
if col4.button("Medical Jokes π"): | |
StreamLLMChatResponse(descriptions["Medical Jokes π"]) | |
if col5.button("Minnesota Humor βοΈ"): | |
StreamLLMChatResponse(descriptions["Minnesota Humor βοΈ"]) | |
if col6.button("Top Funny Stories π"): | |
StreamLLMChatResponse(descriptions["Top Funny Stories π"]) | |
col7 = st.columns(1, gap="small") | |
if col7[0].button("More Funny Rhymes ποΈ"): | |
StreamLLMChatResponse(descriptions["More Funny Rhymes ποΈ"]) | |
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>π Read It Aloud</h1> | |
<textarea id="textArea" rows="10" cols="80"> | |
''' | |
documentHTML5 = documentHTML5 + result | |
documentHTML5 = documentHTML5 + ''' | |
</textarea> | |
<br> | |
<button onclick="readAloud()">π 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 | |
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 | |
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 | |
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 | |
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 | |
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 | |
def vector_store(text_chunks): | |
embeddings = OpenAIEmbeddings(openai_api_key=key) | |
return FAISS.from_texts(texts=text_chunks, embedding=embeddings) | |
# Memory and Retrieval chains | |
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 | |
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 | |
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 π", expanded=False): | |
st.markdown("π©Ί **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 π": "3 Questions and Answers with emojis about Doppler Ultrasound imaging techniques π₯", | |
"Oseltamivir π¦ ": "3 Questions and Answers with emojis about the antiviral medication Oseltamivir π", | |
"IM Epinephrine π": "3 Questions and Answers with emojis about intramuscular administration of epinephrine πͺ", | |
"Hypokalemia π": "3 Questions and Answers with emojis about low potassium levels in blood π©Έ", | |
"Succinylcholine π": "3 Questions and Answers with emojis on the use and side-effects of Succinylcholine π", | |
"Phosphoinositol System π§¬": "3 Questions and Answers with emojis about the Phosphoinositol signalling system π ", | |
"Ramipril π": "3 Questions and Answers with emojis related to the ACE inhibitor Ramipril π©Ί" | |
} | |
# Create columns | |
col1, col2, col3, col4 = st.columns([1, 1, 1, 1], gap="small") | |
# Add buttons to columns | |
if col1.button("Ultrasound with Doppler π"): | |
StreamLLMChatResponse(descriptions["Ultrasound with Doppler π"]) | |
if col2.button("Oseltamivir π¦ "): | |
StreamLLMChatResponse(descriptions["Oseltamivir π¦ "]) | |
if col3.button("IM Epinephrine π"): | |
StreamLLMChatResponse(descriptions["IM Epinephrine π"]) | |
if col4.button("Hypokalemia π"): | |
StreamLLMChatResponse(descriptions["Hypokalemia π"]) | |
col5, col6, col7, col8 = st.columns([1, 1, 1, 1], gap="small") | |
if col5.button("Succinylcholine π"): | |
StreamLLMChatResponse(descriptions["Succinylcholine π"]) | |
if col6.button("Phosphoinositol System π§¬"): | |
StreamLLMChatResponse(descriptions["Phosphoinositol System π§¬"]) | |
if col7.button("Ramipril π"): | |
StreamLLMChatResponse(descriptions["Ramipril π"]) | |
# 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 π", expanded=False): | |
example_input = st.text_input("Enter your example text:", value=prompt, help="Enter text to get a response from DromeLlama.") | |
if st.button("Run Prompt With DromeLlama", help="Click to run the prompt."): | |
try: | |
StreamLLMChatResponse(example_input) | |
except: | |
st.write('DromeLlama is asleep. Starting up now on A10 - please give 5 minutes then retry as KEDA scales up from zero to activate running container(s).') | |
openai.api_key = os.getenv('OPENAI_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("ποΈ 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('π¬ 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("π Delete All Text"): | |
for file in all_files: | |
os.remove(file) | |
st.experimental_rerun() | |
if st.sidebar.button("β¬οΈ 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("π", 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("π", key="open_"+file): # open emoji button | |
with open(file, 'r') as f: | |
file_contents = f.read() | |
next_action='open' | |
with col4: | |
if st.button("π", key="read_"+file): # search emoji button | |
with open(file, 'r') as f: | |
file_contents = f.read() | |
next_action='search' | |
with col5: | |
if st.button("π", 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)}">β¬οΈ 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("π 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("π΅", 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("π", 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 π", expanded=False): | |
feedback = st.radio("Step 8: Give your feedback", ("π Upvote", "π Downvote")) | |
if feedback == "π Upvote": | |
st.write("You upvoted π. Thank you for your feedback!") | |
else: | |
st.write("You downvoted π. 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() |