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import streamlit as st
import os
import json
from PIL import Image
# Set page configuration with a title and favicon
st.set_page_config(
page_title="🌌🚀 Mixable AI - Voice Search",
page_icon="🌠",
layout="wide",
initial_sidebar_state="expanded",
menu_items={
'Get Help': 'https://huggingface.co/awacke1',
'Report a bug': "https://huggingface.co/spaces/awacke1/WebDataDownload",
'About': "# Midjourney: https://discord.com/channels/@me/997514686608191558"
}
)
# Ensure the directory for storing scores exists
score_dir = "scores"
os.makedirs(score_dir, exist_ok=True)
# Function to generate a unique key for each button, including an emoji
def generate_key(label, header, idx):
return f"{header}_{label}_{idx}_key"
# Function to increment and save score
def update_score(key, increment=1):
score_file = os.path.join(score_dir, f"{key}.json")
if os.path.exists(score_file):
with open(score_file, "r") as file:
score_data = json.load(file)
else:
score_data = {"clicks": 0, "score": 0}
score_data["clicks"] += 1
score_data["score"] += increment
with open(score_file, "w") as file:
json.dump(score_data, file)
return score_data["score"]
# Function to load score
def load_score(key):
score_file = os.path.join(score_dir, f"{key}.json")
if os.path.exists(score_file):
with open(score_file, "r") as file:
score_data = json.load(file)
return score_data["score"]
return 0
# Transhuman Space glossary with full content
transhuman_glossary = {
"🚀 Core Technologies": ["Nanotechnology🔬", "Artificial Intelligence🤖", "Quantum Computing💻", "Spacecraft Engineering🛸", "Biotechnology🧬", "Cybernetics🦾", "Virtual Reality🕶️", "Energy Systems⚡", "Material Science🧪", "Communication Technologies📡"],
"🌐 Nations": ["Terran Federation🌍", "Martian Syndicate🔴", "Jovian Republics🪐", "Asteroid Belt Communities🌌", "Venusian Colonies🌋", "Lunar States🌖", "Outer System Alliances✨", "Digital Consciousness Collectives🧠", "Transhumanist Enclaves🦿", "Non-Human Intelligence Tribes👽"],
"💡 Memes": ["Post-Humanism🚶♂️➡️🚀", "Neo-Evolutionism🧬📈", "Digital Ascendancy💾👑", "Solar System Nationalism🌞🏛", "Space Explorationism🚀🛰", "Cyber Democracy🖥️🗳️", "Interstellar Environmentalism🌍💚", "Quantum Mysticism🔮💫", "Techno-Anarchism🔌🏴", "Cosmic Preservationism🌌🛡️"],
"🏛 Institutions": ["Interstellar Council🪖", "Transhuman Ethical Standards Organization📜", "Galactic Trade Union🤝", "Space Habitat Authority🏠", "Artificial Intelligence Safety Commission🤖🔒", "Extraterrestrial Relations Board👽🤝", "Quantum Research Institute🔬", "Biogenetics Oversight Committee🧫", "Cyberspace Regulatory Agency💻", "Planetary Defense Coalition🌍🛡"],
"🔗 Organizations": ["Neural Network Pioneers🧠🌐", "Spacecraft Innovators Guild🚀🛠", "Quantum Computing Consortium💻🔗", "Interplanetary Miners Union⛏️🪐", "Cybernetic Augmentation Advocates🦾❤️", "Biotechnological Harmony Group🧬🕊", "Stellar Navigation Circle🧭✨", "Virtual Reality Creators Syndicate🕶️🎨", "Renewable Energy Pioneers⚡🌱", "Transhuman Rights Activists🦿📢"],
"⚔️ War": ["Space Warfare Tactics🚀⚔️", "Cyber Warfare🖥️🔒", "Biological Warfare🧬💣", "Nanotech Warfare🔬⚔️", "Psychological Operations🧠🗣️", "Quantum Encryption & Decryption🔐💻", "Kinetic Bombardment🚀💥", "Energy Shield Defense🛡️⚡", "Stealth Spacecraft🚀🔇", "Artificial Intelligence Combat🤖⚔️"],
"🎖 Military": ["Interstellar Navy🚀🎖", "Planetary Guard🌍🛡", "Cybernetic Marines🦾🔫", "Nanotech Soldiers🔬💂", "Space Drone Fleet🛸🤖", "Quantum Signal Corps💻📡", "Special Operations Forces👥⚔️", "Artificial Intelligence Strategists🤖🗺️", "Orbital Defense Systems🌌🛡️", "Exoskeleton Brigades🦾🚶♂️"],
"🦹 Outlaws": ["Pirate Fleets🏴☠️🚀", "Hacktivist Collectives💻🚫", "Smuggler Caravans🛸💼", "Rebel AI Entities🤖🚩", "Black Market Biotech Dealers🧬💰", "Quantum Thieves💻🕵️♂️", "Space Nomad Raiders🚀🏴☠️", "Cyberspace Intruders💻👾", "Anti-Transhumanist Factions🚫🦾", "Rogue Nanotech Swarms🔬🦠"],
"👽 Terrorists": ["Bioengineered Virus Spreaders🧬💉", "Nanotechnology Saboteurs🔬🧨", "Cyber Terrorist Networks💻🔥", "Rogue AI Sects🤖🛑", "Space Anarchist Cells🚀Ⓐ", "Quantum Data Hijackers💻🔓", "Environmental Extremists🌍💣", "Technological Singularity Cults🤖🙏", "Interspecies Supremacists👽👑", "Orbital Bombardment Threats🛰️💥"],
}
# Function to search glossary and display results
def search_glossary(query):
for category, terms in transhuman_glossary.items():
if query.lower() in (term.lower() for term in terms):
st.markdown(f"### {category}")
st.write(f"- {query}")
st.write('## Processing query against GPT and Llama:')
# ------------------------------------------------------------------------------------------------
st.write('Reasoning with your inputs using GPT...')
response = chat_with_model(query)
st.write('Response:')
st.write(response)
filename = generate_filename(response, "txt")
create_file(filename, query, response, should_save)
st.write('Reasoning with your inputs using Llama...')
response = StreamLLMChatResponse(query)
filename_txt = generate_filename(query, "md")
create_file(filename_txt, query, response, should_save)
# ------------------------------------------------------------------------------------------------
# Display the glossary with Streamlit components, ensuring emojis are used
def display_glossary(area):
st.subheader(f"📘 Glossary for {area}")
terms = transhuman_glossary[area]
for idx, term in enumerate(terms, start=1):
st.write(f"{idx}. {term}")
# Function to display glossary in a 3x3 grid
def display_glossary_grid(glossary):
# Group related categories for a 3x3 grid
groupings = [
["🚀 Core Technologies", "🌐 Nations", "💡 Memes"],
["🏛 Institutions", "🔗 Organizations", "⚔️ War"],
["🎖 Military", "🦹 Outlaws", "👽 Terrorists"],
]
for group in groupings:
cols = st.columns(3) # Create three columns
for idx, category in enumerate(group):
with cols[idx]:
st.markdown(f"### {category}")
terms = glossary[category]
for term in terms:
st.write(f"- {term}")
# Streamlined UI for displaying buttons with scores, integrating emojis
def display_buttons_with_scores():
for header, terms in transhuman_glossary.items():
st.markdown(f"## {header}")
for term in terms:
key = generate_key(term, header, terms.index(term))
score = load_score(key)
if st.button(f"{term} {score}🚀", key=key):
update_score(key)
search_glossary('Create a three level markdown outline with 3 subpoints each where each line defines and writes out the core technology descriptions with appropriate emojis for the glossary term: ' + term)
st.experimental_rerun()
def fetch_wikipedia_summary(keyword):
# Placeholder function for fetching Wikipedia summaries
# In a real app, you might use requests to fetch from the Wikipedia API
return f"Summary for {keyword}. For more information, visit Wikipedia."
def create_search_url_youtube(keyword):
base_url = "https://www.youtube.com/results?search_query="
return base_url + keyword.replace(' ', '+')
def create_search_url_bing(keyword):
base_url = "https://www.bing.com/search?q="
return base_url + keyword.replace(' ', '+')
def create_search_url_wikipedia(keyword):
base_url = "https://www.wikipedia.org/search-redirect.php?family=wikipedia&language=en&search="
return base_url + keyword.replace(' ', '+')
def create_search_url_google(keyword):
base_url = "https://www.google.com/search?q="
return base_url + keyword.replace(' ', '+')
def display_images_and_wikipedia_summaries():
st.title('Gallery with Related Stories')
image_files = [f for f in os.listdir('.') if f.endswith('.png')]
if not image_files:
st.write("No PNG images found in the current directory.")
return
for image_file in image_files:
image = Image.open(image_file)
st.image(image, caption=image_file, use_column_width=True)
keyword = image_file.split('.')[0] # Assumes keyword is the file name without extension
# Display Wikipedia and Google search links
wikipedia_url = create_search_url_wikipedia(keyword)
google_url = create_search_url_google(keyword)
youtube_url = create_search_url_youtube(keyword)
bing_url = create_search_url_bing(keyword)
links_md = f"""
[Wikipedia]({wikipedia_url}) |
[Google]({google_url}) |
[YouTube]({youtube_url}) |
[Bing]({bing_url})
"""
st.markdown(links_md)
def get_all_query_params(key):
return st.query_params().get(key, [])
def clear_query_params():
st.query_params()
# Function to display content or image based on a query
def display_content_or_image(query):
# Check if the query matches any glossary term
for category, terms in transhuman_glossary.items():
for term in terms:
if query.lower() in term.lower():
st.subheader(f"Found in {category}:")
st.write(term)
return True # Return after finding and displaying the first match
# Check for an image match in a predefined directory (adjust path as needed)
image_dir = "images" # Example directory where images are stored
image_path = f"{image_dir}/{query}.png" # Construct image path with query
if os.path.exists(image_path):
st.image(image_path, caption=f"Image for {query}")
return True
# If no content or image is found
st.warning("No matching content or image found.")
return False
# 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
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
# Meta's Original - Chat HF Free Version:
#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.")
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=300)
#return result
# @st.cache_resource
def StreamLLMChatResponse(prompt):
try:
endpoint_url = API_URL
hf_token = API_KEY
st.write('Running client ' + endpoint_url)
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('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 filename size.
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}
st.write('STT transcript ' + OPENAI_API_URL)
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)
if has_python_code:
st.markdown('# 🐍 Python Code - Lets make an app.py.')
#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 = []
st.write('LLM stream ' + 'gpt-3.5-turbo')
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)
except:
transcript=''
st.write(transcript)
# Whisper to GPT: New!! ---------------------------------------------------------------------
st.write('Reasoning with your inputs with GPT..')
response = chat_with_model(transcript)
st.write('Response:')
st.write(response)
filename = generate_filename(response, "txt")
create_file(filename, transcript, response, should_save)
# Whisper to GPT: New!! ---------------------------------------------------------------------
# Whisper to Llama:
response = StreamLLMChatResponse(transcript)
filename_txt = generate_filename(transcript, "md")
create_file(filename_txt, transcript, response, should_save)
filename_wav = filename_txt.replace('.txt', '.wav')
import shutil
try:
if os.path.exists(filename):
shutil.copyfile(filename, filename_wav)
except:
st.write('.')
if os.path.exists(filename):
os.remove(filename)
#st.experimental_rerun()
#except:
# st.write('Starting Whisper Model on GPU. Please retry in 30 seconds.')
# 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():
# Medical exam terminology descriptions
descriptions = {
"White Blood Cells 🌊": "3 Q&A with emojis about types, facts, function, inputs and outputs of white blood cells 🎥",
"CT Imaging🦠": "3 Q&A with emojis on CT Imaging post surgery, how to, what to look for 💊",
"Hematoma 💉": "3 Q&A with emojis about hematoma and infection care and study including bacteria cultures and tests or labs💪",
"Post Surgery Wound Care 🍌": "3 Q&A with emojis on wound care, and good bedside manner 🩸",
"Healing and humor 💊": "3 Q&A with emojis on stories and humor about healing and caregiving 🚑",
"Psychology of bedside manner 🧬": "3 Q&A with emojis on bedside manner and how to make patients feel at ease🛠",
"CT scan 💊": "3 Q&A with analysis on infection using CT scan and packing for skin, cellulitus and fascia 🩺"
}
# Expander for medical topics
with st.expander("Medical Licensing Exam Topics 📚", expanded=False):
st.markdown("🩺 **Important**: Variety of topics for medical licensing exams.")
# Create buttons for each description with unique keys
for idx, (label, content) in enumerate(descriptions.items()):
button_key = f"button_{idx}"
if st.button(label, key=button_key):
st.write(f"Running {label}")
input='Create markdown outline for definition of topic ' + label + ' also short quiz with appropriate emojis and definitions for: ' + content
response=StreamLLMChatResponse(input)
filename = generate_filename(response, 'txt')
create_file(filename, input, response, should_save)
def add_medical_exam_buttons2():
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 = {
"White Blood Cells 🌊": "3 Questions and Answers with emojis about white blood cells 🎥",
"CT Imaging🦠": "3 Questions and Answers with emojis about CT Imaging of post surgery abscess, hematoma, and cerosanguiness fluid 💊",
"Hematoma 💉": "3 Questions and Answers with emojis about hematoma and infection and how heat helps white blood cells 💪",
"Post Surgery Wound Care 🍌": "3 Questions and Answers with emojis about wound care and how to help as a caregiver🩸",
"Healing and humor 💊": "3 Questions and Answers with emojis on the use of stories and humor to help patients and family 🚑",
"Psychology of bedside manner 🧬": "3 Questions and Answers with emojis about good bedside manner 🛠",
"CT scan 💊": "3 Questions and Answers with analysis of bacteria and understanding infection using cultures and CT scan 🩺"
}
# 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():
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)
# 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)
buttonlabel = '🔍Run with Llama and GPT.'
if st.button(key='RunWithLlamaandGPT', label = buttonlabel):
user_prompt = file_contents
# Llama versus GPT Battle!
all=""
try:
st.write('🔍Running with Llama.')
response = StreamLLMChatResponse(file_contents)
filename = generate_filename(user_prompt, "md")
create_file(filename, file_contents, response, should_save)
all=response
#SpeechSynthesis(response)
except:
st.markdown('Llama is sleeping. Restart ETA 30 seconds.')
# gpt
try:
st.write('🔍Running with GPT.')
response2 = chat_with_model(user_prompt, file_contents, model_choice)
filename2 = generate_filename(file_contents, choice)
create_file(filename2, user_prompt, response, should_save)
all=all+response2
#SpeechSynthesis(response2)
except:
st.markdown('GPT is sleeping. Restart ETA 30 seconds.')
SpeechSynthesis(all)
if next_action=='search':
file_content_area = st.text_area("File Contents:", file_contents, height=500)
st.write('🔍Running with Llama and GPT.')
user_prompt = file_contents
# Llama versus GPT Battle!
all=""
try:
st.write('🔍Running with Llama.')
response = StreamLLMChatResponse(file_contents)
filename = generate_filename(user_prompt, ".md")
create_file(filename, file_contents, response, should_save)
all=response
#SpeechSynthesis(response)
except:
st.markdown('Llama is sleeping. Restart ETA 30 seconds.')
# gpt
try:
st.write('🔍Running with GPT.')
response2 = chat_with_model(user_prompt, file_contents, model_choice)
filename2 = generate_filename(file_contents, choice)
create_file(filename2, user_prompt, response, should_save)
all=all+response2
#SpeechSynthesis(response2)
except:
st.markdown('GPT is sleeping. Restart ETA 30 seconds.')
SpeechSynthesis(all)
# 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
GiveFeedback=False
if GiveFeedback:
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)
# Relocated! Hope you like your new space - enjoy!
# Display instructions and handle query parameters
st.markdown("## Glossary Lookup\nEnter a term in the URL query, like `?q=Nanotechnology` or `?query=Martian Syndicate`.")
try:
query_params = st.query_params
#query = (query_params.get('q') or query_params.get('query') or [''])[0]
query = (query_params.get('q') or query_params.get('query') or [''])
st.markdown('# Running query: ' + query)
if query: search_glossary(query)
except:
st.markdown('No glossary lookup')
# Display the glossary grid
#st.title("Transhuman Space Glossary 🌌")
display_glossary_grid(transhuman_glossary)
#st.title("🌌🚀 Transhuman Space Encyclopedia")
#st.markdown("## Explore the universe of Transhuman Space through interactive storytelling and encyclopedic knowledge.🌠")
display_buttons_with_scores()
display_images_and_wikipedia_summaries()
# Assuming the transhuman_glossary and other setup code remains the same
#st.write("Current Query Parameters:", st.query_params)
#st.markdown("### Query Parameters - These Deep Link Map to Remixable Methods, Navigate or Trigger Functionalities")
# Example: Using query parameters to navigate or trigger functionalities
if 'action' in st.query_params:
action = st.query_params()['action'][0] # Get the first (or only) 'action' parameter
if action == 'show_message':
st.success("Showing a message because 'action=show_message' was found in the URL.")
elif action == 'clear':
clear_query_params()
st.experimental_rerun()
# Handling repeated keys
if 'multi' in st.query_params:
multi_values = get_all_query_params('multi')
st.write("Values for 'multi':", multi_values)
# Manual entry for demonstration
st.write("Enter query parameters in the URL like this: ?action=show_message&multi=1&multi=2")
if 'query' in st.query_params:
query = st.query_params['query'][0] # Get the query parameter
# Display content or image based on the query
display_content_or_image(query)
# Add a clear query parameters button for convenience
if st.button("Clear Query Parameters", key='ClearQueryParams'):
# This will clear the browser URL's query parameters
st.experimental_set_query_params
st.experimental_rerun()
# 18. Run AI Pipeline
if __name__ == "__main__":
whisper_main()
main() |