Spaces:
Running
on
CPU Upgrade
Running
on
CPU Upgrade
File size: 90,131 Bytes
05f51d1 cfb39dc 05f51d1 cfb39dc 05f51d1 cfb39dc 05f51d1 cfb39dc 05f51d1 cfb39dc 05f51d1 03b9d85 cfb39dc 05f51d1 cfb39dc 05f51d1 cfb39dc 05f51d1 cfb39dc 05f51d1 a3f748e e3c75a1 83228b7 a3f748e 05f51d1 0328c8e 05f51d1 20d41ee 05f51d1 e3ecad4 e3c75a1 e3ecad4 0394322 e3ecad4 05f51d1 e3c75a1 a17be0f e3c75a1 a17be0f e3c75a1 05f51d1 e3ecad4 05f51d1 e3ecad4 05f51d1 9e10f5c 05f51d1 20d41ee 05f51d1 c3d4d36 05f51d1 c3d4d36 05f51d1 e317a82 c3d4d36 2969a50 e317a82 c3d4d36 05f51d1 e317a82 c3d4d36 05f51d1 2969a50 c3d4d36 0394322 2969a50 05f51d1 e317a82 05f51d1 e317a82 05f51d1 e317a82 05f51d1 e317a82 2969a50 e317a82 c3d4d36 2969a50 e317a82 05f51d1 e317a82 9e10f5c c3d4d36 9e10f5c c3d4d36 9e10f5c c3d4d36 9e10f5c dc255e5 9e10f5c 2969a50 e317a82 2969a50 e317a82 2969a50 9e10f5c 2969a50 32a91b2 2969a50 32a91b2 2969a50 9e10f5c a17be0f 2969a50 32a91b2 2969a50 a17be0f 32a91b2 a17be0f 2969a50 05f51d1 2969a50 05f51d1 cfb39dc 05f51d1 cfb39dc 05f51d1 20d41ee 05f51d1 f9f3b61 05f51d1 f9f3b61 05f51d1 f9f3b61 05f51d1 f9f3b61 05f51d1 f9f3b61 05f51d1 73e1983 891d006 73e1983 05f51d1 20d41ee 05f51d1 20d41ee cc8083d 83228b7 cc8083d 73e1983 25fa1a6 73e1983 25fa1a6 73e1983 25fa1a6 73e1983 25fa1a6 73e1983 25fa1a6 20d41ee 25fa1a6 20d41ee 7907f22 20d41ee 7907f22 20d41ee 7907f22 20d41ee 7907f22 fb1f402 20d41ee fb1f402 20d41ee fb1f402 7907f22 20d41ee f30dc3c 20d41ee cc8083d 20d41ee cc8083d 20d41ee cc8083d 20d41ee cc8083d 73e1983 20d41ee 73e1983 cc8083d 891d006 cc8083d 73e1983 cc8083d 20d41ee cc8083d 20d41ee fb1f402 20d41ee fb1f402 20d41ee fb1f402 cc8083d fb1f402 20d41ee 45dc639 3e64129 20d41ee 3e64129 20d41ee 3e64129 83228b7 25fa1a6 83228b7 f9f3b61 25fa1a6 83228b7 25fa1a6 83228b7 3e64129 20d41ee |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 |
import base64
import cv2
import glob
import json
import math
import os
import pytz
import random
import re
import requests
import streamlit as st
import streamlit.components.v1 as components
import textract
import time
import zipfile
import PyPDF2
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 gradio_client import Client
from huggingface_hub import InferenceClient
from io import BytesIO
from moviepy.editor import VideoFileClip
from PIL import Image
from PyPDF2 import PdfReader
from templates import bot_template, css, user_template
from urllib.parse import quote # Ensure this import is included
from xml.etree import ElementTree as ET
import openai
from openai import OpenAI
import fitz # pymupdf
# 1. Configuration
Site_Name = 'Scholarly-Article-Document-Search-With-Memory'
title="🔬🧠ScienceBrain.AI"
helpURL='https://huggingface.co/awacke1'
bugURL='https://huggingface.co/spaces/awacke1'
icons='🔬'
st.set_page_config(
page_title=title,
page_icon=icons,
layout="wide",
#initial_sidebar_state="expanded",
initial_sidebar_state="auto",
menu_items={
'Get Help': helpURL,
'Report a bug': bugURL,
'About': title
}
)
client = OpenAI(api_key= os.getenv('OPENAI_API_KEY'), organization=os.getenv('OPENAI_ORG_ID'))
MODEL = "gpt-4o-2024-05-13"
if "openai_model" not in st.session_state:
st.session_state["openai_model"] = MODEL
if "messages" not in st.session_state:
st.session_state.messages = []
if st.button("Clear Session"):
st.session_state.messages = []
def convert_pdf_to_markdown(pdf_path):
try:
doc = fitz.open(pdf_path)
markdown_text = ""
for page in doc:
markdown_text += page.get_text("markdown")
doc.close()
return markdown_text
except Exception as e:
return f"Error converting PDF to markdown: {str(e)}"
def download_and_save_abstract(url, title):
response = requests.get(url)
if response.status_code == 200:
soup = BeautifulSoup(response.text, 'html.parser')
filename = f"{title.replace(' ', '_')}_abstract.html"
with open(filename, 'w', encoding='utf-8') as f:
f.write(str(soup))
return filename
return None
def download_and_save_pdf(url, title):
response = requests.get(url)
if response.status_code == 200:
filename = f"{title.replace(' ', '_')}_abstract.pdf" # Changed to match HTML naming
with open(filename, 'wb') as f:
f.write(response.content)
return filename
return None
# HTML5 based Speech Synthesis (Text to Speech in Browser)
@st.cache_resource
def SpeechSynthesis(result):
documentHTML5='''
<!DOCTYPE html>
<html>
<head>
<title>Read It Aloud</title>
<script type="text/javascript">
function readAloud() {
const text = document.getElementById("textArea").value;
const speech = new SpeechSynthesisUtterance(text);
window.speechSynthesis.speak(speech);
}
</script>
</head>
<body>
<h1>🔊 Read It Aloud</h1>
<textarea id="textArea" rows="10" cols="80">
'''
documentHTML5 = documentHTML5 + result
documentHTML5 = documentHTML5 + '''
</textarea>
<br>
<button onclick="readAloud()">🔊 Read Aloud</button>
</body>
</html>
'''
components.html(documentHTML5, width=1280, height=300)
def parse_to_markdown(text):
return text
def load_file(file_name):
file_extension = os.path.splitext(file_name)[1].lower()
try:
if file_extension == '.pdf':
return "PDF file. Use 'View' button to display the content."
elif file_extension == '.html':
return "HTML file. Use 'View' button to display the content."
else: # For .md and other text files
with open(file_name, 'r', encoding='utf-8') as file:
return file.read()
except Exception as e:
return f"Error loading file {file_name}: {str(e)}"
def extract_urls_old(text):
try:
date_pattern = re.compile(r'### (\d{2} \w{3} \d{4})')
abs_link_pattern = re.compile(r'\[(.*?)\]\((https://arxiv\.org/abs/\d+\.\d+)\)')
pdf_link_pattern = re.compile(r'\[⬇️\]\((https://arxiv\.org/pdf/\d+\.\d+)\)')
title_pattern = re.compile(r'### \d{2} \w{3} \d{4} \| \[(.*?)\]')
date_matches = date_pattern.findall(text)
abs_link_matches = abs_link_pattern.findall(text)
pdf_link_matches = pdf_link_pattern.findall(text)
title_matches = title_pattern.findall(text)
# markdown with the extracted fields
markdown_text = ""
for i in range(len(date_matches)):
date = date_matches[i]
title = title_matches[i]
abs_link = abs_link_matches[i][1]
pdf_link = pdf_link_matches[i]
markdown_text += f"**Date:** {date}\n\n"
markdown_text += f"**Title:** {title}\n\n"
markdown_text += f"**Abstract Link:** [{abs_link}]({abs_link})\n\n"
markdown_text += f"**PDF Link:** [{pdf_link}]({pdf_link})\n\n"
markdown_text += "---\n\n"
return markdown_text
except:
st.write('.')
return ''
def extract_urls(text):
try:
date_pattern = re.compile(r'### (\d{2} \w{3} \d{4})')
abs_link_pattern = re.compile(r'\[(.*?)\]\((https://arxiv\.org/abs/\d+\.\d+)\)')
pdf_link_pattern = re.compile(r'\[⬇️\]\((https://arxiv\.org/pdf/\d+\.\d+)\)')
title_pattern = re.compile(r'### \d{2} \w{3} \d{4} \| \[(.*?)\]')
date_matches = date_pattern.findall(text)
abs_link_matches = abs_link_pattern.findall(text)
pdf_link_matches = pdf_link_pattern.findall(text)
title_matches = title_pattern.findall(text)
markdown_text = ""
for i in range(len(date_matches)):
date = date_matches[i]
title = title_matches[i]
abs_link = abs_link_matches[i][1]
pdf_link = pdf_link_matches[i]
# Download and save abstract
abstract_file = download_and_save_abstract(abs_link, title)
# Download and save PDF
pdf_file = download_and_save_pdf(pdf_link, title)
markdown_text += f"**Date:** {date}\n\n"
markdown_text += f"**Title:** {title}\n\n"
markdown_text += f"**Abstract Link:** [{abs_link}]({abs_link})\n\n"
markdown_text += f"**PDF Link:** [{pdf_link}]({pdf_link})\n\n"
if abstract_file:
markdown_text += f"**Local Abstract:** [View Abstract]({abstract_file})\n\n"
if pdf_file:
markdown_text += f"**Local PDF:** [View PDF]({pdf_file})\n\n"
markdown_text += "---\n\n"
return markdown_text
except:
st.write('Error in extracting URLs')
return ''
def download_pdfs(urls):
local_files = []
for url in urls:
if url.endswith('.pdf'):
local_filename = url.split('/')[-1]
response = requests.get(url)
with open(local_filename, 'wb') as f:
f.write(response.content)
local_files.append(local_filename)
return local_files
def generate_html(local_files):
html = "<ul>"
for file in local_files:
link = f'<li><a href="{file}">{file}</a></li>'
html += link
html += "</ul>"
return html
#@st.cache_resource
def search_arxiv(query):
start_time = time.strftime("%Y-%m-%d %H:%M:%S")
client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
response1 = client.predict(
query,
20,
"Semantic Search - up to 10 Mar 2024",
"mistralai/Mixtral-8x7B-Instruct-v0.1",
api_name="/update_with_rag_md"
)
Question = '### 🔎 ' + query + '\r\n' # Format for markdown display with links
References = response1[0]
ReferenceLinks = extract_urls(References)
RunSecondQuery = True
if RunSecondQuery:
# Search 2 - Retrieve the Summary with Papers Context and Original Query
response2 = client.predict(
query,
"mistralai/Mixtral-8x7B-Instruct-v0.1",
True,
api_name="/ask_llm"
)
if len(response2) > 10:
Answer = response2
SpeechSynthesis(Answer)
# Restructure results to follow format of Question, Answer, References, ReferenceLinks
results = Question + '\r\n' + Answer + '\r\n' + References + '\r\n' + ReferenceLinks
st.markdown(results)
st.write('🔍Run of Multi-Agent System Paper Summary Spec is Complete')
end_time = time.strftime("%Y-%m-%d %H:%M:%S")
start_timestamp = time.mktime(time.strptime(start_time, "%Y-%m-%d %H:%M:%S"))
end_timestamp = time.mktime(time.strptime(end_time, "%Y-%m-%d %H:%M:%S"))
elapsed_seconds = end_timestamp - start_timestamp
st.write(f"Start time: {start_time}")
st.write(f"Finish time: {end_time}")
st.write(f"Elapsed time: {elapsed_seconds:.2f} seconds")
filename = generate_filename(query, "md")
create_file(filename, query, results, should_save)
# After search completes and generates results
result = "Your search results in Markdown format" # Replace with actual results
# Call the function to redraw sidebar and show results
redraw_sidebar_after_search(result)
return results
def download_pdfs_and_generate_html(urls):
pdf_links = []
for url in urls:
if url.endswith('.pdf'):
pdf_filename = os.path.basename(url)
download_pdf(url, pdf_filename)
pdf_links.append(pdf_filename)
local_links_html = '<ul>'
for link in pdf_links:
local_links_html += f'<li><a href="{link}">{link}</a></li>'
local_links_html += '</ul>'
return local_links_html
def download_pdf(url, filename):
response = requests.get(url)
with open(filename, 'wb') as file:
file.write(response.content)
# Prompts for App, for App Product, and App Product Code
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 '
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: '
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:'
# MoE Roleplaying Technique for Context Experts
roleplaying_glossary = {
"🤖 AI Concepts": {
"MoE (Mixture of Experts) 🧠": [
"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.",
"Discuss the key concepts, benefits, and challenges of self-rewarding AI in healthcare, as an expert.",
"Identify the top 3 pain points that MoE addresses in AI and healthcare, such as complexity and resource allocation.",
"Describe the top 3 joys of the MoE solution, including improved performance and adaptability in healthcare AI.",
"Highlight the top 3 superpowers MoE gives users, like tackling complex problems and personalizing interventions.",
"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."
]
}
}
# This displays per video and per image.
@st.cache_resource
def display_glossary_entity(k):
search_urls = {
"🚀🌌ArXiv": lambda k: f"/?q={quote(k)}", # this url plus query!
"🃏Analyst": lambda k: f"/?q={quote(k)}-{quote(PromptPrefix)}", # this url plus query!
"📚PyCoder": lambda k: f"/?q={quote(k)}-{quote(PromptPrefix2)}", # this url plus query!
"🔬JSCoder": lambda k: f"/?q={quote(k)}-{quote(PromptPrefix3)}", # this url plus query!
"📖": 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} {links_md}", unsafe_allow_html=True)
st.markdown(f"**{k}** <small>{links_md}</small>", unsafe_allow_html=True)
# Function to display the entire glossary in a grid format with links
@st.cache_resource
def display_glossary_grid(roleplaying_glossary):
search_urls = {
"🚀🌌ArXiv": lambda k: f"/?q={quote(k)}", # this url plus query!
"🃏Analyst": lambda k: f"/?q={quote(k)}-{quote(PromptPrefix)}", # this url plus query!
"📚PyCoder": lambda k: f"/?q={quote(k)}-{quote(PromptPrefix2)}", # this url plus query!
"🔬JSCoder": lambda k: f"/?q={quote(k)}-{quote(PromptPrefix3)}", # this url plus query!
"📖": 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)) # Create dynamic columns based on the number of games
#cols = st.columns(num_columns_text) # 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}** <small>{links_md}</small>", unsafe_allow_html=True)
# ChatBot client chat completions ------------------------- !!
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') as file:
#with open(file_path, 'r', encoding="unicode", errors="surrogateescape") as file:
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] # 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
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
# Function to compare file sizes and delete duplicates
def compare_and_delete_files(files):
if not files:
st.warning("No files to compare.")
return
# Dictionary to store file sizes and their paths
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]
# Remove all but the latest file for each size group
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()
# Function to get file size
def get_file_size(file_path):
return os.path.getsize(file_path)
def FileSidebar():
# Initialize session state variables if they don't exist
if 'files_to_display' not in st.session_state:
st.session_state.files_to_display = []
if 'search_result' not in st.session_state:
st.session_state.search_result = None
if 'sidebar_needs_update' not in st.session_state:
st.session_state.sidebar_needs_update = False
# Check if sidebar needs update
if st.session_state.sidebar_needs_update:
st.session_state.files_to_display = glob.glob("*.md") + glob.glob("*_abstract.html") + glob.glob("*_abstract.pdf")
st.session_state.files_to_display = [file for file in st.session_state.files_to_display if os.path.basename(file) and len(os.path.splitext(file)[0]) >= 10]
st.session_state.files_to_display.sort(key=lambda x: (os.path.splitext(x)[1], x), reverse=True)
st.session_state.sidebar_needs_update = False
# ⬇️ Download
Files1, Files2 = st.sidebar.columns(2)
with Files1:
if st.sidebar.button("🗑 Delete All"):
for file in st.session_state.files_to_display:
try:
os.remove(file)
except Exception as e:
st.sidebar.error(f"Error deleting {file}: {str(e)}")
st.session_state.sidebar_needs_update = True
st.rerun()
with Files2:
if st.sidebar.button("⬇️ Download"):
zip_file = create_zip_of_files(st.session_state.files_to_display)
st.sidebar.markdown(get_zip_download_link(zip_file), unsafe_allow_html=True)
# Add files 🌐View, 📂Edit, and 🗑Delete per file
for file in st.session_state.files_to_display:
timestamp = time.strftime("%H%M%S%f")[:-4] # HHMMSSNN format
col1, col2, col3, col4 = st.sidebar.columns([3,1,1,1])
with col1:
st.sidebar.markdown(f"**{file}**")
with col2:
if st.sidebar.button("🌐", key=f"view_{timestamp}_{file}"):
st.session_state.current_file = file
st.session_state.action = 'view'
with col3:
if st.sidebar.button("📂", key=f"edit_{timestamp}_{file}"):
st.session_state.current_file = file
st.session_state.action = 'edit'
with col4:
if st.sidebar.button("🗑", key=f"delete_{timestamp}_{file}"):
try:
os.remove(file)
st.sidebar.success(f"Deleted {file}")
st.session_state.sidebar_needs_update = True
st.rerun()
except Exception as e:
st.sidebar.error(f"Error deleting {file}: {str(e)}")
# Display content in main area
if 'current_file' in st.session_state and 'action' in st.session_state:
if st.session_state.action == 'view':
view_file(st.session_state.current_file)
elif st.session_state.action == 'edit':
edit_file(st.session_state.current_file)
# Show the result MD from the search
if st.session_state.search_result:
st.markdown("## Search Result")
st.markdown(st.session_state.search_result)
st.session_state.search_result = None # Clear the search result after displaying
# Function to be called after search completes
def update_sidebar_after_search(search_result):
st.session_state.search_result = search_result
st.session_state.sidebar_needs_update = True
# Function to be called after search completes
def redraw_sidebar_after_search(search_result):
st.session_state.search_result = search_result
#st.rerun()
def view_file(file):
st.header(f"Viewing: {file}")
file_extension = os.path.splitext(file)[1].lower()
try:
if file_extension == '.pdf':
display_pdf(file)
elif file_extension == '.html':
display_html(file)
else: # For .md and other text files
with open(file, 'r', encoding='utf-8') as f:
content = f.read()
st.markdown(content)
except Exception as e:
st.error(f"Error viewing {file}: {str(e)}")
def edit_file(file):
st.header(f"Editing: {file}")
file_extension = os.path.splitext(file)[1].lower()
try:
if file_extension == '.pdf':
md_content = pdf_to_markdown(file)
md_file = file.replace('.pdf', '.md')
new_content = st.text_area("Edit PDF content (as Markdown)", md_content, height=400)
if st.button("Save as Markdown"):
new_file_path = update_file_with_timestamp(md_file, new_content)
st.success(f"File saved as Markdown! New file: {new_file_path}")
elif file_extension == '.html':
with open(file, 'r', encoding='utf-8') as f:
content = f.read()
new_content = st.text_area("Edit HTML content", content, height=400)
if st.button("Save HTML"):
new_file_path = update_file_with_timestamp(file, new_content)
st.success(f"File updated successfully! New file: {new_file_path}")
else: # For .md and other text files
with open(file, 'r', encoding='utf-8') as f:
content = f.read()
new_content = st.text_area("Edit content", content, height=400)
if st.button("Save"):
new_file_path = update_file_with_timestamp(file, new_content)
st.success(f"File updated successfully! New file: {new_file_path}")
except Exception as e:
st.error(f"Error editing {file}: {str(e)}")
def pdf_to_markdown(file_path):
try:
with open(file_path, "rb") as f:
pdf_reader = PyPDF2.PdfReader(f)
markdown_content = ""
for page in pdf_reader.pages:
markdown_content += page.extract_text() + "\n\n"
return markdown_content
except Exception as e:
st.error(f"Error converting PDF to Markdown: {str(e)}")
return ""
def display_pdf(file_path):
try:
with open(file_path, "rb") as f:
pdf_reader = PyPDF2.PdfReader(f)
num_pages = len(pdf_reader.pages)
st.write(f"Total Pages: {num_pages}")
for page_num in range(num_pages):
page = pdf_reader.pages[page_num]
st.subheader(f"Page {page_num + 1}")
st.write(page.extract_text())
if page_num < num_pages - 1:
st.markdown("---") # Add a separator between pages
except Exception as e:
st.error(f"Error displaying PDF {file_path}: {str(e)}")
def save_file_with_timestamp(file_path, content, file_type):
directory, filename = os.path.split(file_path)
name, ext = os.path.splitext(filename)
timestamp = datetime.datetime.now().strftime("%m%d_%H%M")
new_filename = f"{timestamp}_{name}{ext}"
new_file_path = os.path.join(directory, new_filename)
if file_type == 'pdf':
with open(new_file_path, 'wb') as f:
f.write(content)
else: # For HTML and other text files
with open(new_file_path, 'w', encoding='utf-8') as f:
f.write(content)
return new_file_path
def update_file_with_timestamp(file_path, new_content):
file_type = os.path.splitext(file_path)[1].lower()[1:] # Get file extension without the dot
new_file_path = save_file_with_timestamp(file_path, new_content, file_type)
os.remove(file_path) # Remove the old file
return new_file_path
def display_html(file_path):
try:
with open(file_path, 'r', encoding='utf-8') as f:
html_content = f.read()
st.components.v1.html(html_content, height=800, scrolling=True)
except Exception as e:
st.error(f"Error displaying HTML {file_path}: {str(e)}")
def get_file_size(file_path):
try:
return os.path.getsize(file_path)
except Exception as e:
print(f"Error getting file size for {file_path}: {str(e)}")
return 0
# Randomly select a title
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()
# ---- Art Card Sidebar with Random Selection of image:
def get_image_as_base64(url):
response = requests.get(url)
if response.status_code == 200:
# Convert the image to base64
return base64.b64encode(response.content).decode("utf-8")
else:
return None
def create_download_link(filename, base64_str):
href = f'<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"![image](data:image/png;base64,{selected_image_base64})")
else:
st.sidebar.write("Failed to load the image.")
ShowSideImages=False
if ShowSideImages:
SideBarImageShuffle()
# Scoring for feedback: ----------------------------------------------------- emoji
# 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"] += increment
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
# 🔍Search Glossary
@st.cache_resource
def search_glossary(query):
all=""
st.markdown(f"- {query}")
# 🔍Run 1 - ArXiv RAG researcher expert ~-<>-~ Paper Summary & Ask LLM
client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
response2 = client.predict(
query, # str in 'parameter_13' Textbox component
#"mistralai/Mixtral-8x7B-Instruct-v0.1", # Literal['mistralai/Mixtral-8x7B-Instruct-v0.1', 'mistralai/Mistral-7B-Instruct-v0.2', 'google/gemma-7b-it', 'None'] in 'LLM Model' Dropdown component
#"mistralai/Mistral-7B-Instruct-v0.2", # Literal['mistralai/Mixtral-8x7B-Instruct-v0.1', 'mistralai/Mistral-7B-Instruct-v0.2', 'google/gemma-7b-it', 'None'] in 'LLM Model' Dropdown component
"google/gemma-7b-it", # Literal['mistralai/Mixtral-8x7B-Instruct-v0.1', 'mistralai/Mistral-7B-Instruct-v0.2', 'google/gemma-7b-it', 'None'] in 'LLM Model' Dropdown component
True, # bool in 'Stream output' Checkbox component
api_name="/ask_llm"
)
st.write('🔍Run of Multi-Agent System Paper Summary Spec is Complete')
st.markdown(response2)
# ArXiv searcher ~-<>-~ Paper References - Update with RAG
client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
response1 = client.predict(
query,
10,
"Semantic Search - up to 10 Mar 2024", # Literal['Semantic Search - up to 10 Mar 2024', 'Arxiv Search - Latest - (EXPERIMENTAL)'] in 'Search Source' Dropdown component
"mistralai/Mixtral-8x7B-Instruct-v0.1", # Literal['mistralai/Mixtral-8x7B-Instruct-v0.1', 'mistralai/Mistral-7B-Instruct-v0.2', 'google/gemma-7b-it', 'None'] in 'LLM Model' Dropdown component
api_name="/update_with_rag_md"
)
st.write('🔍Run of Multi-Agent System Paper References is Complete')
responseall = response2 + response1[0] + response1[1]
st.markdown(responseall)
return responseall
# Function to display the glossary in a structured format
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}")
#@st.cache_resource
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) # Define num_columns columns outside the loop
col_index = 0 # Initialize column index
for video_file in video_files_sorted:
with cols[col_index % num_columns]: # Use modulo 2 to alternate between the first and second column
# Embedding video with autoplay and loop using HTML
#video_html = ("""<video width="100%" loop autoplay> <source src="{video_file}" type="video/mp4">Your browser does not support the video tag.</video>""")
#st.markdown(video_html, unsafe_allow_html=True)
k = video_file.split('.')[0] # Assumes keyword is the file name without extension
st.video(video_file, format='video/mp4', start_time=0)
display_glossary_entity(k)
col_index += 1 # Increment column index to place the next video in the next column
#@st.cache_resource
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) # Use specified num_columns for layout
col_index = 0 # Initialize column index for cycling through columns
for image_file in image_files_sorted:
with cols[col_index % num_columns]: # Cycle through columns based on num_columns
image = Image.open(image_file)
st.image(image, caption=image_file, use_column_width=True)
k = image_file.split('.')[0] # Assumes keyword is the file name without extension
display_glossary_entity(k)
col_index += 1 # Increment to move to the next column in the next iteration
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
#@st.cache_resource
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 # Return after finding and displaying the first match
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
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": "🌍",
}
# Adjusted display_buttons_with_scores function
def display_buttons_with_scores(num_columns_text):
for category, games in roleplaying_glossary.items():
category_emoji = topic_emojis.get(category, "🔍") # 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, "🎮") # 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} {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()
# 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 = "...."
should_save = st.sidebar.checkbox("💾 Save", value=True, help="Save your session data.")
# 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']:
# ****** line 344 is read utf-8 encoding was needed when running locally to save utf-8 encoding and not fail on write
#with open(f"{base_filename}.md", 'w') as file:
#with open(f"{base_filename}.md", 'w', encoding="ascii", errors="surrogateescape") as file:
with open(f"{base_filename}.md", 'w', encoding='utf-8') as file:
#try:
#content = (prompt.strip() + '\r\n' + decode(response, ))
file.write(response)
#except:
# st.write('.')
# ****** utf-8 encoding was needed when running locally to save utf-8 encoding and not fail on write
#has_python_code = re.search(r"```python([\s\S]*?)```", prompt.strip() + '\r\n' + response)
#has_python_code = bool(re.search(r"```python([\s\S]*?)```", prompt.strip() + '\r\n' + response))
#if has_python_code:
# python_code = re.findall(r"```python([\s\S]*?)```", response)[0].strip()
# with open(f"{base_filename}-Code.py", 'w') as file:
# file.write(python_code)
# with open(f"{base_filename}.md", 'w') as file:
# content = prompt.strip() + '\r\n' + response
# file.write(content)
def truncate_document(document, length):
return document[:length]
def divide_document(document, max_length):
return [document[i:i+max_length] for i in range(0, len(document), max_length)]
def CompressXML(xml_text):
root = ET.fromstring(xml_text)
for elem in list(root.iter()):
if isinstance(elem.tag, str) and 'Comment' in elem.tag:
elem.parent.remove(elem)
return ET.tostring(root, encoding='unicode', method="xml")
# 10. Read in and provide UI for past files
@st.cache_resource
def read_file_content(file,max_length):
if file.type == "application/json":
content = json.load(file)
return str(content)
elif file.type == "text/html" or file.type == "text/htm":
content = BeautifulSoup(file, "html.parser")
return content.text
elif file.type == "application/xml" or file.type == "text/xml":
tree = ET.parse(file)
root = tree.getroot()
xml = CompressXML(ET.tostring(root, encoding='unicode'))
return xml
elif file.type == "text/markdown" or file.type == "text/md":
md = mistune.create_markdown()
content = md(file.read().decode())
return content
elif file.type == "text/plain":
return file.getvalue().decode()
else:
return ""
# 11. Chat with GPT - Caution on quota - now favoring fastest AI pipeline STT Whisper->LLM Llama->TTS
@st.cache_resource
def chat_with_model(prompt, document_section='', model_choice='gpt-3.5-turbo'): # gpt-4-0125-preview 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
# 11.1 45
@st.cache_resource
def chat_with_model45(prompt, document_section='', model_choice='gpt-4-0125-preview'): # gpt-4-0125-preview 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_file_contents(prompt, file_content, model_choice='gpt-3.5-turbo'): # gpt-4-0125-preview gpt-3.5-turbo
#def chat_with_file_contents(prompt, file_content, model_choice='gpt-4-0125-preview'): # gpt-4-0125-preview 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
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}"
# 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
# Sample function to demonstrate a response, replace with your own logic
def StreamMedChatResponse(topic):
st.write(f"Showing resources or questions related to: {topic}")
# Function to encode file to base64
def get_base64_encoded_file(file_path):
with open(file_path, "rb") as file:
return base64.b64encode(file.read()).decode()
# Function to create a download link
def get_audio_download_link(file_path):
base64_file = get_base64_encoded_file(file_path)
return f'<a href="data:file/wav;base64,{base64_file}" download="{os.path.basename(file_path)}">⬇️ Download Audio</a>'
GiveFeedback=False
if GiveFeedback:
with st.expander("Give your feedback 👍", expanded=False):
feedback = st.radio("Step 8: Give your feedback", ("👍 Upvote", "👎 Downvote"))
if feedback == "👍 Upvote":
st.write("You upvoted 👍. Thank you for your feedback!")
else:
st.write("You downvoted 👎. Thank you for your feedback!")
load_dotenv()
st.write(css, unsafe_allow_html=True)
st.header("Chat with documents :books:")
user_question = st.text_input("Ask a question about your documents:")
if user_question:
process_user_input(user_question)
with st.sidebar:
st.subheader("Your documents")
docs = st.file_uploader("import documents", accept_multiple_files=True)
with st.spinner("Processing"):
raw = pdf2txt(docs)
if len(raw) > 0:
length = str(len(raw))
text_chunks = txt2chunks(raw)
vectorstore = vector_store(text_chunks)
st.session_state.conversation = get_chain(vectorstore)
st.markdown('# AI Search Index of Length:' + length + ' Created.') # add timing
filename = generate_filename(raw, 'txt')
create_file(filename, raw, '', should_save)
# ⚙️q= Run ArXiv search from query parameters
try:
query_params = st.query_params
query = (query_params.get('q') or query_params.get('query') or [''])
if len(query) > 1:
result = search_arxiv(query)
#result2 = search_glossary(result)
except:
st.markdown(' ')
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.rerun()
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)
def transcribe_canary(filename):
from gradio_client import Client
client = Client("https://awacke1-speech-recognition-canary-nvidiat4.hf.space/")
result = client.predict(
filename, # filepath in 'parameter_5' Audio component
"English", # Literal['English', 'Spanish', 'French', 'German'] in 'Input audio is spoken in:' Dropdown component
"English", # Literal['English', 'Spanish', 'French', 'German'] in 'Transcribe in language:' Dropdown component
True, # bool in 'Punctuation & Capitalization in transcript?' Checkbox component
api_name="/transcribe"
)
st.write(result)
return result
# Transcript to arxiv and client chat completion ------------------------- !!
filename = save_and_play_audio(audio_recorder)
if filename is not None:
transcript=''
transcript=transcribe_canary(filename)
# Search ArXiV and get the Summary and Reference Papers Listing
result = search_arxiv(transcript)
# Start chatbot with transcript:
# ChatBot Entry
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})
# Scholary ArXiV Search ------------------------- !!
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)
#search_glossary(query)
#search_glossary(result)
st.markdown(' ')
#st.write("Search history:")
for example_input in session_state["search_queries"]:
st.write(example_input)
#if st.button("Run Prompt", help="Click to run."):
# try:
# response=StreamLLMChatResponse(example_input)
# create_file(filename, example_input, response, should_save)
# except:
# st.write('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)
AddAFileForContext=False
if AddAFileForContext:
collength, colupload = st.columns([2,3]) # adjust the ratio as needed
with collength:
#max_length = st.slider(key='maxlength', label="File section length for large files", min_value=1000, max_value=128000, value=12000, step=1000)
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)
# GPT4o documentation
# 1. Cookbook: https://cookbook.openai.com/examples/gpt4o/introduction_to_gpt4o
# 2. Configure your Project and Orgs to limit/allow Models: https://platform.openai.com/settings/organization/general
# 3. Watch your Billing! https://platform.openai.com/settings/organization/billing/overview
# Set API key and organization ID from environment variables
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'))
# Define the model to be used
#MODEL = "gpt-4o"
MODEL = "gpt-4o-2024-05-13"
def process_text(text_input):
if text_input:
st.session_state.messages.append({"role": "user", "content": text_input})
with st.chat_message("user"):
st.markdown(text_input)
with st.chat_message("assistant"):
completion = client.chat.completions.create(
model=MODEL,
messages=[
{"role": m["role"], "content": m["content"]}
for m in st.session_state.messages
],
stream=False
)
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)
st.session_state.messages.append({"role": "assistant", "content": return_text})
#st.write("Assistant: " + completion.choices[0].message.content)
def create_file(filename, prompt, response, is_image=False):
with open(filename, "w", encoding="utf-8") as f:
f.write(prompt + "\n\n" + response)
def save_image_old2(image, filename):
with open(filename, "wb") as f:
f.write(image.getbuffer())
# Now filename length protected for linux and windows filename lengths
def save_image(image, filename):
max_filename_length = 250
filename_stem, extension = os.path.splitext(filename)
truncated_stem = filename_stem[:max_filename_length - len(extension)] if len(filename) > max_filename_length else filename_stem
filename = f"{truncated_stem}{extension}"
with open(filename, "wb") as f:
f.write(image.getbuffer())
return filename
def extract_boldface_terms(text):
return re.findall(r'\*\*(.*?)\*\*', text)
def extract_title(text):
boldface_terms = re.findall(r'\*\*(.*?)\*\*', text)
if boldface_terms:
title = ' '.join(boldface_terms)
else:
title = re.sub(r'[^a-zA-Z0-9_\-]', ' ', text[-200:])
return title[-200:]
def process_image(image_input, user_prompt):
if image_input:
st.markdown('Processing image: ' + image_input.name )
if image_input:
base64_image = base64.b64encode(image_input.read()).decode("utf-8")
response = client.chat.completions.create(
model=MODEL,
messages=[
{"role": "system", "content": "You are a helpful assistant that responds in Markdown."},
{"role": "user", "content": [
{"type": "text", "text": user_prompt},
{"type": "image_url", "image_url": {
"url": f"data:image/png;base64,{base64_image}"}
}
]}
],
temperature=0.0,
)
image_response = response.choices[0].message.content
st.markdown(image_response)
# Save markdown on image AI output from gpt4o
filename_md = generate_filename(image_input.name + '- ' + image_response, "md")
# Save markdown on image AI output from gpt4o
filename_png = filename_md.replace('.md', '.' + image_input.name.split('.')[-1])
create_file(filename_md, image_response, '', True) #create_file() # create_file() 3 required positional arguments: 'filename', 'prompt', and 'response'
with open(filename_md, "w", encoding="utf-8") as f:
f.write(image_response)
# Extract boldface terms from image_response then autoname save file
#boldface_terms = extract_boldface_terms(image_response)
boldface_terms = extract_title(image_response).replace(':','')
filename_stem, extension = os.path.splitext(image_input.name)
filename_img = f"{filename_stem} {''.join(boldface_terms)}{extension}"
newfilename = save_image(image_input, filename_img)
filename_md = newfilename.replace('.png', '.md')
create_file(filename_md, '', image_response, True)
return image_response
def create_audio_file(filename, audio_data, should_save):
if should_save:
with open(filename, "wb") as file:
file.write(audio_data.getvalue())
st.success(f"Audio file saved as {filename}")
else:
st.warning("Audio file not saved.")
def process_audio(audio_input, text_input):
if audio_input:
transcription = client.audio.transcriptions.create(
model="whisper-1",
file=audio_input,
)
st.session_state.messages.append({"role": "user", "content": transcription.text})
with st.chat_message("assistant"):
st.markdown(transcription.text)
SpeechSynthesis(transcription.text)
filename = generate_filename(transcription.text, "wav")
create_audio_file(filename, audio_input, should_save)
#SpeechSynthesis(transcription.text)
filename = generate_filename(transcription.text, "md")
create_file(filename, transcription.text, transcription.text, should_save)
#st.markdown(response.choices[0].message.content)
def process_audio_for_video(video_input):
if video_input:
try:
transcription = client.audio.transcriptions.create(
model="whisper-1",
file=video_input,
)
response = client.chat.completions.create(
model=MODEL,
messages=[
{"role": "system", "content":"""You are generating a transcript summary. Create a summary of the provided transcription. Respond in Markdown."""},
{"role": "user", "content": [{"type": "text", "text": f"The audio transcription is: {transcription}"}],}
],
temperature=0,
)
st.markdown(response.choices[0].message.content)
return response.choices[0].message.content
except:
st.write('No transcript')
def save_video(video_file):
# Save the uploaded video file
with open(video_file.name, "wb") as f:
f.write(video_file.getbuffer())
return video_file.name
def process_video(video_path, seconds_per_frame=2):
base64Frames = []
base_video_path, _ = os.path.splitext(video_path)
video = cv2.VideoCapture(video_path)
total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
fps = video.get(cv2.CAP_PROP_FPS)
frames_to_skip = int(fps * seconds_per_frame)
curr_frame = 0
# Loop through the video and extract frames at specified sampling rate
while curr_frame < total_frames - 1:
video.set(cv2.CAP_PROP_POS_FRAMES, curr_frame)
success, frame = video.read()
if not success:
break
_, buffer = cv2.imencode(".jpg", frame)
base64Frames.append(base64.b64encode(buffer).decode("utf-8"))
curr_frame += frames_to_skip
video.release()
# Extract audio from video
audio_path = f"{base_video_path}.mp3"
try:
clip = VideoFileClip(video_path)
clip.audio.write_audiofile(audio_path, bitrate="32k")
clip.audio.close()
clip.close()
except:
st.write('No audio track found, moving on..')
print(f"Extracted {len(base64Frames)} frames")
print(f"Extracted audio to {audio_path}")
return base64Frames, audio_path
def process_audio_and_video(video_input):
if video_input is not None:
# Save the uploaded video file
video_path = save_video(video_input )
# Process the saved video
base64Frames, audio_path = process_video(video_path, seconds_per_frame=1)
# Get the transcript for the video model call
transcript = process_audio_for_video(video_input)
# Generate a summary with visual and audio
response = client.chat.completions.create(
model=MODEL,
messages=[
{"role": "system", "content": """You are generating a video summary. Create a summary of the provided video and its transcript. Respond in Markdown"""},
{"role": "user", "content": [
"These are the frames from the video.",
*map(lambda x: {"type": "image_url",
"image_url": {"url": f'data:image/jpg;base64,{x}', "detail": "low"}}, base64Frames),
{"type": "text", "text": f"The audio transcription is: {transcript}"}
]},
],
temperature=0,
)
results = response.choices[0].message.content
st.markdown(results)
if transcript:
filename = generate_filename(transcript, "md")
create_file(filename, transcript, results, should_save)
def main():
#st.markdown("### OpenAI GPT-4o Model")
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 = "Transcribe and answer questions as a helpful audio music and speech assistant. "
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:
# To read file as bytes:
bytes_data = uploaded_file.getvalue()
#st.write(bytes_data)
# To convert to a string based IO:
#stringio = StringIO(uploaded_file.getvalue().decode("utf-8"))
#st.write(stringio)
# To read file as string:
#string_data = stringio.read()
#st.write(string_data)
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)
# Enter the GPT-4o omni model in streamlit chatbot
current_messages=[]
for message in st.session_state.messages:
with st.chat_message(message["role"]):
current_messages.append(message)
st.markdown(message["content"])
# 🎵 Wav Audio files - Transcription History in Wav
audio_files = glob.glob("*.wav")
audio_files = [file for file in audio_files if len(os.path.splitext(file)[0]) >= 10] # exclude files with short names
audio_files.sort(key=lambda x: (os.path.splitext(x)[1], x), reverse=True) # sort by file type and file name in descending order
# 🖼 PNG Image files
image_files = glob.glob("*.png")
image_files = [file for file in image_files if len(os.path.splitext(file)[0]) >= 10] # exclude files with short names
image_files.sort(key=lambda x: (os.path.splitext(x)[1], x), reverse=True) # sort by file type and file name in descending order
# 🎥 MP4 Video files
video_files = glob.glob("*.mp4")
video_files = [file for file in video_files if len(os.path.splitext(file)[0]) >= 10] # exclude files with short names
video_files.sort(key=lambda x: (os.path.splitext(x)[1], x), reverse=True) # sort by file type and file name in descending order
main()
# Delete All button for each file type
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 Videos"):
for file in video_files:
os.remove(file)
st.rerun()
# Display and handle audio files
for file in audio_files:
col1, col2 = st.sidebar.columns([6, 1]) # adjust the ratio as needed
with col1:
st.markdown(file)
if st.button("🎵", key="play_" + file): # play emoji button
audio_file = open(file, 'rb')
audio_bytes = audio_file.read()
st.audio(audio_bytes, format='audio/wav')
with col2:
if st.button("🗑", key="delete_" + file):
os.remove(file)
st.rerun()
# Display and handle image files
for file in image_files:
col1, col2 = st.sidebar.columns([6, 1]) # adjust the ratio as needed
with col1:
st.markdown(file)
if st.button("🖼", key="show_" + file): # show emoji button
image = open(file, 'rb').read()
st.image(image)
with col2:
if st.button("🗑", key="delete_" + file):
os.remove(file)
st.rerun()
# Display and handle video files
for file in video_files:
col1, col2 = st.sidebar.columns([6, 1]) # adjust the ratio as needed
with col1:
st.markdown(file)
if st.button("🎥", key="play_" + file): # play emoji button
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()
# ChatBot Entry
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})
# Image and Video Galleries
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) # Image Jump Grid
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) # Video Jump Grid
# Optional UI's
showExtendedTextInterface=False
if showExtendedTextInterface:
display_glossary_grid(roleplaying_glossary) # Word Glossary Jump Grid - Dynamically calculates columns based on details length to keep topic together
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) # Feedback Jump Grid
st.markdown(personality_factors)
#if __name__ == "__main__":
|