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import base64 |
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import cv2 |
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import glob |
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import json |
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import math |
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import os |
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import pytz |
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import random |
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import re |
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import requests |
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import streamlit as st |
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import streamlit.components.v1 as components |
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import textract |
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import time |
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import zipfile |
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from audio_recorder_streamlit import audio_recorder |
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from bs4 import BeautifulSoup |
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from collections import deque |
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from datetime import datetime |
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from dotenv import load_dotenv |
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from gradio_client import Client |
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from huggingface_hub import InferenceClient |
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from io import BytesIO |
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from moviepy.editor import VideoFileClip |
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from PIL import Image |
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from PyPDF2 import PdfReader |
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from templates import bot_template, css, user_template |
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from urllib.parse import quote |
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from xml.etree import ElementTree as ET |
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import openai |
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from openai import OpenAI |
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Site_Name = 'Scholarly-Article-Document-Search-With-Memory' |
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title="🔬🧠ScienceBrain.AI" |
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helpURL='https://huggingface.co/awacke1' |
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bugURL='https://huggingface.co/spaces/awacke1' |
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icons='🔬' |
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st.set_page_config( |
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page_title=title, |
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page_icon=icons, |
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layout="wide", |
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initial_sidebar_state="auto", |
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menu_items={ |
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'Get Help': helpURL, |
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'Report a bug': bugURL, |
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'About': title |
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} |
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) |
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API_URL = 'https://qe55p8afio98s0u3.us-east-1.aws.endpoints.huggingface.cloud' |
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API_KEY = os.getenv('API_KEY') |
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MODEL1="meta-llama/Llama-2-7b-chat-hf" |
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MODEL1URL="https://huggingface.co/meta-llama/Llama-2-7b-chat-hf" |
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HF_KEY = os.getenv('HF_KEY') |
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headers = { |
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"Authorization": f"Bearer {HF_KEY}", |
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"Content-Type": "application/json" |
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} |
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key = os.getenv('OPENAI_API_KEY') |
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prompt = "...." |
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should_save = st.sidebar.checkbox("💾 Save", value=True, help="Save your session data.") |
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client = OpenAI(api_key= os.getenv('OPENAI_API_KEY'), organization=os.getenv('OPENAI_ORG_ID')) |
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MODEL = "gpt-4o-2024-05-13" |
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if "openai_model" not in st.session_state: |
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st.session_state["openai_model"] = MODEL |
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if "messages" not in st.session_state: |
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st.session_state.messages = [] |
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if st.button("Clear Session"): |
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st.session_state.messages = [] |
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@st.cache_resource |
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def SpeechSynthesis(result): |
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documentHTML5=''' |
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<!DOCTYPE html> |
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<html> |
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<head> |
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<title>Read It Aloud</title> |
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<script type="text/javascript"> |
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function readAloud() { |
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const text = document.getElementById("textArea").value; |
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const speech = new SpeechSynthesisUtterance(text); |
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window.speechSynthesis.speak(speech); |
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} |
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</script> |
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</head> |
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<body> |
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<h1>🔊 Read It Aloud</h1> |
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<textarea id="textArea" rows="10" cols="80"> |
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''' |
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documentHTML5 = documentHTML5 + result |
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documentHTML5 = documentHTML5 + ''' |
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</textarea> |
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<br> |
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<button onclick="readAloud()">🔊 Read Aloud</button> |
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</body> |
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</html> |
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''' |
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components.html(documentHTML5, width=1280, height=300) |
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openai.api_key = os.getenv('OPENAI_API_KEY') |
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openai.organization = os.getenv('OPENAI_ORG_ID') |
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client = OpenAI(api_key= os.getenv('OPENAI_API_KEY'), organization=os.getenv('OPENAI_ORG_ID')) |
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MODEL = "gpt-4o-2024-05-13" |
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def generate_filename(prompt, file_type): |
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central = pytz.timezone('US/Central') |
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safe_date_time = datetime.now(central).strftime("%m%d_%H%M") |
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replaced_prompt = prompt.replace(" ", "_").replace("\n", "_") |
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safe_prompt = "".join(x for x in replaced_prompt if x.isalnum() or x == "_")[:240] |
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return f"{safe_date_time}_{safe_prompt}.{file_type}" |
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def process_text(text_input): |
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if text_input: |
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st.session_state.messages.append({"role": "user", "content": text_input}) |
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with st.chat_message("user"): |
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st.markdown(text_input) |
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with st.chat_message("assistant"): |
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completion = client.chat.completions.create( |
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model=MODEL, |
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messages=[ |
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{"role": m["role"], "content": m["content"]} |
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for m in st.session_state.messages |
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], |
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stream=False |
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) |
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return_text = completion.choices[0].message.content |
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st.write("Assistant: " + return_text) |
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filename = generate_filename(text_input, "md") |
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create_file(filename, text_input, return_text, should_save) |
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st.session_state.messages.append({"role": "assistant", "content": return_text}) |
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def create_file(filename, prompt, response, is_image=False): |
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with open(filename, "w", encoding="utf-8") as f: |
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f.write(prompt + "\n\n" + response) |
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def save_image_old2(image, filename): |
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with open(filename, "wb") as f: |
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f.write(image.getbuffer()) |
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def save_image(image, filename): |
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max_filename_length = 250 |
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filename_stem, extension = os.path.splitext(filename) |
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truncated_stem = filename_stem[:max_filename_length - len(extension)] if len(filename) > max_filename_length else filename_stem |
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filename = f"{truncated_stem}{extension}" |
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with open(filename, "wb") as f: |
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f.write(image.getbuffer()) |
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return filename |
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def extract_boldface_terms(text): |
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return re.findall(r'\*\*(.*?)\*\*', text) |
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def extract_title(text): |
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boldface_terms = re.findall(r'\*\*(.*?)\*\*', text) |
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if boldface_terms: |
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title = ' '.join(boldface_terms) |
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else: |
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title = re.sub(r'[^a-zA-Z0-9_\-]', ' ', text[-200:]) |
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return title[-200:] |
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def process_image(image_input, user_prompt): |
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if image_input: |
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st.markdown('Processing image: ' + image_input.name ) |
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if image_input: |
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base64_image = base64.b64encode(image_input.read()).decode("utf-8") |
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response = client.chat.completions.create( |
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model=MODEL, |
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messages=[ |
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{"role": "system", "content": "You are a helpful assistant that responds in Markdown."}, |
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{"role": "user", "content": [ |
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{"type": "text", "text": user_prompt}, |
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{"type": "image_url", "image_url": { |
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"url": f"data:image/png;base64,{base64_image}"} |
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} |
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]} |
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], |
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temperature=0.0, |
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) |
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image_response = response.choices[0].message.content |
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st.markdown(image_response) |
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filename_md = generate_filename(image_input.name + '- ' + image_response, "md") |
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filename_png = filename_md.replace('.md', '.' + image_input.name.split('.')[-1]) |
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create_file(filename_md, image_response, '', True) |
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with open(filename_md, "w", encoding="utf-8") as f: |
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f.write(image_response) |
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boldface_terms = extract_title(image_response).replace(':','') |
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filename_stem, extension = os.path.splitext(image_input.name) |
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filename_img = f"{filename_stem} {''.join(boldface_terms)}{extension}" |
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newfilename = save_image(image_input, filename_img) |
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filename_md = newfilename.replace('.png', '.md') |
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create_file(filename_md, '', image_response, True) |
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return image_response |
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def create_audio_file(filename, audio_data, should_save): |
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if should_save: |
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with open(filename, "wb") as file: |
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file.write(audio_data.getvalue()) |
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st.success(f"Audio file saved as {filename}") |
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else: |
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st.warning("Audio file not saved.") |
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def process_audio(audio_input, text_input): |
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if audio_input: |
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transcription = client.audio.transcriptions.create( |
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model="whisper-1", |
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file=audio_input, |
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) |
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st.session_state.messages.append({"role": "user", "content": transcription.text}) |
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with st.chat_message("assistant"): |
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st.markdown(transcription.text) |
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SpeechSynthesis(transcription.text) |
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filename = generate_filename(transcription.text, "wav") |
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create_audio_file(filename, audio_input, should_save) |
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filename = generate_filename(transcription.text, "md") |
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create_file(filename, transcription.text, transcription.text, should_save) |
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def process_audio_for_video(video_input): |
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if video_input: |
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try: |
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transcription = client.audio.transcriptions.create( |
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model="whisper-1", |
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file=video_input, |
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) |
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response = client.chat.completions.create( |
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model=MODEL, |
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messages=[ |
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{"role": "system", "content":"""You are generating a transcript summary. Create a summary of the provided transcription. Respond in Markdown."""}, |
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{"role": "user", "content": [{"type": "text", "text": f"The audio transcription is: {transcription}"}],} |
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], |
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temperature=0, |
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) |
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st.markdown(response.choices[0].message.content) |
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return response.choices[0].message.content |
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except: |
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st.write('No transcript') |
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def save_video(video_file): |
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with open(video_file.name, "wb") as f: |
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f.write(video_file.getbuffer()) |
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return video_file.name |
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def process_video(video_path, seconds_per_frame=2): |
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base64Frames = [] |
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base_video_path, _ = os.path.splitext(video_path) |
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video = cv2.VideoCapture(video_path) |
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total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT)) |
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fps = video.get(cv2.CAP_PROP_FPS) |
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frames_to_skip = int(fps * seconds_per_frame) |
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curr_frame = 0 |
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while curr_frame < total_frames - 1: |
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video.set(cv2.CAP_PROP_POS_FRAMES, curr_frame) |
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success, frame = video.read() |
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if not success: |
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break |
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_, buffer = cv2.imencode(".jpg", frame) |
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base64Frames.append(base64.b64encode(buffer).decode("utf-8")) |
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curr_frame += frames_to_skip |
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video.release() |
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audio_path = f"{base_video_path}.mp3" |
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try: |
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clip = VideoFileClip(video_path) |
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clip.audio.write_audiofile(audio_path, bitrate="32k") |
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clip.audio.close() |
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clip.close() |
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except: |
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st.write('No audio track found, moving on..') |
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print(f"Extracted {len(base64Frames)} frames") |
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print(f"Extracted audio to {audio_path}") |
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return base64Frames, audio_path |
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def process_audio_and_video(video_input): |
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if video_input is not None: |
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video_path = save_video(video_input ) |
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base64Frames, audio_path = process_video(video_path, seconds_per_frame=1) |
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transcript = process_audio_for_video(video_input) |
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response = client.chat.completions.create( |
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model=MODEL, |
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messages=[ |
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{"role": "system", "content": """You are generating a video summary. Create a summary of the provided video and its transcript. Respond in Markdown"""}, |
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{"role": "user", "content": [ |
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"These are the frames from the video.", |
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*map(lambda x: {"type": "image_url", |
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"image_url": {"url": f'data:image/jpg;base64,{x}', "detail": "low"}}, base64Frames), |
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{"type": "text", "text": f"The audio transcription is: {transcript}"} |
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]}, |
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], |
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temperature=0, |
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) |
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results = response.choices[0].message.content |
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st.markdown(results) |
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if transcript: |
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filename = generate_filename(transcript, "md") |
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create_file(filename, transcript, results, should_save) |
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def search_glossary(query): |
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all="" |
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st.markdown(f"- {query}") |
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client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern") |
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response2 = client.predict( |
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query, |
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"google/gemma-7b-it", |
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True, |
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api_name="/ask_llm" |
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) |
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st.write('🔍Run of Multi-Agent System Paper Summary Spec is Complete') |
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st.markdown(response2) |
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client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern") |
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response1 = client.predict( |
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query, |
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10, |
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"Semantic Search - up to 10 Mar 2024", |
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"mistralai/Mixtral-8x7B-Instruct-v0.1", |
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api_name="/update_with_rag_md" |
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) |
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st.write('🔍Run of Multi-Agent System Paper References is Complete') |
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responseall = response2 + response1[0] + response1[1] |
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st.markdown(responseall) |
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return responseall |
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def parse_to_markdown(text): |
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return text |
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def load_file(file_name): |
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with open(file_name, "r", encoding='utf-8') as file: |
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content = file.read() |
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return content |
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def extract_urls(text): |
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try: |
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date_pattern = re.compile(r'### (\d{2} \w{3} \d{4})') |
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abs_link_pattern = re.compile(r'\[(.*?)\]\((https://arxiv\.org/abs/\d+\.\d+)\)') |
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pdf_link_pattern = re.compile(r'\[⬇️\]\((https://arxiv\.org/pdf/\d+\.\d+)\)') |
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title_pattern = re.compile(r'### \d{2} \w{3} \d{4} \| \[(.*?)\]') |
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date_matches = date_pattern.findall(text) |
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abs_link_matches = abs_link_pattern.findall(text) |
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pdf_link_matches = pdf_link_pattern.findall(text) |
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title_matches = title_pattern.findall(text) |
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markdown_text = "" |
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for i in range(len(date_matches)): |
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date = date_matches[i] |
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title = title_matches[i] |
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abs_link = abs_link_matches[i][1] |
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pdf_link = pdf_link_matches[i] |
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markdown_text += f"**Date:** {date}\n\n" |
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markdown_text += f"**Title:** {title}\n\n" |
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markdown_text += f"**Abstract Link:** [{abs_link}]({abs_link})\n\n" |
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markdown_text += f"**PDF Link:** [{pdf_link}]({pdf_link})\n\n" |
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markdown_text += "---\n\n" |
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return markdown_text |
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except: |
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st.write('.') |
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return '' |
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def download_pdfs(urls): |
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local_files = [] |
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for url in urls: |
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if url.endswith('.pdf'): |
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local_filename = url.split('/')[-1] |
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response = requests.get(url) |
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with open(local_filename, 'wb') as f: |
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f.write(response.content) |
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local_files.append(local_filename) |
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return local_files |
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def generate_html(local_files): |
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html = "<ul>" |
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for file in local_files: |
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link = f'<li><a href="{file}">{file}</a></li>' |
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html += link |
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html += "</ul>" |
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return html |
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def search_arxiv(query): |
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start_time = time.strftime("%Y-%m-%d %H:%M:%S") |
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client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern") |
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response1 = client.predict( |
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query, |
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20, |
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"Semantic Search - up to 10 Mar 2024", |
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"mistralai/Mixtral-8x7B-Instruct-v0.1", |
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api_name="/update_with_rag_md" |
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) |
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Question = '### 🔎 ' + query + '\r\n' |
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References = response1[0] |
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ReferenceLinks = extract_urls(References) |
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RunSecondQuery = True |
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results='' |
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if RunSecondQuery: |
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response2 = client.predict( |
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query, |
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"mistralai/Mixtral-8x7B-Instruct-v0.1", |
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True, |
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api_name="/ask_llm" |
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) |
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if len(response2) > 10: |
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Answer = response2 |
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SpeechSynthesis(Answer) |
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results = Question + '\r\n' + Answer + '\r\n' + References + '\r\n' + ReferenceLinks |
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st.markdown(results) |
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st.write('🔍Run of Multi-Agent System Paper Summary Spec is Complete') |
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end_time = time.strftime("%Y-%m-%d %H:%M:%S") |
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start_timestamp = time.mktime(time.strptime(start_time, "%Y-%m-%d %H:%M:%S")) |
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end_timestamp = time.mktime(time.strptime(end_time, "%Y-%m-%d %H:%M:%S")) |
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elapsed_seconds = end_timestamp - start_timestamp |
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st.write(f"Start time: {start_time}") |
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st.write(f"Finish time: {end_time}") |
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st.write(f"Elapsed time: {elapsed_seconds:.2f} seconds") |
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filename = generate_filename(query, "md") |
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create_file(filename, query, results, should_save) |
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return results |
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def download_pdfs_and_generate_html(urls): |
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pdf_links = [] |
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for url in urls: |
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if url.endswith('.pdf'): |
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pdf_filename = os.path.basename(url) |
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download_pdf(url, pdf_filename) |
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pdf_links.append(pdf_filename) |
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local_links_html = '<ul>' |
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for link in pdf_links: |
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local_links_html += f'<li><a href="{link}">{link}</a></li>' |
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local_links_html += '</ul>' |
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return local_links_html |
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|
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def download_pdf(url, filename): |
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response = requests.get(url) |
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with open(filename, 'wb') as file: |
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file.write(response.content) |
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|
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|
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PromptPrefix = 'Create a specification with streamlit functions creating markdown outlines and tables rich with appropriate emojis for methodical step by step rules defining the concepts at play. Use story structure architect rules to plan, structure and write three dramatic situations to include in the rules and how to play by matching the theme for topic of ' |
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PromptPrefix2 = 'Create a streamlit python user app with full code listing to create a UI implementing the using streamlit, gradio, huggingface to create user interface elements like emoji buttons, sliders, drop downs, and data interfaces like dataframes to show tables, session_statematching this ruleset and thematic story plot line: ' |
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PromptPrefix3 = 'Create a HTML5 aframe and javascript app using appropriate libraries to create a word game simulation with advanced libraries like aframe to render 3d scenes creating moving entities that stay within a bounding box but show text and animation in 3d for inventory, components and story entities. Show full code listing. Add a list of new random entities say 3 of a few different types to any list appropriately and use emojis to make things easier and fun to read. Use appropriate emojis in labels. Create the UI to implement storytelling in the style of a dungeon master, with features using three emoji appropriate text plot twists and recurring interesting funny fascinating and complex almost poetic 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:' |
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roleplaying_glossary = { |
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"🤖 AI Concepts": { |
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"MoE (Mixture of Experts) 🧠": [ |
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"As a leading AI health researcher, provide an overview of MoE, MAS, memory, and mirroring in healthcare applications.", |
|
"Explain how MoE and MAS can be leveraged to create AGI and AMI systems for healthcare, as an AI architect.", |
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"Discuss the key concepts, benefits, and challenges of self-rewarding AI in healthcare, as an expert.", |
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"Identify the top 3 pain points that MoE addresses in AI and healthcare, such as complexity and resource allocation.", |
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"Describe the top 3 joys of the MoE solution, including improved performance and adaptability in healthcare AI.", |
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"Highlight the top 3 superpowers MoE gives users, like tackling complex problems and personalizing interventions.", |
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"Identify the top 3 problems MoE solves in AI and healthcare, such as model complexity, lack of specialization, and inefficient resource allocation, and explain how it addresses each problem effectively.", |
|
"Outline the 3 essential method steps required for implementing MoE in AI systems, highlighting the novelty and significance of each step in advancing healthcare applications.", |
|
"Discuss the innovative aspects of the MoE method steps and how they differ from traditional approaches, contributing to advancements in AI and healthcare.", |
|
"Propose 3 creative ways to structure MoE-based projects and collaborations to optimize performance, efficiency, and impact in healthcare AI applications." |
|
], |
|
"Multi Agent Systems (MAS) 🤝": [ |
|
"As a renowned MAS researcher, describe the key characteristics of distributed, autonomous, and cooperative MAS.", |
|
"Discuss how MAS is applied in robotics, simulations, and decentralized problem-solving, as an AI engineer.", |
|
"Provide insights into future trends and breakthroughs in MAS research and applications, as a thought leader.", |
|
"Identify the top 3 pain points MAS addresses in complex environments, such as coordination and adaptability.", |
|
"Describe the top 3 joys of the MAS solution, including enhanced collaboration and emergent behaviors in AI.", |
|
"Highlight the top 3 superpowers MAS gives users, like modeling complex systems and building resilient applications.", |
|
"Identify the top 3 problems MAS solves in complex, distributed environments, such as lack of coordination, limited adaptability, and centralized control, and explain how it addresses each problem effectively.", |
|
"Outline the 3 essential method steps required for designing and implementing MAS, highlighting the novelty and significance of each step in advancing AI applications.", |
|
"Discuss the innovative aspects of the MAS method steps and how they differ from traditional approaches, contributing to advancements in distributed AI systems.", |
|
"Propose 3 creative ways to structure MAS-based projects and collaborations to optimize performance, efficiency, and impact in various AI domains." |
|
], |
|
"Self Rewarding AI 🎁": [ |
|
"As a leading expert, discuss the main research areas in developing AI with intrinsic motivation and goal-setting.", |
|
"Explain how self-rewarding AI enables open-ended development and adaptability, as a curiosity-driven researcher.", |
|
"Share your vision for the future of AI systems that autonomously set goals, learn, and adapt, as a pioneer.", |
|
"Identify the top 3 pain points self-rewarding AI addresses, such as lack of motivation and limited adaptability.", |
|
"Describe the top 3 joys of the self-rewarding AI solution, including autonomous learning and novel solutions.", |
|
"Highlight the top 3 superpowers self-rewarding AI gives users, like creating continuously improving AI systems.", |
|
"Identify the top 3 problems self-rewarding AI solves in current AI systems, such as lack of intrinsic motivation, limited adaptability, and reliance on external rewards, and explain how it addresses each problem effectively.", |
|
"Outline the 3 essential method steps required for developing self-rewarding AI systems, highlighting the novelty and significance of each step in advancing autonomous AI.", |
|
"Discuss the innovative aspects of the self-rewarding AI method steps and how they differ from traditional approaches, contributing to advancements in open-ended AI development.", |
|
"Propose 3 creative ways to structure self-rewarding AI projects and collaborations to optimize performance, efficiency, and impact in creating adaptive and self-motivated AI systems." |
|
] |
|
}, |
|
"🛠️ AI Tools & Platforms": { |
|
"ChatDev 💬": [ |
|
"As a chatbot developer, ask about the features and capabilities ChatDev offers for building conversational AI.", |
|
"Inquire about the pre-built assets, integrations, and multi-platform support in ChatDev, as a product manager.", |
|
"Ask how ChatDev facilitates chatbot development, deployment, and analytics across channels, as a business owner.", |
|
"Identify the top 3 challenges ChatDev helps overcome in chatbot development, such as customization and management.", |
|
"Outline the top 3 essential method steps in building chatbots with ChatDev, emphasizing novelty and efficiency.", |
|
"Propose 3 innovative ways to structure chatbot projects using ChatDev for optimizing speed, engagement, and deployment.", |
|
"Identify the top 3 problems ChatDev solves in chatbot development, such as limited customization, lack of multi-platform support, and difficulty in managing conversational flows, and explain how it addresses each problem effectively.", |
|
"Outline the 3 essential method steps required for building chatbots using ChatDev, highlighting the novelty and significance of each step in streamlining the development process.", |
|
"Discuss the innovative aspects of the ChatDev method steps and how they differ from traditional approaches, contributing to advancements in conversational AI development.", |
|
"Propose 3 creative ways to structure chatbot projects using ChatDev to optimize performance, efficiency, and impact in creating engaging and multi-platform conversational experiences." |
|
], |
|
"Online Multiplayer Experiences 🌐": [ |
|
"As a game developer, explore the potential of online multiplayer experiences, including games, AR, and VR.", |
|
"Discuss the future of image and video models in enhancing online multiplayer experiences, as a researcher.", |
|
"Inquire about the challenges and opportunities in creating immersive and interactive online multiplayer environments.", |
|
"Identify the top 3 problems online multiplayer experiences solve, such as limited social interaction, lack of realism, and difficulty in creating engaging content, and explain how they address each problem effectively.", |
|
"Outline the 3 essential method steps required for developing cutting-edge online multiplayer experiences, highlighting the novelty and significance of each step in advancing gaming, AR, and VR.", |
|
"Discuss the innovative aspects of online multiplayer experience development and how they differ from traditional approaches, contributing to advancements in immersive technologies.", |
|
"Propose 3 creative ways to structure online multiplayer projects and collaborations to optimize performance, efficiency, and impact in creating captivating and socially engaging experiences.", |
|
"Explore the potential of integrating AI and machine learning techniques in online multiplayer experiences to enhance player interactions, generate dynamic content, and personalize experiences.", |
|
"Discuss the ethical considerations and challenges in developing online multiplayer experiences, such as ensuring fair play, protecting user privacy, and moderating user-generated content.", |
|
"Identify the key trends and future directions in online multiplayer experiences, considering advancements in AI, AR, VR, and cloud computing technologies." |
|
] |
|
}, |
|
"🔬 Science Topics": { |
|
"Physics 🔭": [ |
|
"As a Physics student, ask about the main branches and research areas in Physics and their interconnections.", |
|
"Discuss the current state and future directions of Astrophysics research, as a researcher in the field.", |
|
"Explain how General Relativity, Quantum Cosmology, and Mathematical Physics interrelate, as a theorist.", |
|
"Identify the top 3 fundamental questions in Physics that recent research aims to answer and their implications.", |
|
"Outline the top 3 essential method steps in conducting cutting-edge Physics research, emphasizing novelty.", |
|
"Propose 3 innovative ways to structure research collaborations in Physics for interdisciplinary breakthroughs.", |
|
"Identify the top 3 problems physics research solves, such as understanding fundamental laws, resolving theory inconsistencies, and exploring the universe's origins, and explain how it addresses each problem effectively.", |
|
"Outline the 3 essential method steps required for conducting cutting-edge physics research, highlighting the novelty and significance of each step in advancing our understanding of the universe.", |
|
"Discuss the innovative aspects of the physics research method steps and how they differ from traditional approaches, contributing to advancements in the field.", |
|
"Propose 3 creative ways to structure physics research projects and collaborations to optimize performance, efficiency, and impact in making groundbreaking discoveries." |
|
], |
|
"Mathematics ➗": [ |
|
"As a Mathematics enthusiast, inquire about the main branches of Mathematics and their key research areas.", |
|
"Ask about the main branches of pure Mathematics, like Algebra and Geometry, and their fundamental concepts.", |
|
"Discuss how Probability, Statistics, and Applied Math relate to other Mathematical fields, as an applied mathematician.", |
|
"Identify the top 3 unsolved problems in Mathematics that researchers are actively working on and their significance.", |
|
"Describe the top 3 core method steps in advancing mathematical research, highlighting novelty and creativity.", |
|
"Suggest 3 innovative ways to structure mathematical research and collaborations for discoveries and applications.", |
|
"Identify the top 3 problems mathematics research solves, such as proving theorems, developing new tools, and finding real-world applications, and explain how it addresses each problem effectively.", |
|
"Outline the 3 essential method steps required for advancing mathematical research, highlighting the novelty and significance of each step in expanding mathematical knowledge.", |
|
"Discuss the innovative aspects of the mathematical research method steps and how they differ from traditional approaches, contributing to advancements in the field.", |
|
"Propose 3 creative ways to structure mathematical research projects and collaborations to optimize performance, efficiency, and impact in making novel discoveries and finding interdisciplinary applications." |
|
], |
|
"Computer Science 💻": [ |
|
"As a Computer Science student, ask about the main research areas shaping the future of computing.", |
|
"Discuss the major research topics in AI, ML, NLP, Vision, Graphics, and Robotics, as an AI researcher.", |
|
"Inquire about the interconnections between Algorithms, Data Structures, Databases, and Programming Languages.", |
|
"Identify the top 3 critical challenges in Computer Science that current research aims to address and approaches.", |
|
"Outline the top 3 essential method steps in conducting groundbreaking Computer Science research, emphasizing novelty.", |
|
"Propose 3 creative ways to structure research projects in Computer Science for innovation and real-world applications.", |
|
"Identify the top 3 problems computer science research solves, such as developing efficient algorithms, building secure systems, and advancing AI and machine learning, and explain how it addresses each problem effectively.", |
|
"Outline the 3 essential method steps required for conducting groundbreaking computer science research, highlighting the novelty and significance of each step in pushing the boundaries of computing.", |
|
"Discuss the innovative aspects of the computer science research method steps and how they differ from traditional approaches, contributing to advancements in the field.", |
|
"Propose 3 creative ways to structure computer science research projects and collaborations to optimize performance, efficiency, and impact in driving innovation and solving real-world problems." |
|
] |
|
} |
|
} |
|
|
|
@st.cache_resource |
|
def display_glossary_entity(k): |
|
search_urls = { |
|
"🚀🌌ArXiv": lambda k: f"/?q={quote(k)}", |
|
"🃏Analyst": lambda k: f"/?q={quote(k)}-{quote(PromptPrefix)}", |
|
"📚PyCoder": lambda k: f"/?q={quote(k)}-{quote(PromptPrefix2)}", |
|
"🔬JSCoder": lambda k: f"/?q={quote(k)}-{quote(PromptPrefix3)}", |
|
"📖": lambda k: f"https://en.wikipedia.org/wiki/{quote(k)}", |
|
"🔍": lambda k: f"https://www.google.com/search?q={quote(k)}", |
|
"🔎": lambda k: f"https://www.bing.com/search?q={quote(k)}", |
|
"🎥": lambda k: f"https://www.youtube.com/results?search_query={quote(k)}", |
|
"🐦": lambda k: f"https://twitter.com/search?q={quote(k)}", |
|
} |
|
links_md = ' '.join([f"[{emoji}]({url(k)})" for emoji, url in search_urls.items()]) |
|
|
|
st.markdown(f"**{k}** <small>{links_md}</small>", unsafe_allow_html=True) |
|
|
|
|
|
@st.cache_resource |
|
def display_glossary_grid(roleplaying_glossary): |
|
search_urls = { |
|
"🚀🌌ArXiv": lambda k: f"/?q={quote(k)}", |
|
"🃏Analyst": lambda k: f"/?q={quote(k)}-{quote(PromptPrefix)}", |
|
"📚PyCoder": lambda k: f"/?q={quote(k)}-{quote(PromptPrefix2)}", |
|
"🔬JSCoder": lambda k: f"/?q={quote(k)}-{quote(PromptPrefix3)}", |
|
"📖": lambda k: f"https://en.wikipedia.org/wiki/{quote(k)}", |
|
"🔍": lambda k: f"https://www.google.com/search?q={quote(k)}", |
|
"▶️": lambda k: f"https://www.youtube.com/results?search_query={quote(k)}", |
|
"🔎": lambda k: f"https://www.bing.com/search?q={quote(k)}", |
|
"🎥": lambda k: f"https://www.youtube.com/results?search_query={quote(k)}", |
|
"🐦": lambda k: f"https://twitter.com/search?q={quote(k)}", |
|
} |
|
|
|
for category, details in roleplaying_glossary.items(): |
|
st.write(f"### {category}") |
|
cols = st.columns(len(details)) |
|
|
|
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}** <small>{links_md}</small>", unsafe_allow_html=True) |
|
|
|
|
|
|
|
def process_text2(MODEL='gpt-4o-2024-05-13', text_input='What is 2+2 and what is an imaginary number'): |
|
if text_input: |
|
completion = client.chat.completions.create( |
|
model=MODEL, |
|
messages=st.session_state.messages |
|
) |
|
return_text = completion.choices[0].message.content |
|
st.write("Assistant: " + return_text) |
|
filename = generate_filename(text_input, "md") |
|
create_file(filename, text_input, return_text, should_save) |
|
return return_text |
|
|
|
@st.cache_resource |
|
def get_table_download_link(file_path): |
|
|
|
try: |
|
|
|
|
|
with open(file_path, 'r', encoding='utf-8') 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] |
|
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' |
|
href = f'<a href="data:{mime_type};base64,{b64}" target="_blank" download="{file_name}">{file_name}</a>' |
|
return href |
|
except: |
|
return '' |
|
|
|
|
|
@st.cache_resource |
|
def create_zip_of_files(files): |
|
zip_name = "Arxiv-Paper-Search-QA-RAG-Streamlit-Gradio-AP.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 |
|
|
|
def get_file(): |
|
st.write(st.session_state['file']) |
|
|
|
def SaveFileTextClicked(): |
|
fileText = st.session_state.file_content_area |
|
fileName = st.session_state.file_name_input |
|
with open(fileName, 'w', encoding='utf-8') as file: |
|
file.write(fileText) |
|
st.markdown('Saved ' + fileName + '.') |
|
|
|
def SaveFileNameClicked(): |
|
newFileName = st.session_state.file_name_input |
|
oldFileName = st.session_state.filename |
|
if (newFileName!=oldFileName): |
|
os.rename(oldFileName, newFileName) |
|
st.markdown('Renamed file ' + oldFileName + ' to ' + newFileName + '.') |
|
newFileText = st.session_state.file_content_area |
|
oldFileText = st.session_state.filetext |
|
|
|
|
|
|
|
def compare_and_delete_files(files): |
|
if not files: |
|
st.warning("No files to compare.") |
|
return |
|
|
|
|
|
file_sizes = {} |
|
for file in files: |
|
size = os.path.getsize(file) |
|
if size in file_sizes: |
|
file_sizes[size].append(file) |
|
else: |
|
file_sizes[size] = [file] |
|
|
|
|
|
for size, paths in file_sizes.items(): |
|
if len(paths) > 1: |
|
latest_file = max(paths, key=os.path.getmtime) |
|
for file in paths: |
|
if file != latest_file: |
|
os.remove(file) |
|
st.success(f"Deleted {file} as a duplicate.") |
|
st.rerun() |
|
|
|
|
|
def get_file_size(file_path): |
|
return os.path.getsize(file_path) |
|
|
|
def FileSidebar(): |
|
|
|
|
|
all_files = glob.glob("*.md") |
|
all_files = [file for file in all_files if len(os.path.splitext(file)[0]) >= 10] |
|
all_files.sort(key=lambda x: (os.path.splitext(x)[1], x), reverse=True) |
|
|
|
|
|
Files1, Files2 = st.sidebar.columns(2) |
|
with Files1: |
|
if st.button("🗑 Delete All"): |
|
for file in all_files: |
|
os.remove(file) |
|
st.rerun() |
|
with Files2: |
|
if st.button("⬇️ Download"): |
|
zip_file = create_zip_of_files(all_files) |
|
st.sidebar.markdown(get_zip_download_link(zip_file), unsafe_allow_html=True) |
|
file_contents='' |
|
file_name='' |
|
next_action='' |
|
|
|
|
|
for file in all_files: |
|
col1, col2, col3, col4, col5 = st.sidebar.columns([1,6,1,1,1]) |
|
with col1: |
|
if st.button("🌐", key="md_"+file): |
|
file_contents = load_file(file) |
|
file_name=file |
|
next_action='md' |
|
st.session_state['next_action'] = next_action |
|
with col2: |
|
st.markdown(get_table_download_link(file), unsafe_allow_html=True) |
|
with col3: |
|
if st.button("📂", key="open_"+file): |
|
file_contents = load_file(file) |
|
file_name=file |
|
next_action='open' |
|
st.session_state['lastfilename'] = file |
|
st.session_state['filename'] = file |
|
st.session_state['filetext'] = file_contents |
|
st.session_state['next_action'] = next_action |
|
with col4: |
|
if st.button("▶️", key="read_"+file): |
|
file_contents = load_file(file) |
|
file_name=file |
|
next_action='search' |
|
st.session_state['next_action'] = next_action |
|
with col5: |
|
if st.button("🗑", key="delete_"+file): |
|
os.remove(file) |
|
file_name=file |
|
st.rerun() |
|
next_action='delete' |
|
st.session_state['next_action'] = next_action |
|
|
|
|
|
|
|
file_sizes = [get_file_size(file) for file in all_files] |
|
previous_size = None |
|
st.sidebar.title("File Operations") |
|
for file, size in zip(all_files, file_sizes): |
|
duplicate_flag = "🚩" if size == previous_size else "" |
|
with st.sidebar.expander(f"File: {file} {duplicate_flag}"): |
|
st.text(f"Size: {size} bytes") |
|
|
|
if st.button("View", key=f"view_{file}"): |
|
try: |
|
with open(file, "r", encoding='utf-8') as f: |
|
file_content = f.read() |
|
st.code(file_content, language="markdown") |
|
except UnicodeDecodeError: |
|
st.error("Failed to decode the file with UTF-8. It might contain non-UTF-8 encoded characters.") |
|
|
|
if st.button("Delete", key=f"delete3_{file}"): |
|
os.remove(file) |
|
st.rerun() |
|
previous_size = size |
|
|
|
if len(file_contents) > 0: |
|
if next_action=='open': |
|
if 'lastfilename' not in st.session_state: |
|
st.session_state['lastfilename'] = '' |
|
if 'filename' not in st.session_state: |
|
st.session_state['filename'] = '' |
|
if 'filetext' not in st.session_state: |
|
st.session_state['filetext'] = '' |
|
open1, open2 = st.columns(spec=[.8,.2]) |
|
|
|
with open1: |
|
|
|
file_name_input = st.text_input(key='file_name_input', on_change=SaveFileNameClicked, label="File Name:",value=file_name ) |
|
file_content_area = st.text_area(key='file_content_area', on_change=SaveFileTextClicked, label="File Contents:", value=file_contents, height=300) |
|
|
|
ShowButtons = False |
|
if ShowButtons: |
|
bp1,bp2 = st.columns([.5,.5]) |
|
with bp1: |
|
if st.button(label='💾 Save Name'): |
|
SaveFileNameClicked() |
|
with bp2: |
|
if st.button(label='💾 Save File'): |
|
SaveFileTextClicked() |
|
|
|
new_file_content_area = st.session_state['file_content_area'] |
|
if new_file_content_area != file_contents: |
|
st.markdown(new_file_content_area) |
|
|
|
if next_action=='search': |
|
filesearch = PromptPrefix + file_contents |
|
st.markdown(filesearch) |
|
process_text(filesearch) |
|
|
|
if next_action=='md': |
|
st.markdown(file_contents) |
|
SpeechSynthesis(file_contents) |
|
|
|
buttonlabel = '🔍Run' |
|
if st.button(key='Runmd', label = buttonlabel): |
|
MODEL = "gpt-4o-2024-05-13" |
|
openai.api_key = os.getenv('OPENAI_API_KEY') |
|
openai.organization = os.getenv('OPENAI_ORG_ID') |
|
client = OpenAI(api_key= os.getenv('OPENAI_API_KEY'), organization=os.getenv('OPENAI_ORG_ID')) |
|
st.session_state.messages.append({"role": "user", "content": transcript}) |
|
with st.chat_message("user"): |
|
st.markdown(transcript) |
|
with st.chat_message("assistant"): |
|
completion = client.chat.completions.create( |
|
model=MODEL, |
|
messages = st.session_state.messages, |
|
stream=True |
|
) |
|
response = process_text2(text_input=prompt) |
|
st.session_state.messages.append({"role": "assistant", "content": response}) |
|
|
|
|
|
|
|
|
|
|
|
if next_action=='search': |
|
file_content_area = st.text_area("File Contents:", file_contents, height=500) |
|
user_prompt = file_contents |
|
|
|
|
|
filesearch = PromptPrefix2 + file_content_area |
|
st.markdown(filesearch) |
|
if st.button(key='rerun', label='🔍Re-Code' ): |
|
|
|
search_arxiv(filesearch) |
|
|
|
|
|
|
|
|
|
|
|
|
|
titles = [ |
|
"🧠🎭 Semantic Symphonies 🎹🎸 & Episodic Encores 🥁🎻", |
|
"🌌🎼 AI Rhythms 🎺🎷 of Memory Lane 🏰", |
|
"🎭🎉 Cognitive Crescendos 🎹💃 & Neural Harmonies 🎸🎤", |
|
"🧠🎺 Mnemonic Melodies 🎷 & Synaptic Grooves 🥁", |
|
"🎼🎸 Straight Outta Cognition ⚙️", |
|
"🥁🎻 Jazzy 🎷 Jambalaya 🍛 of AI Memories", |
|
"🏰 Semantic 🧠 Soul 🙌 & Episodic 📜 Essence", |
|
"🥁🎻 The Music Of AI's Mind 🧠🎭🎉" |
|
] |
|
selected_title = random.choice(titles) |
|
st.markdown(f"**{selected_title}**") |
|
|
|
FileSidebar() |
|
|
|
|
|
|
|
def get_image_as_base64(url): |
|
response = requests.get(url) |
|
if response.status_code == 200: |
|
|
|
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 |
|
|
|
@st.cache_resource |
|
def SideBarImageShuffle(): |
|
image_urls = [ |
|
"https://cdn-uploads.huggingface.co/production/uploads/620630b603825909dcbeba35/cfhJIasuxLkT5fnaAE6Gj.png", |
|
"https://cdn-uploads.huggingface.co/production/uploads/620630b603825909dcbeba35/UMo4oWNrrd6RLLzsFxQAi.png", |
|
"https://cdn-uploads.huggingface.co/production/uploads/620630b603825909dcbeba35/o_EH4cTs5Qxiu7xTZw9I3.png", |
|
"https://cdn-uploads.huggingface.co/production/uploads/620630b603825909dcbeba35/cmCZ5RTdSx3usMm7MwwWK.png", |
|
] |
|
|
|
selected_image_url = random.choice(image_urls) |
|
selected_image_base64 = get_image_as_base64(selected_image_url) |
|
if selected_image_base64 is not None: |
|
with st.sidebar: |
|
st.markdown(f"") |
|
else: |
|
st.sidebar.write("Failed to load the image.") |
|
|
|
ShowSideImages=False |
|
if ShowSideImages: |
|
SideBarImageShuffle() |
|
|
|
|
|
|
|
|
|
|
|
|
|
score_dir = "scores" |
|
os.makedirs(score_dir, exist_ok=True) |
|
|
|
|
|
def generate_key(label, header, idx): |
|
return f"{header}_{label}_{idx}_key" |
|
|
|
|
|
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"] += increment |
|
score_data["score"] += increment |
|
with open(score_file, "w") as file: |
|
json.dump(score_data, file) |
|
return score_data["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 display_glossary(glossary, area): |
|
if area in glossary: |
|
st.subheader(f"📘 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}") |
|
|
|
|
|
|
|
|
|
def display_videos_and_links(num_columns): |
|
video_files = [f for f in os.listdir('.') if f.endswith('.mp4')] |
|
if not video_files: |
|
st.write("No MP4 videos found in the current directory.") |
|
return |
|
|
|
video_files_sorted = sorted(video_files, key=lambda x: len(x.split('.')[0])) |
|
cols = st.columns(num_columns) |
|
col_index = 0 |
|
|
|
for video_file in video_files_sorted: |
|
with cols[col_index % num_columns]: |
|
|
|
|
|
|
|
k = video_file.split('.')[0] |
|
st.video(video_file, format='video/mp4', start_time=0) |
|
display_glossary_entity(k) |
|
col_index += 1 |
|
|
|
|
|
def display_images_and_wikipedia_summaries(num_columns=4): |
|
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 |
|
|
|
image_files_sorted = sorted(image_files, key=lambda x: len(x.split('.')[0])) |
|
|
|
cols = st.columns(num_columns) |
|
col_index = 0 |
|
|
|
for image_file in image_files_sorted: |
|
with cols[col_index % num_columns]: |
|
image = Image.open(image_file) |
|
st.image(image, caption=image_file, use_column_width=True) |
|
k = image_file.split('.')[0] |
|
display_glossary_entity(k) |
|
col_index += 1 |
|
|
|
|
|
def get_all_query_params(key): |
|
return st.query_params().get(key, []) |
|
|
|
def clear_query_params(): |
|
st.query_params() |
|
|
|
|
|
|
|
def display_content_or_image(query): |
|
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 |
|
image_dir = "images" |
|
image_path = f"{image_dir}/{query}.png" |
|
if os.path.exists(image_path): |
|
st.image(image_path, caption=f"Image for {query}") |
|
return True |
|
st.warning("No matching content or image found.") |
|
return False |
|
|
|
game_emojis = { |
|
"Dungeons and Dragons": "🐉", |
|
"Call of Cthulhu": "🐙", |
|
"GURPS": "🎲", |
|
"Pathfinder": "🗺️", |
|
"Kindred of the East": "🌅", |
|
"Changeling": "🍃", |
|
} |
|
|
|
topic_emojis = { |
|
"Core Rulebooks": "📚", |
|
"Maps & Settings": "🗺️", |
|
"Game Mechanics & Tools": "⚙️", |
|
"Monsters & Adversaries": "👹", |
|
"Campaigns & Adventures": "📜", |
|
"Creatives & Assets": "🎨", |
|
"Game Master Resources": "🛠️", |
|
"Lore & Background": "📖", |
|
"Character Development": "🧍", |
|
"Homebrew Content": "🔧", |
|
"General Topics": "🌍", |
|
} |
|
|
|
|
|
def display_buttons_with_scores(num_columns_text): |
|
for category, games in roleplaying_glossary.items(): |
|
category_emoji = topic_emojis.get(category, "🔍") |
|
st.markdown(f"## {category_emoji} {category}") |
|
for game, terms in games.items(): |
|
game_emoji = game_emojis.get(game, "🎮") |
|
for term in terms: |
|
key = f"{category}_{game}_{term}".replace(' ', '_').lower() |
|
score = load_score(key) |
|
if st.button(f"{game_emoji} {category} {game} {term} {score}", key=key): |
|
newscore = update_score(key.replace('?','')) |
|
query_prefix = f"{category_emoji} {game_emoji} ** {category} - {game} - {term} - **" |
|
st.markdown("Scored " + query_prefix + ' with score ' + str(newscore) + '.') |
|
|
|
|
|
def get_all_query_params(key): |
|
return st.query_params().get(key, []) |
|
|
|
def clear_query_params(): |
|
st.query_params() |
|
|
|
|
|
|
|
|
|
|
|
@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).') |
|
|
|
|
|
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}) |
|
|
|
|
|
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 |
|
|
|
|
|
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 |
|
|
|
|
|
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', encoding='utf-8') as file: |
|
|
|
|
|
file.write(response) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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") |
|
|
|
|
|
@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 "" |
|
|
|
|
|
|
|
@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 coder, inventor, and writer of quotes on wisdom as a helpful expert in all fields of health, math, development and AI using python.'}] |
|
conversation.append({'role': 'user', 'content': prompt}) |
|
if len(document_section)>0: |
|
conversation.append({'role': 'assistant', 'content': document_section}) |
|
start_time = time.time() |
|
report = [] |
|
res_box = st.empty() |
|
collected_chunks = [] |
|
collected_messages = [] |
|
|
|
for chunk in openai.ChatCompletion.create(model=model_choice, 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 |
|
|
|
|
|
@st.cache_resource |
|
def chat_with_model45(prompt, document_section='', model_choice='gpt-4-0125-preview'): |
|
model = model_choice |
|
conversation = [{'role': 'system', 'content': 'You are a coder, inventor, and writer of quotes on wisdom as a helpful expert in all fields of health, math, development and AI using python.'}] |
|
conversation.append({'role': 'user', 'content': prompt}) |
|
if len(document_section)>0: |
|
conversation.append({'role': 'assistant', 'content': document_section}) |
|
start_time = time.time() |
|
report = [] |
|
res_box = st.empty() |
|
collected_chunks = [] |
|
collected_messages = [] |
|
|
|
for chunk in openai.ChatCompletion.create(model=model_choice, 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 |
|
|
|
@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): |
|
|
|
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}") |
|
|
|
|
|
@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() |
|
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) |
|
|
|
|
|
@st.cache_resource |
|
def vector_store(text_chunks): |
|
embeddings = OpenAIEmbeddings(openai_api_key=key) |
|
return FAISS.from_texts(texts=text_chunks, embedding=embeddings) |
|
|
|
|
|
@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 |
|
|
|
|
|
|
|
API_URL_IE = f'https://tonpixzfvq3791u9.us-east-1.aws.endpoints.huggingface.cloud' |
|
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" |
|
HF_KEY = st.secrets['HF_KEY'] |
|
headers = { |
|
"Authorization": f"Bearer {HF_KEY}", |
|
"Content-Type": "audio/wav" |
|
} |
|
|
|
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}" |
|
|
|
|
|
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 |
|
|
|
|
|
def transcribe_audio(filename): |
|
output = query(filename) |
|
return output |
|
|
|
|
|
|
|
def StreamMedChatResponse(topic): |
|
st.write(f"Showing resources or questions related to: {topic}") |
|
|
|
|
|
def get_base64_encoded_file(file_path): |
|
with open(file_path, "rb") as file: |
|
return base64.b64encode(file.read()).decode() |
|
|
|
|
|
def get_audio_download_link(file_path): |
|
base64_file = get_base64_encoded_file(file_path) |
|
return f'<a href="data:file/wav;base64,{base64_file}" download="{os.path.basename(file_path)}">⬇️ Download Audio</a>' |
|
|
|
|
|
|
|
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|
|
|
GiveFeedback=False |
|
if GiveFeedback: |
|
with st.expander("Give your feedback 👍", expanded=False): |
|
feedback = st.radio("Step 8: Give your feedback", ("👍 Upvote", "👎 Downvote")) |
|
if feedback == "👍 Upvote": |
|
st.write("You upvoted 👍. Thank you for your feedback!") |
|
else: |
|
st.write("You downvoted 👎. Thank you for your feedback!") |
|
load_dotenv() |
|
st.write(css, unsafe_allow_html=True) |
|
st.header("Chat with documents :books:") |
|
user_question = st.text_input("Ask a question about your documents:") |
|
if user_question: |
|
process_user_input(user_question) |
|
with st.sidebar: |
|
st.subheader("Your documents") |
|
docs = st.file_uploader("import documents", accept_multiple_files=True) |
|
with st.spinner("Processing"): |
|
raw = pdf2txt(docs) |
|
if len(raw) > 0: |
|
length = str(len(raw)) |
|
text_chunks = txt2chunks(raw) |
|
vectorstore = vector_store(text_chunks) |
|
st.session_state.conversation = get_chain(vectorstore) |
|
st.markdown('# AI Search Index of Length:' + length + ' Created.') |
|
filename = generate_filename(raw, 'txt') |
|
create_file(filename, raw, '', should_save) |
|
|
|
|
|
try: |
|
query_params = st.query_params |
|
query = (query_params.get('q') or query_params.get('query') or ['']) |
|
if len(query) > 1: |
|
|
|
|
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|
|
filesearch = PromptPrefix + query |
|
st.markdown(filesearch) |
|
process_text(filesearch) |
|
except: |
|
st.markdown(' ') |
|
|
|
if 'action' in st.query_params: |
|
action = st.query_params()['action'][0] |
|
if action == 'show_message': |
|
st.success("Showing a message because 'action=show_message' was found in the URL.") |
|
elif action == 'clear': |
|
clear_query_params() |
|
|
|
|
|
if 'query' in st.query_params: |
|
query = st.query_params['query'][0] |
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|
|
display_content_or_image(query) |
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|
|
def transcribe_canary(filename): |
|
from gradio_client import Client |
|
|
|
client = Client("https://awacke1-speech-recognition-canary-nvidiat4.hf.space/") |
|
result = client.predict( |
|
filename, |
|
"English", |
|
"English", |
|
True, |
|
api_name="/transcribe" |
|
) |
|
st.write(result) |
|
return result |
|
|
|
|
|
|
|
filename = save_and_play_audio(audio_recorder) |
|
if filename is not None: |
|
transcript='' |
|
transcript=transcribe_canary(filename) |
|
|
|
|
|
result = search_arxiv(transcript) |
|
|
|
|
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|
|
MODEL = "gpt-4o-2024-05-13" |
|
openai.api_key = os.getenv('OPENAI_API_KEY') |
|
openai.organization = os.getenv('OPENAI_ORG_ID') |
|
client = OpenAI(api_key= os.getenv('OPENAI_API_KEY'), organization=os.getenv('OPENAI_ORG_ID')) |
|
st.session_state.messages.append({"role": "user", "content": transcript}) |
|
with st.chat_message("user"): |
|
st.markdown(transcript) |
|
with st.chat_message("assistant"): |
|
completion = client.chat.completions.create( |
|
model=MODEL, |
|
messages = st.session_state.messages, |
|
stream=True |
|
) |
|
response = process_text2(text_input=prompt) |
|
st.session_state.messages.append({"role": "assistant", "content": response}) |
|
|
|
|
|
session_state = {} |
|
if "search_queries" not in session_state: |
|
session_state["search_queries"] = [] |
|
|
|
example_input = st.text_input("AI Search ArXiV Scholarly Articles", value=session_state["search_queries"][-1] if session_state["search_queries"] else "") |
|
|
|
if example_input: |
|
session_state["search_queries"].append(example_input) |
|
query=example_input |
|
if query: |
|
result = search_arxiv(query) |
|
|
|
|
|
st.markdown(' ') |
|
|
|
|
|
for example_input in session_state["search_queries"]: |
|
st.write(example_input) |
|
|
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|
|
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) |
|
|
|
AddAFileForContext=False |
|
if AddAFileForContext: |
|
|
|
collength, colupload = st.columns([2,3]) |
|
with collength: |
|
|
|
max_length = 128000 |
|
with colupload: |
|
uploaded_file = st.file_uploader("Add a file for context:", type=["pdf", "xml", "json", "xlsx", "csv", "html", "htm", "md", "txt"]) |
|
document_sections = deque() |
|
document_responses = {} |
|
if uploaded_file is not None: |
|
file_content = read_file_content(uploaded_file, max_length) |
|
document_sections.extend(divide_document(file_content, max_length)) |
|
|
|
|
|
if len(document_sections) > 0: |
|
if st.button("👁️ View Upload"): |
|
st.markdown("**Sections of the uploaded file:**") |
|
for i, section in enumerate(list(document_sections)): |
|
st.markdown(f"**Section {i+1}**\n{section}") |
|
|
|
st.markdown("**Chat with the model:**") |
|
for i, section in enumerate(list(document_sections)): |
|
if i in document_responses: |
|
st.markdown(f"**Section {i+1}**\n{document_responses[i]}") |
|
else: |
|
if st.button(f"Chat about Section {i+1}"): |
|
st.write('Reasoning with your inputs...') |
|
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) |
|
|
|
|
|
|
|
|
|
def main(): |
|
|
|
st.markdown("##### GPT-4o Omni Model: Text, Audio, Image, & Video") |
|
option = st.selectbox("Select an option", ("Text", "Image", "Audio", "Video")) |
|
if option == "Text": |
|
text_input = st.text_input("Enter your text:") |
|
if (text_input > ''): |
|
textResponse = process_text(text_input) |
|
elif option == "Image": |
|
text = "Help me understand what is in this picture and list ten facts as markdown outline with appropriate emojis that describes what you see." |
|
text_input = st.text_input(label="Enter text prompt to use with Image context.", value=text) |
|
image_input = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"]) |
|
image_response = process_image(image_input, text_input) |
|
|
|
elif option == "Audio": |
|
text = "You are generating a transcript summary. Create a summary of the provided transcription. Respond in Markdown." |
|
text_input = st.text_input(label="Enter text prompt to use with Audio context.", value=text) |
|
uploaded_files = st.file_uploader("Upload an audio file", type=["mp3", "wav"], accept_multiple_files=True) |
|
|
|
for audio_input in uploaded_files: |
|
st.write(audio_input.name) |
|
if audio_input is not None: |
|
process_audio(audio_input, text_input) |
|
|
|
elif option == "Audio old": |
|
|
|
text = "You are generating a transcript summary. Create a summary of the provided transcription. Respond in Markdown." |
|
text_input = st.text_input(label="Enter text prompt to use with Audio context.", value=text) |
|
|
|
uploaded_files = st.file_uploader("Upload an audio file", type=["mp3", "wav"], accept_multiple_files=True) |
|
for audio_input in uploaded_files: |
|
st.write(audio_input.name) |
|
|
|
if audio_input is not None: |
|
|
|
bytes_data = uploaded_file.getvalue() |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
process_audio(audio_input, text_input) |
|
|
|
elif option == "Video": |
|
video_input = st.file_uploader("Upload a video file", type=["mp4"]) |
|
process_audio_and_video(video_input) |
|
|
|
|
|
|
|
current_messages=[] |
|
for message in st.session_state.messages: |
|
with st.chat_message(message["role"]): |
|
current_messages.append(message) |
|
st.markdown(message["content"]) |
|
|
|
|
|
|
|
|
|
audio_files = glob.glob("*.wav") |
|
audio_files = [file for file in audio_files if len(os.path.splitext(file)[0]) >= 10] |
|
audio_files.sort(key=lambda x: (os.path.splitext(x)[1], x), reverse=True) |
|
|
|
|
|
image_files = glob.glob("*.png") |
|
image_files = [file for file in image_files if len(os.path.splitext(file)[0]) >= 10] |
|
image_files.sort(key=lambda x: (os.path.splitext(x)[1], x), reverse=True) |
|
|
|
|
|
video_files = glob.glob("*.mp4") |
|
video_files = [file for file in video_files if len(os.path.splitext(file)[0]) >= 10] |
|
video_files.sort(key=lambda x: (os.path.splitext(x)[1], x), reverse=True) |
|
|
|
|
|
video_files_mp3 = glob.glob("*.mp3") |
|
video_files_mp3 = [file for file in video_files_mp3 if len(os.path.splitext(file)[0]) >= 10] |
|
video_files_mp3.sort(key=lambda x: (os.path.splitext(x)[1], x), reverse=True) |
|
|
|
|
|
|
|
|
|
main() |
|
|
|
|
|
if st.sidebar.button("🗑 Delete All Audio"): |
|
for file in audio_files: |
|
os.remove(file) |
|
st.rerun() |
|
|
|
if st.sidebar.button("🗑 Delete All Images"): |
|
for file in image_files: |
|
os.remove(file) |
|
st.rerun() |
|
|
|
if st.sidebar.button("🗑 Delete All MP4 Videos"): |
|
for file in video_files: |
|
os.remove(file) |
|
st.rerun() |
|
|
|
if st.sidebar.button("🗑 Delete All MP3 Videos"): |
|
for file in video_files_mp3: |
|
os.remove(file) |
|
st.rerun() |
|
|
|
|
|
for file in audio_files: |
|
col1, col2 = st.sidebar.columns([6, 1]) |
|
with col1: |
|
st.markdown(file) |
|
if st.button("🎵", key="play_" + file): |
|
audio_file = open(file, 'rb') |
|
audio_bytes = audio_file.read() |
|
st.audio(audio_bytes, format='audio/wav') |
|
with col2: |
|
if st.button("🗑", key="delete_" + file): |
|
os.remove(file) |
|
st.rerun() |
|
|
|
|
|
for file in image_files: |
|
col1, col2 = st.sidebar.columns([6, 1]) |
|
with col1: |
|
st.markdown(file) |
|
if st.button("🖼", key="show_" + file): |
|
image = open(file, 'rb').read() |
|
st.image(image) |
|
with col2: |
|
if st.button("🗑", key="delete_" + file): |
|
os.remove(file) |
|
st.rerun() |
|
|
|
|
|
for file in video_files: |
|
col1, col2 = st.sidebar.columns([6, 1]) |
|
with col1: |
|
st.markdown(file) |
|
if st.button("🎥", key="play_" + file): |
|
video_file = open(file, 'rb') |
|
video_bytes = video_file.read() |
|
st.video(video_bytes) |
|
with col2: |
|
if st.button("🗑", key="delete_" + file): |
|
os.remove(file) |
|
st.rerun() |
|
|
|
|
|
for file in video_files_mp3: |
|
col1, col2 = st.sidebar.columns([6, 1]) |
|
with col1: |
|
st.markdown(file) |
|
if st.button("🎥", key="play_" + file): |
|
video_file = open(file, 'rb') |
|
video_bytes = video_file_mp3.read() |
|
st.video(video_bytes) |
|
with col2: |
|
if st.button("🗑", key="delete_" + file): |
|
os.remove(file) |
|
st.rerun() |
|
|
|
|
|
|
|
|
|
|
|
|
|
if prompt := st.chat_input("GPT-4o Multimodal ChatBot - What can I help you with?"): |
|
st.session_state.messages.append({"role": "user", "content": prompt}) |
|
with st.chat_message("user"): |
|
st.markdown(prompt) |
|
with st.chat_message("assistant"): |
|
completion = client.chat.completions.create( |
|
model=MODEL, |
|
messages = st.session_state.messages, |
|
stream=True |
|
) |
|
response = process_text2(text_input=prompt) |
|
st.session_state.messages.append({"role": "assistant", "content": response}) |
|
|
|
|
|
|
|
|
|
|
|
|
|
num_columns_images=st.slider(key="num_columns_images", label="Choose Number of Image Columns", min_value=1, max_value=15, value=3) |
|
display_images_and_wikipedia_summaries(num_columns_images) |
|
|
|
num_columns_video=st.slider(key="num_columns_video", label="Choose Number of Video Columns", min_value=1, max_value=15, value=3) |
|
display_videos_and_links(num_columns_video) |
|
|
|
|
|
|
|
showExtendedTextInterface=False |
|
if showExtendedTextInterface: |
|
display_glossary_grid(roleplaying_glossary) |
|
num_columns_text=st.slider(key="num_columns_text", label="Choose Number of Text Columns", min_value=1, max_value=15, value=4) |
|
display_buttons_with_scores(num_columns_text) |
|
st.markdown(personality_factors) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
