import streamlit as st
import os
import json
import random
# 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
import streamlit.components.v1 as components # Import Streamlit Components for HTML5
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
from PIL import Image
from urllib.parse import quote # Ensure this import is included
# Set page configuration with a title and favicon
st.set_page_config(
page_title="๐๐GraphicNovelAI",
page_icon="๐๐",
layout="wide",
initial_sidebar_state="expanded",
menu_items={
'Get Help': 'https://huggingface.co/awacke1',
'Report a bug': "https://huggingface.co/spaces/awacke1/GraphicAINovel",
'About': "# Midjourney: https://discord.com/channels/@me/997514686608191558"
}
)
experimentalSubProgram="""
import streamlit as st
import random
import numpy as np
"""
PromptPrefix = 'Create a markdown outline and table with appropriate emojis for graphic novel rules defining the method steps of play for topic of '
PromptPrefix2 = 'Create a streamlit python user app. Show full code listing. Create a UI implementing storytelling features using plot twists and recurring interesting named characters with genius traits and file IO, randomness, ten point choice lists, math distribution tradeoffs, witty humorous dilemnas with emoji , rewards, variables, reusable functions with parameters, and data driven app with python libraries and streamlit components for Javascript and HTML5. Use appropriate emojis for labels to summarize and list parts, function, conditions for topic: '
#PromptPrefix2 = PromptPrefix2 + experimentalSubProgram # Super meta program!
st.markdown('''### ๐โจ๐ GraphicNovelAI ''')
with st.expander("Help / About ๐", expanded=False):
st.markdown('''
- ๐ **Unlock Plots:** Elevate your vocabulary with AI. Turns plots into thrilling experiences.
- ๐ **Features:** Creates extensive glossaries & exciting challenges.
- ๐งโโ๏ธ **Experience:** Become a graphic novel plot wizard, boost your language skills.
- ๐ **Query Use:** Input `?q=Palindrome` or `?query=Anagram` in URL for new challenges.
''')
roleplaying_glossary = {
"๐จโ๐ฉโ๐งโ๐ฆ Top Graphic Novel Plot Themes": {
"Epic Fantasy": [
"Ancient prophecies and mystical artifacts",
"Epic battles between good and evil",
"Complex world-building with diverse cultures",
"Journey of a reluctant hero",
"Alliance of unlikely companions",
"Betrayal and redemption arcs",
"Magic systems and mythical creatures",
"Climactic confrontation with a dark lord"
],
"Superhero Sagas": [
"Origin stories of heroes and villains",
"Struggle with personal identity and responsibility",
"Formation of superhero teams",
"Epic battles to save the city/world",
"Moral dilemmas and ethical questions",
"Interdimensional threats and cosmic wars",
"Evolution of powers and discovery of new abilities",
"Legacy heroes and passing of the mantle"
],
"Post-Apocalyptic Survival": [
"Survival in a world after a global catastrophe",
"Rebuilding society from the ashes",
"Conflict between surviving factions",
"Quests for scarce resources",
"Encounters with mutated creatures",
"Moral ambiguity and survival ethics",
"Exploration of human resilience",
"Discovery of a safe haven or cure"
],
"Science Fiction and Space Opera": [
"Exploration of distant galaxies",
"Conflict between alien species",
"Advanced technology and space travel",
"Utopian and dystopian societies",
"Time travel and alternate realities",
"Artificial intelligence and robotics",
"Quests for knowledge and discovery",
"Rebellion against oppressive regimes"
],
"Horror and Supernatural": [
"Haunted locations and ghost stories",
"Battles against demonic forces",
"Survival horror and psychological terror",
"Folklore and urban legends",
"Vampires, werewolves, and other monsters",
"Occult practices and dark magic",
"Apocalyptic and Lovecraftian themes",
"Investigations into the unknown"
],
"Romance and Relationship Dramas": [
"Complex romantic entanglements",
"Struggles with identity and societal expectations",
"Heartbreak, healing, and growth",
"Forbidden love and star-crossed lovers",
"Contemporary relationship dynamics",
"Cultural and social differences",
"Self-discovery and personal fulfillment",
"Romantic comedies and tragedies"
]
}
}
# 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'{file_name}'
return href
def FileSidebar():
# ----------------------------------------------------- File Sidebar for Jump Gates ------------------------------------------
# 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("๐ Delete All Text"):
for file in all_files:
os.remove(file)
st.experimental_rerun()
if st.sidebar.button("โฌ๏ธ 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("๐", 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("๐", key="open_"+file): # open emoji button
with open(file, 'r') as f:
file_contents = f.read()
next_action='open'
with col4:
if st.button("๐", key="read_"+file): # search emoji button
with open(file, 'r') as f:
file_contents = f.read()
next_action='search'
with col5:
if st.button("๐", 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 = '๐Run'
if st.button(key='RunWithLlamaandGPT', label = buttonlabel):
user_prompt = file_contents
# gpt
try:
st.write('๐Running..')
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=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('๐Running..')
user_prompt = file_contents
# gpt
try:
st.write('๐Running 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=response2
except:
st.markdown('GPT is sleeping. Restart ETA 30 seconds.')
SpeechSynthesis(all)
# ----------------------------------------------------- File Sidebar for Jump Gates ------------------------------------------
FileSidebar()
# ---- Art Card Sidebar with Random Selection of image:
def get_image_as_base64(url):
response = requests.get(url)
if response.status_code == 200:
# Convert the image to base64
return base64.b64encode(response.content).decode("utf-8")
else:
return None
def create_download_link(filename, base64_str):
href = f'Download Image'
return href
# List of image URLs
image_urls = [
"https://cdn-uploads.huggingface.co/production/uploads/620630b603825909dcbeba35/f1RmBh0D92Lm7eJXg0q32.png",
"https://cdn-uploads.huggingface.co/production/uploads/620630b603825909dcbeba35/gv1xmIiXh1NGTeeV-cYF2.png",
"https://cdn-uploads.huggingface.co/production/uploads/620630b603825909dcbeba35/2YsnDyc_nDNW71PPKozdN.png",
"https://cdn-uploads.huggingface.co/production/uploads/620630b603825909dcbeba35/G_GkRD_IT3f14K7gWlbwi.png",
"https://cdn-uploads.huggingface.co/production/uploads/620630b603825909dcbeba35/2-KfxcuXRcTFiHf4XlNsX.png"
]
# Select a random URL from the list
selected_image_url = random.choice(image_urls)
# Get the base64 encoded string of the selected image
selected_image_base64 = get_image_as_base64(selected_image_url)
if selected_image_base64 is not None:
with st.sidebar:
st.markdown("""### Word Game AI""")
# Display the image
st.markdown(f"")
# Create and display the download link
download_link = create_download_link("downloaded_image.png", selected_image_base64)
st.markdown(download_link, unsafe_allow_html=True)
else:
st.sidebar.write("Failed to load the image.")
# ---- Art Card Sidebar with random selection of image.
# 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
def search_glossary(query):
for category, terms in roleplaying_glossary.items():
if query.lower() in (term.lower() for term in terms):
st.markdown(f"#### {category}")
st.write(f"- {query}")
all=""
query2 = PromptPrefix + query # Add prompt preface for method step task behavior
# st.write('## ' + query2)
st.write('## ๐ Running with GPT.') # -------------------------------------------------------------------------------------------------
response = chat_with_model(query2)
filename = generate_filename(query2 + ' --- ' + response, "md")
create_file(filename, query, response, should_save)
query3 = PromptPrefix2 + query + ' creating streamlit functions that implement outline of method steps below: ' + response # Add prompt preface for coding task behavior
# st.write('## ' + query3)
st.write('## ๐ Coding with GPT.') # -------------------------------------------------------------------------------------------------
response2 = chat_with_model(query3)
filename_txt = generate_filename(query + ' --- ' + response2, "py")
create_file(filename_txt, query, response2, should_save)
all = '# Query: ' + query + '# Response: ' + response + '# Response2: ' + response2
filename_txt2 = generate_filename(query + ' --- ' + all, "md")
create_file(filename_txt2, query, all, should_save)
SpeechSynthesis(all)
return all
# Function to display the glossary in a structured format
def display_glossary(glossary, area):
if area in glossary:
st.subheader(f"๐ Glossary for {area}")
for game, terms in glossary[area].items():
st.markdown(f"### {game}")
for idx, term in enumerate(terms, start=1):
st.write(f"{idx}. {term}")
# Function to display the entire glossary in a grid format with links
def display_glossary_grid(roleplaying_glossary):
search_urls = {
"๐": lambda k: f"https://en.wikipedia.org/wiki/{quote(k)}",
"๐": lambda k: f"https://www.google.com/search?q={quote(k)}",
"โถ๏ธ": lambda k: f"https://www.youtube.com/results?search_query={quote(k)}",
"๐": lambda k: f"https://www.bing.com/search?q={quote(k)}",
"๐ฒ": lambda k: f"https://huggingface.co/spaces/awacke1/GraphicAINovel?q={quote(k)}", # this url plus query!
"๐": lambda k: f"https://huggingface.co/spaces/awacke1/GraphicAINovel?q={quote(PromptPrefix)}{quote(k)}", # this url plus query!
"๐": lambda k: f"https://huggingface.co/spaces/awacke1/GraphicAINovel?q={quote(PromptPrefix2)}", # this url plus query!
}
for category, details in roleplaying_glossary.items():
st.write(f"### {category}")
cols = st.columns(len(details)) # Create dynamic columns based on the number of games
for idx, (game, terms) in enumerate(details.items()):
with cols[idx]:
st.markdown(f"#### {game}")
for term in terms:
links_md = ' '.join([f"[{emoji}]({url(term)})" for emoji, url in search_urls.items()])
st.markdown(f"{term} {links_md}", unsafe_allow_html=True)
game_emojis = {
"Dungeons and Dragons": "๐",
"Call of Cthulhu": "๐",
"GURPS": "๐ฒ",
"Pathfinder": "๐บ๏ธ",
"Kindred of the East": "๐ ",
"Changeling": "๐",
}
topic_emojis = {
"Core Rulebooks": "๐",
"Maps & Settings": "๐บ๏ธ",
"Game Mechanics & Tools": "โ๏ธ",
"Monsters & Adversaries": "๐น",
"Campaigns & Adventures": "๐",
"Creatives & Assets": "๐จ",
"Game Master Resources": "๐ ๏ธ",
"Lore & Background": "๐",
"Character Development": "๐ง",
"Homebrew Content": "๐ง",
"General Topics": "๐",
}
# Adjusted display_buttons_with_scores function
def display_buttons_with_scores():
for category, games in roleplaying_glossary.items():
category_emoji = topic_emojis.get(category, "๐") # Default to search icon if no match
st.markdown(f"## {category_emoji} {category}")
for game, terms in games.items():
game_emoji = game_emojis.get(game, "๐ฎ") # Default to generic game controller if no match
for term in terms:
key = f"{category}_{game}_{term}".replace(' ', '_').lower()
score = load_score(key)
if st.button(f"{game_emoji} {term} {score}", key=key):
update_score(key)
# Create a dynamic query incorporating emojis and formatting for clarity
query_prefix = f"{category_emoji} {game_emoji} **{game} - {category}:**"
# ----------------------------------------------------------------------------------------------
#query_body = f"Create a detailed outline for **{term}** with subpoints highlighting key aspects, using emojis for visual engagement. Include step-by-step rules and boldface important entities and ruleset elements."
query_body = f"Create a streamlit python app.py that produces a detailed markdown outline and emoji laden user interface with labels with the entity name and emojis in all labels with a set of streamlit UI components with drop down lists and dataframes and buttons with expander and sidebar for the app to run the data as default values mostly in text boxes. Feature a 3 point outline sith 3 subpoints each where each line has about six words describing this and also contain appropriate emoji for creating sumamry of all aspeccts of this topic. an outline for **{term}** with subpoints highlighting key aspects, using emojis for visual engagement. Include step-by-step rules and boldface important entities and ruleset elements."
response = search_glossary(query_prefix + query_body)
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 create_search_url_ai(keyword):
base_url = "https://huggingface.co/spaces/awacke1/GraphicAINovel?q="
return base_url + keyword.replace(' ', '+')
def display_images_and_wikipedia_summaries():
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
# Sort image_files based on the length of the keyword to create a visually consistent grid
image_files_sorted = sorted(image_files, key=lambda x: len(x.split('.')[0]))
# Calculate the grid size based on the sorted keywords
grid_sizes = [len(f.split('.')[0]) for f in image_files_sorted]
# Dynamically adjust column size based on keyword length
col_sizes = ['small' if size <= 4 else 'medium' if size <= 8 else 'large' for size in grid_sizes]
# Create a map for number of columns to use for each size
num_columns_map = {"small": 4, "medium": 3, "large": 2}
current_grid_size = 0
for image_file, col_size in zip(image_files_sorted, col_sizes):
if current_grid_size != num_columns_map[col_size]:
cols = st.columns(num_columns_map[col_size])
current_grid_size = num_columns_map[col_size]
col_index = 0
with cols[col_index % current_grid_size]:
image = Image.open(image_file)
st.image(image, caption=image_file, use_column_width=True)
# Display Wikipedia and Google search links
keyword = image_file.split('.')[0] # Assumes keyword is the file name without extension
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)
ai_url = create_search_url_ai(keyword)
links_md = f"""
[Wikipedia]({wikipedia_url}) |
[Google]({google_url}) |
[YouTube]({youtube_url}) |
[Bing]({bing_url}) |
[AI]({ai_url})
"""
st.markdown(links_md)
col_index += 1
def display_images_and_wikipedia_summaries_deprecated():
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)
ai_url = create_search_url_ai(keyword)
links_md = f"""
[Wikipedia]({wikipedia_url}) |
[Google]({google_url}) |
[YouTube]({youtube_url}) |
[Bing]({bing_url}) |
[AI]({ai_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
# ------------------------------------
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("๐ข The dataset is empty.")
else:
st.write("""
๐ Use the search box to filter questions or use the grid to scroll through the dataset.
""")
# ๐ฉโ๐ฌ Search Box
search_term = st.text_input("Search for a specific question:", "")
# ๐ 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
# ๐ Display Data
start_idx = (page_number - 1) * records_per_page
end_idx = start_idx + records_per_page
# ๐งช 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)
# ๐ 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"๐ Total Records: {num_records} | ๐ 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("๐พ 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 ๐คฃ", expanded=True):
# Tip about the Dromedary family
st.markdown("๐ฌ **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 ๐": "Write ten random adult limericks based on quotes that are tweet length and make you laugh ๐ญ",
"Wise Quotes ๐ง": "Generate ten wise quotes that are tweet length ๐ฆ",
"Funny Rhymes ๐ค": "Create ten funny rhymes that are tweet length ๐ถ",
"Medical Jokes ๐": "Create ten medical jokes that are tweet length ๐ฅ",
"Minnesota Humor โ๏ธ": "Create ten jokes about Minnesota that are tweet length ๐จ๏ธ",
"Top Funny Stories ๐": "Create ten funny stories that are tweet length ๐",
"More Funny Rhymes ๐๏ธ": "Create ten more funny rhymes that are tweet length ๐ต"
}
# Create columns
col1, col2, col3 = st.columns([1, 1, 1], gap="small")
# Add buttons to columns
if col1.button("Wise Limericks ๐"):
StreamLLMChatResponse(descriptions["Generate Limericks ๐"])
if col2.button("Wise Quotes ๐ง"):
StreamLLMChatResponse(descriptions["Wise Quotes ๐ง"])
#if col3.button("Funny Rhymes ๐ค"):
# StreamLLMChatResponse(descriptions["Funny Rhymes ๐ค"])
col4, col5, col6 = st.columns([1, 1, 1], gap="small")
if col4.button("Top Ten Funniest Clean Jokes ๐"):
StreamLLMChatResponse(descriptions["Top Ten Funniest Clean Jokes ๐"])
if col5.button("Minnesota Humor โ๏ธ"):
StreamLLMChatResponse(descriptions["Minnesota Humor โ๏ธ"])
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. ๐๏ธ"):
StreamLLMChatResponse(descriptions["Top Ten Best Write a streamlit python program prompts to build AI programs. ๐๏ธ"])
def SpeechSynthesis(result):
documentHTML5='''
Read It Aloud
๐ Read It Aloud
'''
components.html(documentHTML5, width=1280, height=300)
#return result
# 3. Stream Llama Response
# @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|>", ""],
)
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}
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)
#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)]
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'Download All'
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 ๐": "3 Q&A with emojis about types, facts, function, inputs and outputs of white blood cells ๐ฅ",
"CT Imaging๐ฆ ": "3 Q&A with emojis on CT Imaging post surgery, how to, what to look for ๐",
"Hematoma ๐": "3 Q&A with emojis about hematoma and infection care and study including bacteria cultures and tests or labs๐ช",
"Post Surgery Wound Care ๐": "3 Q&A with emojis on wound care, and good bedside manner ๐ฉธ",
"Healing and humor ๐": "3 Q&A with emojis on stories and humor about healing and caregiving ๐",
"Psychology of bedside manner ๐งฌ": "3 Q&A with emojis on bedside manner and how to make patients feel at ease๐ ",
"CT scan ๐": "3 Q&A with analysis on infection using CT scan and packing for skin, cellulitus and fascia ๐ฉบ"
}
# Expander for medical topics
with st.expander("Medical Licensing Exam Topics ๐", expanded=False):
st.markdown("๐ฉบ **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)
# 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 ๐", 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("๐๏ธ 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('๐ฌ 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)
# 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'โฌ๏ธ Download Audio'
# 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("๐ 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("๐ต", 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("๐", 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 ๐", expanded=False):
feedback = st.radio("Step 8: Give your feedback", ("๐ Upvote", "๐ Downvote"))
if feedback == "๐ Upvote":
st.write("You upvoted ๐. Thank you for your feedback!")
else:
st.write("You downvoted ๐. 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(' ')
# Display the glossary grid
st.markdown("### ๐ฒ๐บ๏ธ Graphic Novel Gallery")
display_images_and_wikipedia_summaries()
display_glossary_grid(roleplaying_glossary)
st.markdown("## Explore the vast universe of Graphic Novels including card and board games with fascinating readable content and semantic rules where language is fun!.๐ ")
display_buttons_with_scores()
# 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()