GraphicAINovel / app.py
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import streamlit as st
import os
import json
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="๐ŸŒŒ๐Ÿš€ Mixable AI - Voice Search",
page_icon="๐ŸŒ ",
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"
}
)
# -----------------------------------------------------------------Art Card Sidebar:
import base64
import requests
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'<a href="data:file/png;base64,{base64_str}" download="{filename}">Download Image</a>'
return href
# Get this from paste into markdown feature
image_url = "https://cdn-uploads.huggingface.co/production/uploads/620630b603825909dcbeba35/G_GkRD_IT3f14K7gWlbwi.png"
image_url2 = "https://cdn-uploads.huggingface.co/production/uploads/620630b603825909dcbeba35/gikaT871Mm8k6wuv4pl_g.png"
image_url3 = "https://cdn-uploads.huggingface.co/production/uploads/620630b603825909dcbeba35/gv1xmIiXh1NGTeeV-cYF2.png"
image_url4 = "https://cdn-uploads.huggingface.co/production/uploads/620630b603825909dcbeba35/2YsnDyc_nDNW71PPKozdN.png"
image_url5 = "https://cdn-uploads.huggingface.co/production/uploads/620630b603825909dcbeba35/eGii5DvGIuCtWCU08_i-D.png"
image_url6 = "https://cdn-uploads.huggingface.co/production/uploads/620630b603825909dcbeba35/2-KfxcuXRcTFiHf4XlNsX.png"
image_base64 = get_image_as_base64(image_url)
image_base642 = get_image_as_base64(image_url2)
image_base643 = get_image_as_base64(image_url3)
image_base644 = get_image_as_base64(image_url4)
image_base645 = get_image_as_base64(image_url5)
image_base646 = get_image_as_base64(image_url6)
if image_base64 is not None:
with st.sidebar:
st.markdown("""### Word Game AI""")
st.markdown(f"![image](data:image/png;base64,{image_base64})")
st.markdown(f"![image](data:image/png;base64,{image_base642})")
st.markdown(f"![image](data:image/png;base64,{image_base643})")
st.markdown(f"![image](data:image/png;base64,{image_base644})")
st.markdown(f"![image](data:image/png;base64,{image_base645})")
st.markdown(f"![image](data:image/png;base64,{image_base646})")
#download_link = create_download_link("downloaded_image.png", image_base64)
#st.markdown(download_link, unsafe_allow_html=True)
else:
st.sidebar.write("Failed to load the image.")
# ------------------------------------------------------------- Art Card Sidebar
# 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
roleplaying_glossary = {
"๐Ÿ“š Traditional Word Games": {
"Scrabble": ["Tile placement", "Word formation", "Point scoring"],
"Boggle": ["Letter grid", "Timed word search", "Word length points"],
"Crossword Puzzles": ["Clue solving", "Word filling", "Thematic puzzles"],
"Banagrams": ["Tile shuffling", "Personal anagram puzzles", "Speed challenge"],
"Hangman": ["Word guessing", "Letter guessing", "Limited attempts"],
},
"๐Ÿ’ก Digital Word Games": {
"Words With Friends": ["Digital Scrabble-like", "Online multiplayer", "Social interaction"],
"Wordle": ["Daily word guessing", "Limited tries", "Shareable results"],
"Letterpress": ["Competitive word search", "Territory control", "Strategic letter usage"],
"Alphabear": ["Word formation", "Cute characters", "Puzzle strategy"],
},
"๐ŸŽฎ Game Design and Mechanics": {
"Gameplay Dynamics": ["Word discovery", "Strategic placement", "Time pressure"],
"Player Engagement": ["Daily challenges", "Leaderboards", "Community puzzles"],
"Learning and Development": ["Vocabulary building", "Spelling practice", "Cognitive skills"],
},
"๐ŸŒ Online Platforms & Tools": {
"Multiplayer Platforms": ["Real-time competition", "Asynchronous play", "Global matchmaking"],
"Educational Tools": ["Learning modes", "Progress tracking", "Skill levels"],
"Community Features": ["Forums", "Tips and tricks sharing", "Tournament organization"],
},
"๐ŸŽ–๏ธ Competitive Scene": {
"Scrabble Tournaments": ["Official rules", "National and international", "Professional rankings"],
"Crossword Competitions": ["Speed solving", "Puzzle variety", "Prizes and recognition"],
"Wordle Challenges": ["Streaks", "Perfect scores", "Community leaderboards"],
},
"๐Ÿ“š Lore & Background": {
"History of Word Games": ["Evolution over time", "Cultural significance", "Famous games"],
"Iconic Word Game Creators": ["Creators and designers", "Inspirational stories", "Game development"],
"Word Games in Literature": ["Literary puzzles", "Wordplay in writing", "Famous examples"],
},
"๐Ÿ› ๏ธ Resources & Development": {
"Game Creation Tools": ["Word game generators", "Puzzle design software", "Community mods"],
"Educational Resources": ["Vocabulary lists", "Word game strategies", "Learning methodologies"],
"Digital Platforms": ["App development", "Online game hosting", "Social media integration"],
},
}
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}")
st.write('## ' + query)
all=""
st.write('## ๐Ÿ” Running with GPT.') # -------------------------------------------------------------------------------------------------
response = chat_with_model(query)
#st.write(response)
filename = generate_filename(query + ' --- ' + response, "md")
create_file(filename, query, response, should_save)
st.write('## ๐Ÿ” Running with Llama.') # -------------------------------------------------------------------------------------------------
response2 = StreamLLMChatResponse(query)
#st.write(response2)
filename_txt = generate_filename(query + ' --- ' + response2, "md")
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"๐Ÿ“˜ 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 = {
"๐Ÿ“–": lambda k: f"https://en.wikipedia.org/wiki/{quote(k)}",
"๐Ÿ”": lambda k: f"https://www.google.com/search?q={quote(k)}",
"โ–ถ๏ธ": lambda k: f"https://www.youtube.com/results?search_query={quote(k)}",
"๐Ÿ”Ž": lambda k: f"https://www.bing.com/search?q={quote(k)}",
"๐ŸŽฒ": lambda k: f"https://huggingface.co/spaces/awacke1/MixableWordGameAI?q={quote(k)}", # 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": "๐Ÿ‰",
"Call of Cthulhu": "๐Ÿ™",
"GURPS": "๐ŸŽฒ",
"Pathfinder": "๐Ÿ—บ๏ธ",
"Kindred of the East": "๐ŸŒ…",
"Changeling": "๐Ÿƒ",
}
topic_emojis = {
"Core Rulebooks": "๐Ÿ“š",
"Maps & Settings": "๐Ÿ—บ๏ธ",
"Game Mechanics & Tools": "โš™๏ธ",
"Monsters & Adversaries": "๐Ÿ‘น",
"Campaigns & Adventures": "๐Ÿ“œ",
"Creatives & Assets": "๐ŸŽจ",
"Game Master Resources": "๐Ÿ› ๏ธ",
"Lore & Background": "๐Ÿ“–",
"Character Development": "๐Ÿง",
"Homebrew Content": "๐Ÿ”ง",
"General Topics": "๐ŸŒ",
}
# Adjusted display_buttons_with_scores function
def display_buttons_with_scores():
for category, games in roleplaying_glossary.items():
category_emoji = topic_emojis.get(category, "๐Ÿ”") # 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, "๐ŸŽฎ") # 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 CSV dataset user interface with 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, roleplaying_glossary)
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("๐Ÿ˜ข The dataset is empty.")
else:
st.write("""
๐Ÿ” Use the search box to filter questions or use the grid to scroll through the dataset.
""")
# ๐Ÿ‘ฉโ€๐Ÿ”ฌ Search Box
search_term = st.text_input("Search for a specific question:", "")
# ๐ŸŽ› 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
# ๐Ÿ“Š Display Data
start_idx = (page_number - 1) * records_per_page
end_idx = start_idx + records_per_page
# ๐Ÿงช 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)
# ๐ŸŒ 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"๐Ÿ˜Š Total Records: {num_records} | ๐Ÿ“„ 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("๐Ÿ’พ 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 ๐Ÿคฃ", expanded=True):
# Tip about the Dromedary family
st.markdown("๐Ÿ”ฌ **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 ๐Ÿ˜‚": "Write ten random adult limericks based on quotes that are tweet length and make you laugh ๐ŸŽญ",
"Wise Quotes ๐Ÿง™": "Generate ten wise quotes that are tweet length ๐Ÿฆ‰",
"Funny Rhymes ๐ŸŽค": "Create ten funny rhymes that are tweet length ๐ŸŽถ",
"Medical Jokes ๐Ÿ’‰": "Create ten medical jokes that are tweet length ๐Ÿฅ",
"Minnesota Humor โ„๏ธ": "Create ten jokes about Minnesota that are tweet length ๐ŸŒจ๏ธ",
"Top Funny Stories ๐Ÿ“–": "Create ten funny stories that are tweet length ๐Ÿ“š",
"More Funny Rhymes ๐ŸŽ™๏ธ": "Create ten more funny rhymes that are tweet length ๐ŸŽต"
}
# Create columns
col1, col2, col3 = st.columns([1, 1, 1], gap="small")
# Add buttons to columns
if col1.button("Wise Limericks ๐Ÿ˜‚"):
StreamLLMChatResponse(descriptions["Generate Limericks ๐Ÿ˜‚"])
if col2.button("Wise Quotes ๐Ÿง™"):
StreamLLMChatResponse(descriptions["Wise Quotes ๐Ÿง™"])
#if col3.button("Funny Rhymes ๐ŸŽค"):
# StreamLLMChatResponse(descriptions["Funny Rhymes ๐ŸŽค"])
col4, col5, col6 = st.columns([1, 1, 1], gap="small")
if col4.button("Top Ten Funniest Clean Jokes ๐Ÿ’‰"):
StreamLLMChatResponse(descriptions["Top Ten Funniest Clean Jokes ๐Ÿ’‰"])
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. ๐ŸŽ™๏ธ"):
StreamLLMChatResponse(descriptions["Top Ten Best Write a streamlit python program prompts to build AI programs. ๐ŸŽ™๏ธ"])
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>๐Ÿ”Š Read It Aloud</h1>
<textarea id="textArea" rows="10" cols="80">
'''
documentHTML5 = documentHTML5 + result
documentHTML5 = documentHTML5 + '''
</textarea>
<br>
<button onclick="readAloud()">๐Ÿ”Š Read Aloud</button>
</body>
</html>
'''
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|>", "</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}
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)]
# 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 ๐ŸŒŠ": "3 Q&A with emojis about types, facts, function, inputs and outputs of white blood cells ๐ŸŽฅ",
"CT Imaging๐Ÿฆ ": "3 Q&A with emojis on CT Imaging post surgery, how to, what to look for ๐Ÿ’Š",
"Hematoma ๐Ÿ’‰": "3 Q&A with emojis about hematoma and infection care and study including bacteria cultures and tests or labs๐Ÿ’ช",
"Post Surgery Wound Care ๐ŸŒ": "3 Q&A with emojis on wound care, and good bedside manner ๐Ÿฉธ",
"Healing and humor ๐Ÿ’Š": "3 Q&A with emojis on stories and humor about healing and caregiving ๐Ÿš‘",
"Psychology of bedside manner ๐Ÿงฌ": "3 Q&A with emojis on bedside manner and how to make patients feel at ease๐Ÿ› ",
"CT scan ๐Ÿ’Š": "3 Q&A with analysis on infection using CT scan and packing for skin, cellulitus and fascia ๐Ÿฉบ"
}
# Expander for medical topics
with st.expander("Medical Licensing Exam Topics ๐Ÿ“š", expanded=False):
st.markdown("๐Ÿฉบ **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 ๐Ÿ“š", expanded=False):
st.markdown("๐Ÿฉบ **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 ๐ŸŒŠ": "3 Questions and Answers with emojis about white blood cells ๐ŸŽฅ",
"CT Imaging๐Ÿฆ ": "3 Questions and Answers with emojis about CT Imaging of post surgery abscess, hematoma, and cerosanguiness fluid ๐Ÿ’Š",
"Hematoma ๐Ÿ’‰": "3 Questions and Answers with emojis about hematoma and infection and how heat helps white blood cells ๐Ÿ’ช",
"Post Surgery Wound Care ๐ŸŒ": "3 Questions and Answers with emojis about wound care and how to help as a caregiver๐Ÿฉธ",
"Healing and humor ๐Ÿ’Š": "3 Questions and Answers with emojis on the use of stories and humor to help patients and family ๐Ÿš‘",
"Psychology of bedside manner ๐Ÿงฌ": "3 Questions and Answers with emojis about good bedside manner ๐Ÿ› ",
"CT scan ๐Ÿ’Š": "3 Questions and Answers with analysis of bacteria and understanding infection using cultures and CT scan ๐Ÿฉบ"
}
# Create columns
col1, col2, col3, col4 = st.columns([1, 1, 1, 1], gap="small")
# Add buttons to columns
if col1.button("Ultrasound with Doppler ๐ŸŒŠ"):
StreamLLMChatResponse(descriptions["Ultrasound with Doppler ๐ŸŒŠ"])
if col2.button("Oseltamivir ๐Ÿฆ "):
StreamLLMChatResponse(descriptions["Oseltamivir ๐Ÿฆ "])
if col3.button("IM Epinephrine ๐Ÿ’‰"):
StreamLLMChatResponse(descriptions["IM Epinephrine ๐Ÿ’‰"])
if col4.button("Hypokalemia ๐ŸŒ"):
StreamLLMChatResponse(descriptions["Hypokalemia ๐ŸŒ"])
col5, col6, col7, col8 = st.columns([1, 1, 1, 1], gap="small")
if col5.button("Succinylcholine ๐Ÿ’Š"):
StreamLLMChatResponse(descriptions["Succinylcholine ๐Ÿ’Š"])
if col6.button("Phosphoinositol System ๐Ÿงฌ"):
StreamLLMChatResponse(descriptions["Phosphoinositol System ๐Ÿงฌ"])
if col7.button("Ramipril ๐Ÿ’Š"):
StreamLLMChatResponse(descriptions["Ramipril ๐Ÿ’Š"])
# 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 ๐Ÿ“š", 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("๐Ÿ‘๏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('๐Ÿ’ฌ 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("๐Ÿ—‘ 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("๐ŸŒ", 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("๐Ÿ“‚", key="open_"+file): # open emoji button
with open(file, 'r') as f:
file_contents = f.read()
next_action='open'
with col4:
if st.button("๐Ÿ”", key="read_"+file): # search emoji button
with open(file, 'r') as f:
file_contents = f.read()
next_action='search'
with col5:
if st.button("๐Ÿ—‘", 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 = '๐Ÿ”Run with Llama and GPT.'
if st.button(key='RunWithLlamaandGPT', label = buttonlabel):
user_prompt = file_contents
# Llama versus GPT Battle!
all=""
try:
st.write('๐Ÿ”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('๐Ÿ”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('๐Ÿ”Running with Llama and GPT.')
user_prompt = file_contents
# Llama versus GPT Battle!
all=""
try:
st.write('๐Ÿ”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('๐Ÿ”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)}">โฌ‡๏ธ 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("๐Ÿ—‘ 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("๐ŸŽต", 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("๐Ÿ—‘", 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 ๐Ÿ‘", expanded=False):
feedback = st.radio("Step 8: Give your feedback", ("๐Ÿ‘ Upvote", "๐Ÿ‘Ž Downvote"))
if feedback == "๐Ÿ‘ Upvote":
st.write("You upvoted ๐Ÿ‘. Thank you for your feedback!")
else:
st.write("You downvoted ๐Ÿ‘Ž. 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`.")
st.markdown('''
### Mixable Word Game AI ๐Ÿ“–โœจ๐Ÿ”
- **Unlock the Power of Words with Mixable Word Game AI:** Transform your vocabulary with an AI that brings words to life.
- **Capabilities:** Generates comprehensive glossaries and thrilling challenges.
- **Experience:** Your key to becoming a word wizard, enhancing your language skills.
- **Query Parameter Usage:** Enter a vocabulary term in the URL query, like `?q=Palindrome` or `?query=Anagram`, to explore new word game challenges.
''')
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("Word Games Glossary ๐ŸŽฒ")
display_glossary_grid(roleplaying_glossary)
st.title("๐ŸŽฒ๐Ÿ—บ๏ธ Word Game Universe")
st.markdown("## Explore the vast universes of Dungeons and Dragons, Call of Cthulhu, GURPS, and more through interactive storytelling and encyclopedic knowledge.๐ŸŒ ")
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()