File size: 165,583 Bytes
f257153 |
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 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 2254 2255 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 2268 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 2282 2283 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 2304 2305 2306 2307 2308 2309 2310 2311 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321 2322 2323 2324 2325 2326 2327 2328 2329 2330 2331 2332 2333 2334 2335 2336 2337 2338 2339 2340 2341 2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 2352 2353 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363 2364 2365 2366 2367 2368 2369 2370 2371 2372 2373 2374 2375 2376 2377 2378 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2405 2406 2407 2408 2409 2410 2411 2412 2413 2414 2415 2416 2417 2418 2419 2420 2421 2422 2423 2424 2425 2426 2427 2428 2429 2430 2431 2432 2433 2434 2435 2436 2437 2438 2439 2440 2441 2442 2443 2444 2445 2446 2447 2448 2449 2450 2451 2452 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2465 2466 2467 2468 2469 2470 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484 2485 2486 2487 2488 2489 2490 2491 2492 2493 2494 2495 2496 2497 2498 2499 2500 2501 2502 2503 2504 2505 2506 2507 2508 2509 2510 2511 2512 2513 2514 2515 2516 2517 2518 2519 2520 2521 2522 2523 2524 2525 2526 2527 2528 2529 2530 2531 2532 2533 2534 2535 2536 2537 2538 2539 2540 2541 2542 2543 2544 2545 2546 2547 2548 2549 2550 2551 2552 2553 2554 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2567 2568 2569 2570 2571 2572 2573 2574 2575 2576 2577 2578 2579 2580 2581 2582 2583 2584 2585 2586 2587 2588 2589 2590 2591 2592 2593 2594 2595 2596 2597 2598 2599 2600 2601 2602 2603 2604 2605 2606 2607 2608 2609 2610 2611 2612 2613 2614 2615 2616 2617 2618 2619 2620 2621 2622 2623 2624 2625 2626 2627 2628 2629 2630 2631 2632 2633 2634 2635 2636 2637 2638 2639 2640 2641 2642 2643 2644 2645 2646 2647 2648 2649 2650 2651 2652 2653 2654 2655 2656 2657 2658 2659 2660 2661 2662 2663 2664 2665 2666 2667 2668 2669 2670 2671 2672 2673 2674 2675 2676 2677 2678 2679 2680 2681 2682 2683 2684 2685 2686 2687 2688 2689 2690 2691 2692 2693 2694 2695 2696 2697 2698 2699 2700 2701 2702 2703 2704 2705 2706 2707 2708 2709 2710 2711 2712 2713 2714 2715 2716 2717 2718 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728 2729 2730 2731 2732 2733 2734 2735 2736 2737 2738 2739 2740 2741 2742 2743 2744 2745 2746 2747 2748 2749 2750 2751 2752 2753 2754 2755 2756 2757 2758 2759 2760 2761 2762 2763 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773 2774 2775 2776 2777 2778 2779 2780 2781 2782 2783 2784 2785 2786 2787 2788 2789 2790 2791 2792 2793 2794 2795 2796 2797 2798 2799 2800 2801 2802 2803 2804 2805 2806 2807 2808 2809 2810 2811 2812 2813 2814 2815 2816 2817 2818 2819 2820 2821 2822 2823 2824 2825 2826 2827 2828 2829 2830 2831 2832 2833 2834 2835 2836 2837 2838 2839 2840 2841 2842 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2869 2870 2871 2872 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886 2887 2888 2889 2890 2891 2892 2893 2894 2895 2896 2897 2898 2899 2900 2901 2902 2903 2904 2905 2906 2907 2908 2909 2910 2911 2912 2913 2914 2915 2916 2917 2918 2919 2920 2921 2922 2923 2924 2925 2926 2927 2928 2929 2930 2931 2932 2933 2934 |
import ast
import copy
import functools
import inspect
import itertools
import json
import os
import pprint
import random
import shutil
import sys
import time
import traceback
import typing
import uuid
import filelock
import pandas as pd
import requests
import tabulate
from iterators import TimeoutIterator
from gradio_utils.css import get_css
from gradio_utils.prompt_form import make_chatbots
# This is a hack to prevent Gradio from phoning home when it gets imported
os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
def my_get(url, **kwargs):
print('Gradio HTTP request redirected to localhost :)', flush=True)
kwargs.setdefault('allow_redirects', True)
return requests.api.request('get', 'http://127.0.0.1/', **kwargs)
original_get = requests.get
requests.get = my_get
import gradio as gr
requests.get = original_get
def fix_pydantic_duplicate_validators_error():
try:
from pydantic import class_validators
class_validators.in_ipython = lambda: True # type: ignore[attr-defined]
except ImportError:
pass
fix_pydantic_duplicate_validators_error()
from enums import DocumentSubset, no_model_str, no_lora_str, no_server_str, LangChainAction, LangChainMode, \
DocumentChoice, langchain_modes_intrinsic
from gradio_themes import H2oTheme, SoftTheme, get_h2o_title, get_simple_title, get_dark_js, spacing_xsm, radius_xsm, \
text_xsm
from prompter import prompt_type_to_model_name, prompt_types_strings, inv_prompt_type_to_model_lower, non_hf_types, \
get_prompt
from utils import flatten_list, zip_data, s3up, clear_torch_cache, get_torch_allocated, system_info_print, \
ping, get_short_name, makedirs, get_kwargs, remove, system_info, ping_gpu, get_url, get_local_ip, \
save_collection_names
from gen import get_model, languages_covered, evaluate, score_qa, inputs_kwargs_list, scratch_base_dir, \
get_max_max_new_tokens, get_minmax_top_k_docs, history_to_context, langchain_actions, langchain_agents_list, \
update_langchain
from evaluate_params import eval_func_param_names, no_default_param_names, eval_func_param_names_defaults, \
input_args_list
from apscheduler.schedulers.background import BackgroundScheduler
def fix_text_for_gradio(text, fix_new_lines=False, fix_latex_dollars=True):
if fix_latex_dollars:
ts = text.split('```')
for parti, part in enumerate(ts):
inside = parti % 2 == 1
if not inside:
ts[parti] = ts[parti].replace('$', '﹩')
text = '```'.join(ts)
if fix_new_lines:
# let Gradio handle code, since got improved recently
## FIXME: below conflicts with Gradio, but need to see if can handle multiple \n\n\n etc. properly as is.
# ensure good visually, else markdown ignores multiple \n
# handle code blocks
ts = text.split('```')
for parti, part in enumerate(ts):
inside = parti % 2 == 1
if not inside:
ts[parti] = ts[parti].replace('\n', '<br>')
text = '```'.join(ts)
return text
def go_gradio(**kwargs):
allow_api = kwargs['allow_api']
is_public = kwargs['is_public']
is_hf = kwargs['is_hf']
memory_restriction_level = kwargs['memory_restriction_level']
n_gpus = kwargs['n_gpus']
admin_pass = kwargs['admin_pass']
model_states = kwargs['model_states']
dbs = kwargs['dbs']
db_type = kwargs['db_type']
visible_langchain_actions = kwargs['visible_langchain_actions']
visible_langchain_agents = kwargs['visible_langchain_agents']
allow_upload_to_user_data = kwargs['allow_upload_to_user_data']
allow_upload_to_my_data = kwargs['allow_upload_to_my_data']
enable_sources_list = kwargs['enable_sources_list']
enable_url_upload = kwargs['enable_url_upload']
enable_text_upload = kwargs['enable_text_upload']
use_openai_embedding = kwargs['use_openai_embedding']
hf_embedding_model = kwargs['hf_embedding_model']
enable_captions = kwargs['enable_captions']
captions_model = kwargs['captions_model']
enable_ocr = kwargs['enable_ocr']
enable_pdf_ocr = kwargs['enable_pdf_ocr']
caption_loader = kwargs['caption_loader']
# for dynamic state per user session in gradio
model_state0 = kwargs['model_state0']
score_model_state0 = kwargs['score_model_state0']
my_db_state0 = kwargs['my_db_state0']
selection_docs_state0 = kwargs['selection_docs_state0']
# for evaluate defaults
langchain_modes0 = kwargs['langchain_modes']
visible_langchain_modes0 = kwargs['visible_langchain_modes']
langchain_mode_paths0 = kwargs['langchain_mode_paths']
# easy update of kwargs needed for evaluate() etc.
queue = True
allow_upload = allow_upload_to_user_data or allow_upload_to_my_data
kwargs.update(locals())
# import control
if kwargs['langchain_mode'] != 'Disabled':
from gpt_langchain import file_types, have_arxiv
else:
have_arxiv = False
file_types = []
if 'mbart-' in kwargs['model_lower']:
instruction_label_nochat = "Text to translate"
else:
instruction_label_nochat = "Instruction (Shift-Enter or push Submit to send message," \
" use Enter for multiple input lines)"
title = 'h2oGPT'
description = """<iframe src="https://ghbtns.com/github-btn.html?user=h2oai&repo=h2ogpt&type=star&count=true&size=small" frameborder="0" scrolling="0" width="250" height="20" title="GitHub"></iframe><small><a href="https://github.com/h2oai/h2ogpt">h2oGPT</a> <a href="https://github.com/h2oai/h2o-llmstudio">H2O LLM Studio</a><br><a href="https://huggingface.co/h2oai">🤗 Models</a>"""
description_bottom = "If this host is busy, try<br>[Multi-Model](https://gpt.h2o.ai)<br>[Falcon 40B](https://falcon.h2o.ai)<br>[Vicuna 33B](https://wizardvicuna.h2o.ai)<br>[MPT 30B-Chat](https://mpt.h2o.ai)<br>[HF Spaces1](https://huggingface.co/spaces/h2oai/h2ogpt-chatbot)<br>[HF Spaces2](https://huggingface.co/spaces/h2oai/h2ogpt-chatbot2)<br>"
if is_hf:
description_bottom += '''<a href="https://huggingface.co/spaces/h2oai/h2ogpt-chatbot?duplicate=true"><img src="https://bit.ly/3gLdBN6" style="white-space: nowrap" alt="Duplicate Space"></a>'''
task_info_md = ''
css_code = get_css(kwargs)
if kwargs['gradio_offline_level'] >= 0:
# avoid GoogleFont that pulls from internet
if kwargs['gradio_offline_level'] == 1:
# front end would still have to download fonts or have cached it at some point
base_font = 'Source Sans Pro'
else:
base_font = 'Helvetica'
theme_kwargs = dict(font=(base_font, 'ui-sans-serif', 'system-ui', 'sans-serif'),
font_mono=('IBM Plex Mono', 'ui-monospace', 'Consolas', 'monospace'))
else:
theme_kwargs = dict()
if kwargs['gradio_size'] == 'xsmall':
theme_kwargs.update(dict(spacing_size=spacing_xsm, text_size=text_xsm, radius_size=radius_xsm))
elif kwargs['gradio_size'] in [None, 'small']:
theme_kwargs.update(dict(spacing_size=gr.themes.sizes.spacing_sm, text_size=gr.themes.sizes.text_sm,
radius_size=gr.themes.sizes.spacing_sm))
elif kwargs['gradio_size'] == 'large':
theme_kwargs.update(dict(spacing_size=gr.themes.sizes.spacing_lg, text_size=gr.themes.sizes.text_lg),
radius_size=gr.themes.sizes.spacing_lg)
elif kwargs['gradio_size'] == 'medium':
theme_kwargs.update(dict(spacing_size=gr.themes.sizes.spacing_md, text_size=gr.themes.sizes.text_md,
radius_size=gr.themes.sizes.spacing_md))
theme = H2oTheme(**theme_kwargs) if kwargs['h2ocolors'] else SoftTheme(**theme_kwargs)
demo = gr.Blocks(theme=theme, css=css_code, title="h2oGPT", analytics_enabled=False)
callback = gr.CSVLogger()
model_options0 = flatten_list(list(prompt_type_to_model_name.values())) + kwargs['extra_model_options']
if kwargs['base_model'].strip() not in model_options0:
model_options0 = [kwargs['base_model'].strip()] + model_options0
lora_options = kwargs['extra_lora_options']
if kwargs['lora_weights'].strip() not in lora_options:
lora_options = [kwargs['lora_weights'].strip()] + lora_options
server_options = kwargs['extra_server_options']
if kwargs['inference_server'].strip() not in server_options:
server_options = [kwargs['inference_server'].strip()] + server_options
if os.getenv('OPENAI_API_KEY'):
if 'openai_chat' not in server_options:
server_options += ['openai_chat']
if 'openai' not in server_options:
server_options += ['openai']
# always add in no lora case
# add fake space so doesn't go away in gradio dropdown
model_options0 = [no_model_str] + model_options0
lora_options = [no_lora_str] + lora_options
server_options = [no_server_str] + server_options
# always add in no model case so can free memory
# add fake space so doesn't go away in gradio dropdown
# transcribe, will be detranscribed before use by evaluate()
if not kwargs['base_model'].strip():
kwargs['base_model'] = no_model_str
if not kwargs['lora_weights'].strip():
kwargs['lora_weights'] = no_lora_str
if not kwargs['inference_server'].strip():
kwargs['inference_server'] = no_server_str
# transcribe for gradio
kwargs['gpu_id'] = str(kwargs['gpu_id'])
no_model_msg = 'h2oGPT [ !!! Please Load Model in Models Tab !!! ]'
output_label0 = f'h2oGPT [Model: {kwargs.get("base_model")}]' if kwargs.get(
'base_model') else no_model_msg
output_label0_model2 = no_model_msg
def update_prompt(prompt_type1, prompt_dict1, model_state1, which_model=0):
if not prompt_type1 or which_model != 0:
# keep prompt_type and prompt_dict in sync if possible
prompt_type1 = kwargs.get('prompt_type', prompt_type1)
prompt_dict1 = kwargs.get('prompt_dict', prompt_dict1)
# prefer model specific prompt type instead of global one
if not prompt_type1 or which_model != 0:
prompt_type1 = model_state1.get('prompt_type', prompt_type1)
prompt_dict1 = model_state1.get('prompt_dict', prompt_dict1)
if not prompt_dict1 or which_model != 0:
# if still not defined, try to get
prompt_dict1 = kwargs.get('prompt_dict', prompt_dict1)
if not prompt_dict1 or which_model != 0:
prompt_dict1 = model_state1.get('prompt_dict', prompt_dict1)
return prompt_type1, prompt_dict1
default_kwargs = {k: kwargs[k] for k in eval_func_param_names_defaults}
# ensure prompt_type consistent with prep_bot(), so nochat API works same way
default_kwargs['prompt_type'], default_kwargs['prompt_dict'] = \
update_prompt(default_kwargs['prompt_type'], default_kwargs['prompt_dict'],
model_state1=model_state0, which_model=0)
for k in no_default_param_names:
default_kwargs[k] = ''
def dummy_fun(x):
# need dummy function to block new input from being sent until output is done,
# else gets input_list at time of submit that is old, and shows up as truncated in chatbot
return x
def allow_empty_instruction(langchain_mode1, document_subset1, langchain_action1):
allow = False
allow |= langchain_action1 not in LangChainAction.QUERY.value
allow |= document_subset1 in DocumentSubset.TopKSources.name
if langchain_mode1 in [LangChainMode.LLM.value]:
allow = False
return allow
with demo:
# avoid actual model/tokenizer here or anything that would be bad to deepcopy
# https://github.com/gradio-app/gradio/issues/3558
model_state = gr.State(
dict(model='model', tokenizer='tokenizer', device=kwargs['device'],
base_model=kwargs['base_model'],
tokenizer_base_model=kwargs['tokenizer_base_model'],
lora_weights=kwargs['lora_weights'],
inference_server=kwargs['inference_server'],
prompt_type=kwargs['prompt_type'],
prompt_dict=kwargs['prompt_dict'],
)
)
def update_langchain_mode_paths(db1s, selection_docs_state1):
if allow_upload_to_my_data:
selection_docs_state1['langchain_mode_paths'].update({k: None for k in db1s})
dup = selection_docs_state1['langchain_mode_paths'].copy()
for k, v in dup.items():
if k not in selection_docs_state1['visible_langchain_modes']:
selection_docs_state1['langchain_mode_paths'].pop(k)
return selection_docs_state1
# Setup some gradio states for per-user dynamic state
model_state2 = gr.State(kwargs['model_state_none'].copy())
model_options_state = gr.State([model_options0])
lora_options_state = gr.State([lora_options])
server_options_state = gr.State([server_options])
my_db_state = gr.State(my_db_state0)
chat_state = gr.State({})
docs_state00 = kwargs['document_choice'] + [DocumentChoice.ALL.value]
docs_state0 = []
[docs_state0.append(x) for x in docs_state00 if x not in docs_state0]
docs_state = gr.State(docs_state0)
viewable_docs_state0 = []
viewable_docs_state = gr.State(viewable_docs_state0)
selection_docs_state0 = update_langchain_mode_paths(my_db_state0, selection_docs_state0)
selection_docs_state = gr.State(selection_docs_state0)
gr.Markdown(f"""
{get_h2o_title(title, description) if kwargs['h2ocolors'] else get_simple_title(title, description)}
""")
# go button visible if
base_wanted = kwargs['base_model'] != no_model_str and kwargs['login_mode_if_model0']
go_btn = gr.Button(value="ENTER", visible=base_wanted, variant="primary")
nas = ' '.join(['NA'] * len(kwargs['model_states']))
res_value = "Response Score: NA" if not kwargs[
'model_lock'] else "Response Scores: %s" % nas
if kwargs['langchain_mode'] != LangChainMode.DISABLED.value:
extra_prompt_form = ". For summarization, no query required, just click submit"
else:
extra_prompt_form = ""
if kwargs['input_lines'] > 1:
instruction_label = "Shift-Enter to Submit, Enter for more lines%s" % extra_prompt_form
else:
instruction_label = "Enter to Submit, Shift-Enter for more lines%s" % extra_prompt_form
def get_langchain_choices(selection_docs_state1):
langchain_modes = selection_docs_state1['langchain_modes']
visible_langchain_modes = selection_docs_state1['visible_langchain_modes']
if is_hf:
# don't show 'wiki' since only usually useful for internal testing at moment
no_show_modes = ['Disabled', 'wiki']
else:
no_show_modes = ['Disabled']
allowed_modes = visible_langchain_modes.copy()
# allowed_modes = [x for x in allowed_modes if x in dbs]
allowed_modes += ['LLM']
if allow_upload_to_my_data and 'MyData' not in allowed_modes:
allowed_modes += ['MyData']
if allow_upload_to_user_data and 'UserData' not in allowed_modes:
allowed_modes += ['UserData']
choices = [x for x in langchain_modes if x in allowed_modes and x not in no_show_modes]
return choices
def get_df_langchain_mode_paths(selection_docs_state1):
langchain_mode_paths = selection_docs_state1['langchain_mode_paths']
if langchain_mode_paths:
df = pd.DataFrame.from_dict(langchain_mode_paths.items(), orient='columns')
df.columns = ['Collection', 'Path']
else:
df = pd.DataFrame(None)
return df
normal_block = gr.Row(visible=not base_wanted, equal_height=False)
with normal_block:
side_bar = gr.Column(elem_id="col_container", scale=1, min_width=100)
with side_bar:
with gr.Accordion("Chats", open=False, visible=True):
radio_chats = gr.Radio(value=None, label="Saved Chats", show_label=False,
visible=True, interactive=True,
type='value')
upload_visible = kwargs['langchain_mode'] != 'Disabled' and allow_upload
with gr.Accordion("Upload", open=False, visible=upload_visible):
with gr.Column():
with gr.Row(equal_height=False):
file_types_str = '[' + ' '.join(file_types) + ' URL ArXiv TEXT' + ']'
fileup_output = gr.File(label=f'Upload {file_types_str}',
show_label=False,
file_types=file_types,
file_count="multiple",
scale=1,
min_width=0,
elem_id="warning", elem_classes="feedback")
fileup_output_text = gr.Textbox(visible=False)
url_visible = kwargs['langchain_mode'] != 'Disabled' and allow_upload and enable_url_upload
url_label = 'URL/ArXiv' if have_arxiv else 'URL'
url_text = gr.Textbox(label=url_label,
# placeholder="Enter Submits",
max_lines=1,
interactive=True)
text_visible = kwargs['langchain_mode'] != 'Disabled' and allow_upload and enable_text_upload
user_text_text = gr.Textbox(label='Paste Text',
# placeholder="Enter Submits",
interactive=True,
visible=text_visible)
github_textbox = gr.Textbox(label="Github URL", visible=False) # FIXME WIP
database_visible = kwargs['langchain_mode'] != 'Disabled'
with gr.Accordion("Resources", open=False, visible=database_visible):
langchain_choices0 = get_langchain_choices(selection_docs_state0)
langchain_mode = gr.Radio(
langchain_choices0,
value=kwargs['langchain_mode'],
label="Collections",
show_label=True,
visible=kwargs['langchain_mode'] != 'Disabled',
min_width=100)
add_chat_history_to_context = gr.Checkbox(label="Chat History",
value=kwargs['add_chat_history_to_context'])
document_subset = gr.Radio([x.name for x in DocumentSubset],
label="Subset",
value=DocumentSubset.Relevant.name,
interactive=True,
)
allowed_actions = [x for x in langchain_actions if x in visible_langchain_actions]
langchain_action = gr.Radio(
allowed_actions,
value=allowed_actions[0] if len(allowed_actions) > 0 else None,
label="Action",
visible=True)
allowed_agents = [x for x in langchain_agents_list if x in visible_langchain_agents]
langchain_agents = gr.Dropdown(
langchain_agents_list,
value=kwargs['langchain_agents'],
label="Agents",
multiselect=True,
interactive=True,
visible=False) # WIP
col_tabs = gr.Column(elem_id="col_container", scale=10)
with (col_tabs, gr.Tabs()):
with gr.TabItem("Chat"):
if kwargs['langchain_mode'] == 'Disabled':
text_output_nochat = gr.Textbox(lines=5, label=output_label0, show_copy_button=True,
visible=not kwargs['chat'])
else:
# text looks a bit worse, but HTML links work
text_output_nochat = gr.HTML(label=output_label0, visible=not kwargs['chat'])
with gr.Row():
# NOCHAT
instruction_nochat = gr.Textbox(
lines=kwargs['input_lines'],
label=instruction_label_nochat,
placeholder=kwargs['placeholder_instruction'],
visible=not kwargs['chat'],
)
iinput_nochat = gr.Textbox(lines=4, label="Input context for Instruction",
placeholder=kwargs['placeholder_input'],
visible=not kwargs['chat'])
submit_nochat = gr.Button("Submit", size='sm', visible=not kwargs['chat'])
flag_btn_nochat = gr.Button("Flag", size='sm', visible=not kwargs['chat'])
score_text_nochat = gr.Textbox("Response Score: NA", show_label=False,
visible=not kwargs['chat'])
submit_nochat_api = gr.Button("Submit nochat API", visible=False)
inputs_dict_str = gr.Textbox(label='API input for nochat', show_label=False, visible=False)
text_output_nochat_api = gr.Textbox(lines=5, label='API nochat output', visible=False,
show_copy_button=True)
# CHAT
col_chat = gr.Column(visible=kwargs['chat'])
with col_chat:
with gr.Row(): # elem_id='prompt-form-area'):
with gr.Column(scale=50):
instruction = gr.Textbox(
lines=kwargs['input_lines'],
label='Ask anything',
placeholder=instruction_label,
info=None,
elem_id='prompt-form',
container=True,
)
submit_buttons = gr.Row(equal_height=False)
with submit_buttons:
mw1 = 50
mw2 = 50
with gr.Column(min_width=mw1):
submit = gr.Button(value='Submit', variant='primary', size='sm',
min_width=mw1)
stop_btn = gr.Button(value="Stop", variant='secondary', size='sm',
min_width=mw1)
save_chat_btn = gr.Button("Save", size='sm', min_width=mw1)
with gr.Column(min_width=mw2):
retry_btn = gr.Button("Redo", size='sm', min_width=mw2)
undo = gr.Button("Undo", size='sm', min_width=mw2)
clear_chat_btn = gr.Button(value="Clear", size='sm', min_width=mw2)
text_output, text_output2, text_outputs = make_chatbots(output_label0, output_label0_model2,
**kwargs)
with gr.Row():
with gr.Column(visible=kwargs['score_model']):
score_text = gr.Textbox(res_value,
show_label=False,
visible=True)
score_text2 = gr.Textbox("Response Score2: NA", show_label=False,
visible=False and not kwargs['model_lock'])
with gr.TabItem("Document Selection"):
document_choice = gr.Dropdown(docs_state0,
label="Select Subset of Document(s) %s" % file_types_str,
value=[DocumentChoice.ALL.value],
interactive=True,
multiselect=True,
visible=kwargs['langchain_mode'] != 'Disabled',
)
sources_visible = kwargs['langchain_mode'] != 'Disabled' and enable_sources_list
with gr.Row():
with gr.Column(scale=1):
get_sources_btn = gr.Button(value="Update UI with Document(s) from DB", scale=0, size='sm',
visible=sources_visible)
show_sources_btn = gr.Button(value="Show Sources from DB", scale=0, size='sm',
visible=sources_visible)
refresh_sources_btn = gr.Button(value="Update DB with new/changed files on disk", scale=0,
size='sm',
visible=sources_visible and allow_upload_to_user_data)
with gr.Column(scale=4):
pass
with gr.Row():
with gr.Column(scale=1):
visible_add_remove_collection = (allow_upload_to_user_data or
allow_upload_to_my_data) and \
kwargs['langchain_mode'] != 'Disabled'
add_placeholder = "e.g. UserData2, user_path2 (optional)" \
if not is_public else "e.g. MyData2"
remove_placeholder = "e.g. UserData2" if not is_public else "e.g. MyData2"
new_langchain_mode_text = gr.Textbox(value="", visible=visible_add_remove_collection,
label='Add Collection',
placeholder=add_placeholder,
interactive=True)
remove_langchain_mode_text = gr.Textbox(value="", visible=visible_add_remove_collection,
label='Remove Collection',
placeholder=remove_placeholder,
interactive=True)
load_langchain = gr.Button(value="Load LangChain State", scale=0, size='sm',
visible=allow_upload_to_user_data and
kwargs['langchain_mode'] != 'Disabled')
with gr.Column(scale=1):
df0 = get_df_langchain_mode_paths(selection_docs_state0)
langchain_mode_path_text = gr.Dataframe(value=df0,
visible=visible_add_remove_collection,
label='LangChain Mode-Path',
show_label=False,
interactive=False)
with gr.Column(scale=4):
pass
sources_row = gr.Row(visible=kwargs['langchain_mode'] != 'Disabled' and enable_sources_list,
equal_height=False)
with sources_row:
with gr.Column(scale=1):
file_source = gr.File(interactive=False,
label="Download File w/Sources")
with gr.Column(scale=2):
sources_text = gr.HTML(label='Sources Added', interactive=False)
doc_exception_text = gr.Textbox(value="", label='Document Exceptions',
interactive=False,
visible=kwargs['langchain_mode'] != 'Disabled')
with gr.TabItem("Document Viewer"):
with gr.Row(visible=kwargs['langchain_mode'] != 'Disabled'):
with gr.Column(scale=2):
get_viewable_sources_btn = gr.Button(value="Update UI with Document(s) from DB", scale=0,
size='sm',
visible=sources_visible)
view_document_choice = gr.Dropdown(viewable_docs_state0,
label="Select Single Document",
value=None,
interactive=True,
multiselect=False,
visible=True,
)
with gr.Column(scale=4):
pass
document = 'http://infolab.stanford.edu/pub/papers/google.pdf'
doc_view = gr.HTML(visible=False)
doc_view2 = gr.Dataframe(visible=False)
doc_view3 = gr.JSON(visible=False)
doc_view4 = gr.Markdown(visible=False)
with gr.TabItem("Chat History"):
with gr.Row():
with gr.Column(scale=1):
remove_chat_btn = gr.Button(value="Remove Selected Saved Chats", visible=True, size='sm')
flag_btn = gr.Button("Flag Current Chat", size='sm')
export_chats_btn = gr.Button(value="Export Chats to Download", size='sm')
with gr.Column(scale=4):
pass
with gr.Row():
chats_file = gr.File(interactive=False, label="Download Exported Chats")
chatsup_output = gr.File(label="Upload Chat File(s)",
file_types=['.json'],
file_count='multiple',
elem_id="warning", elem_classes="feedback")
with gr.Row():
if 'mbart-' in kwargs['model_lower']:
src_lang = gr.Dropdown(list(languages_covered().keys()),
value=kwargs['src_lang'],
label="Input Language")
tgt_lang = gr.Dropdown(list(languages_covered().keys()),
value=kwargs['tgt_lang'],
label="Output Language")
chat_exception_text = gr.Textbox(value="", visible=True, label='Chat Exceptions',
interactive=False)
with gr.TabItem("Expert"):
with gr.Row():
with gr.Column():
stream_output = gr.components.Checkbox(label="Stream output",
value=kwargs['stream_output'])
prompt_type = gr.Dropdown(prompt_types_strings,
value=kwargs['prompt_type'], label="Prompt Type",
visible=not kwargs['model_lock'],
interactive=not is_public,
)
prompt_type2 = gr.Dropdown(prompt_types_strings,
value=kwargs['prompt_type'], label="Prompt Type Model 2",
visible=False and not kwargs['model_lock'],
interactive=not is_public)
do_sample = gr.Checkbox(label="Sample",
info="Enable sampler, required for use of temperature, top_p, top_k",
value=kwargs['do_sample'])
temperature = gr.Slider(minimum=0.01, maximum=2,
value=kwargs['temperature'],
label="Temperature",
info="Lower is deterministic (but may lead to repeats), Higher more creative (but may lead to hallucinations)")
top_p = gr.Slider(minimum=1e-3, maximum=1.0 - 1e-3,
value=kwargs['top_p'], label="Top p",
info="Cumulative probability of tokens to sample from")
top_k = gr.Slider(
minimum=1, maximum=100, step=1,
value=kwargs['top_k'], label="Top k",
info='Num. tokens to sample from'
)
# FIXME: https://github.com/h2oai/h2ogpt/issues/106
if os.getenv('TESTINGFAIL'):
max_beams = 8 if not (memory_restriction_level or is_public) else 1
else:
max_beams = 1
num_beams = gr.Slider(minimum=1, maximum=max_beams, step=1,
value=min(max_beams, kwargs['num_beams']), label="Beams",
info="Number of searches for optimal overall probability. "
"Uses more GPU memory/compute",
interactive=False)
max_max_new_tokens = get_max_max_new_tokens(model_state0, **kwargs)
max_new_tokens = gr.Slider(
minimum=1, maximum=max_max_new_tokens, step=1,
value=min(max_max_new_tokens, kwargs['max_new_tokens']), label="Max output length",
)
min_new_tokens = gr.Slider(
minimum=0, maximum=max_max_new_tokens, step=1,
value=min(max_max_new_tokens, kwargs['min_new_tokens']), label="Min output length",
)
max_new_tokens2 = gr.Slider(
minimum=1, maximum=max_max_new_tokens, step=1,
value=min(max_max_new_tokens, kwargs['max_new_tokens']), label="Max output length 2",
visible=False and not kwargs['model_lock'],
)
min_new_tokens2 = gr.Slider(
minimum=0, maximum=max_max_new_tokens, step=1,
value=min(max_max_new_tokens, kwargs['min_new_tokens']), label="Min output length 2",
visible=False and not kwargs['model_lock'],
)
early_stopping = gr.Checkbox(label="EarlyStopping", info="Stop early in beam search",
value=kwargs['early_stopping'])
max_time = gr.Slider(minimum=0, maximum=kwargs['max_max_time'], step=1,
value=min(kwargs['max_max_time'],
kwargs['max_time']), label="Max. time",
info="Max. time to search optimal output.")
repetition_penalty = gr.Slider(minimum=0.01, maximum=3.0,
value=kwargs['repetition_penalty'],
label="Repetition Penalty")
num_return_sequences = gr.Slider(minimum=1, maximum=10, step=1,
value=kwargs['num_return_sequences'],
label="Number Returns", info="Must be <= num_beams",
interactive=not is_public)
iinput = gr.Textbox(lines=4, label="Input",
placeholder=kwargs['placeholder_input'],
interactive=not is_public)
context = gr.Textbox(lines=3, label="System Pre-Context",
info="Directly pre-appended without prompt processing",
interactive=not is_public)
chat = gr.components.Checkbox(label="Chat mode", value=kwargs['chat'],
visible=False, # no longer support nochat in UI
interactive=not is_public,
)
count_chat_tokens_btn = gr.Button(value="Count Chat Tokens",
visible=not is_public and not kwargs['model_lock'],
interactive=not is_public)
chat_token_count = gr.Textbox(label="Chat Token Count", value=None,
visible=not is_public and not kwargs['model_lock'],
interactive=False)
chunk = gr.components.Checkbox(value=kwargs['chunk'],
label="Whether to chunk documents",
info="For LangChain",
visible=kwargs['langchain_mode'] != 'Disabled',
interactive=not is_public)
min_top_k_docs, max_top_k_docs, label_top_k_docs = get_minmax_top_k_docs(is_public)
top_k_docs = gr.Slider(minimum=min_top_k_docs, maximum=max_top_k_docs, step=1,
value=kwargs['top_k_docs'],
label=label_top_k_docs,
info="For LangChain",
visible=kwargs['langchain_mode'] != 'Disabled',
interactive=not is_public)
chunk_size = gr.Number(value=kwargs['chunk_size'],
label="Chunk size for document chunking",
info="For LangChain (ignored if chunk=False)",
minimum=128,
maximum=2048,
visible=kwargs['langchain_mode'] != 'Disabled',
interactive=not is_public,
precision=0)
with gr.TabItem("Models"):
model_lock_msg = gr.Textbox(lines=1, label="Model Lock Notice",
placeholder="Started in model_lock mode, no model changes allowed.",
visible=bool(kwargs['model_lock']), interactive=False)
load_msg = "Load-Unload Model/LORA [unload works if did not use --base_model]" if not is_public \
else "LOAD-UNLOAD DISABLED FOR HOSTED DEMO"
load_msg2 = "Load-Unload Model/LORA 2 [unload works if did not use --base_model]" if not is_public \
else "LOAD-UNLOAD DISABLED FOR HOSTED DEMO 2"
variant_load_msg = 'primary' if not is_public else 'secondary'
compare_checkbox = gr.components.Checkbox(label="Compare Mode",
value=kwargs['model_lock'],
visible=not is_public and not kwargs['model_lock'])
with gr.Row():
n_gpus_list = [str(x) for x in list(range(-1, n_gpus))]
with gr.Column():
with gr.Row():
with gr.Column(scale=20, visible=not kwargs['model_lock']):
model_choice = gr.Dropdown(model_options_state.value[0], label="Choose Model",
value=kwargs['base_model'])
lora_choice = gr.Dropdown(lora_options_state.value[0], label="Choose LORA",
value=kwargs['lora_weights'], visible=kwargs['show_lora'])
server_choice = gr.Dropdown(server_options_state.value[0], label="Choose Server",
value=kwargs['inference_server'], visible=not is_public)
with gr.Column(scale=1, visible=not kwargs['model_lock']):
load_model_button = gr.Button(load_msg, variant=variant_load_msg, scale=0,
size='sm', interactive=not is_public)
model_load8bit_checkbox = gr.components.Checkbox(
label="Load 8-bit [requires support]",
value=kwargs['load_8bit'], interactive=not is_public)
model_use_gpu_id_checkbox = gr.components.Checkbox(
label="Choose Devices [If not Checked, use all GPUs]",
value=kwargs['use_gpu_id'], interactive=not is_public)
model_gpu = gr.Dropdown(n_gpus_list,
label="GPU ID [-1 = all GPUs, if Choose is enabled]",
value=kwargs['gpu_id'], interactive=not is_public)
model_used = gr.Textbox(label="Current Model", value=kwargs['base_model'],
interactive=False)
lora_used = gr.Textbox(label="Current LORA", value=kwargs['lora_weights'],
visible=kwargs['show_lora'], interactive=False)
server_used = gr.Textbox(label="Current Server",
value=kwargs['inference_server'],
visible=bool(kwargs['inference_server']) and not is_public,
interactive=False)
prompt_dict = gr.Textbox(label="Prompt (or Custom)",
value=pprint.pformat(kwargs['prompt_dict'], indent=4),
interactive=not is_public, lines=4)
col_model2 = gr.Column(visible=False)
with col_model2:
with gr.Row():
with gr.Column(scale=20, visible=not kwargs['model_lock']):
model_choice2 = gr.Dropdown(model_options_state.value[0], label="Choose Model 2",
value=no_model_str)
lora_choice2 = gr.Dropdown(lora_options_state.value[0], label="Choose LORA 2",
value=no_lora_str,
visible=kwargs['show_lora'])
server_choice2 = gr.Dropdown(server_options_state.value[0], label="Choose Server 2",
value=no_server_str,
visible=not is_public)
with gr.Column(scale=1, visible=not kwargs['model_lock']):
load_model_button2 = gr.Button(load_msg2, variant=variant_load_msg, scale=0,
size='sm', interactive=not is_public)
model_load8bit_checkbox2 = gr.components.Checkbox(
label="Load 8-bit 2 [requires support]",
value=kwargs['load_8bit'], interactive=not is_public)
model_use_gpu_id_checkbox2 = gr.components.Checkbox(
label="Choose Devices 2 [If not Checked, use all GPUs]",
value=kwargs[
'use_gpu_id'], interactive=not is_public)
model_gpu2 = gr.Dropdown(n_gpus_list,
label="GPU ID 2 [-1 = all GPUs, if choose is enabled]",
value=kwargs['gpu_id'], interactive=not is_public)
# no model/lora loaded ever in model2 by default
model_used2 = gr.Textbox(label="Current Model 2", value=no_model_str,
interactive=False)
lora_used2 = gr.Textbox(label="Current LORA 2", value=no_lora_str,
visible=kwargs['show_lora'], interactive=False)
server_used2 = gr.Textbox(label="Current Server 2", value=no_server_str,
interactive=False,
visible=not is_public)
prompt_dict2 = gr.Textbox(label="Prompt (or Custom) 2",
value=pprint.pformat(kwargs['prompt_dict'], indent=4),
interactive=not is_public, lines=4)
with gr.Row(visible=not kwargs['model_lock']):
with gr.Column(scale=50):
new_model = gr.Textbox(label="New Model name/path", interactive=not is_public)
with gr.Column(scale=50):
new_lora = gr.Textbox(label="New LORA name/path", visible=kwargs['show_lora'],
interactive=not is_public)
with gr.Column(scale=50):
new_server = gr.Textbox(label="New Server url:port", interactive=not is_public)
with gr.Row():
add_model_lora_server_button = gr.Button("Add new Model, Lora, Server url:port", scale=0,
size='sm', interactive=not is_public)
with gr.TabItem("System"):
with gr.Row():
with gr.Column(scale=1):
side_bar_text = gr.Textbox('on', visible=False, interactive=False)
submit_buttons_text = gr.Textbox('on', visible=False, interactive=False)
side_bar_btn = gr.Button("Toggle SideBar", variant="secondary", size="sm")
submit_buttons_btn = gr.Button("Toggle Submit Buttons", variant="secondary", size="sm")
col_tabs_scale = gr.Slider(minimum=1, maximum=20, value=10, step=1, label='Window Size')
text_outputs_height = gr.Slider(minimum=100, maximum=2000, value=kwargs['height'] or 400,
step=50, label='Chat Height')
dark_mode_btn = gr.Button("Dark Mode", variant="secondary", size="sm")
with gr.Column(scale=4):
pass
system_visible0 = not is_public and not admin_pass
admin_row = gr.Row()
with admin_row:
with gr.Column(scale=1):
admin_pass_textbox = gr.Textbox(label="Admin Password", type='password',
visible=not system_visible0)
with gr.Column(scale=4):
pass
system_row = gr.Row(visible=system_visible0)
with system_row:
with gr.Column():
with gr.Row():
system_btn = gr.Button(value='Get System Info', size='sm')
system_text = gr.Textbox(label='System Info', interactive=False, show_copy_button=True)
with gr.Row():
system_input = gr.Textbox(label='System Info Dict Password', interactive=True,
visible=not is_public)
system_btn2 = gr.Button(value='Get System Info Dict', visible=not is_public, size='sm')
system_text2 = gr.Textbox(label='System Info Dict', interactive=False,
visible=not is_public, show_copy_button=True)
with gr.Row():
system_btn3 = gr.Button(value='Get Hash', visible=not is_public, size='sm')
system_text3 = gr.Textbox(label='Hash', interactive=False,
visible=not is_public, show_copy_button=True)
with gr.Row():
zip_btn = gr.Button("Zip", size='sm')
zip_text = gr.Textbox(label="Zip file name", interactive=False)
file_output = gr.File(interactive=False, label="Zip file to Download")
with gr.Row():
s3up_btn = gr.Button("S3UP", size='sm')
s3up_text = gr.Textbox(label='S3UP result', interactive=False)
with gr.TabItem("Terms of Service"):
description = ""
description += """<p><b> DISCLAIMERS: </b><ul><i><li>The model was trained on The Pile and other data, which may contain objectionable content. Use at own risk.</i></li>"""
if kwargs['load_8bit']:
description += """<i><li> Model is loaded in 8-bit and has other restrictions on this host. UX can be worse than non-hosted version.</i></li>"""
description += """<i><li>Conversations may be used to improve h2oGPT. Do not share sensitive information.</i></li>"""
if 'h2ogpt-research' in kwargs['base_model']:
description += """<i><li>Research demonstration only, not used for commercial purposes.</i></li>"""
description += """<i><li>By using h2oGPT, you accept our <a href="https://github.com/h2oai/h2ogpt/blob/main/docs/tos.md">Terms of Service</a></i></li></ul></p>"""
gr.Markdown(value=description, show_label=False, interactive=False)
with gr.TabItem("Hosts"):
gr.Markdown(f"""
{description_bottom}
{task_info_md}
""")
# Get flagged data
zip_data1 = functools.partial(zip_data, root_dirs=['flagged_data_points', kwargs['save_dir']])
zip_event = zip_btn.click(zip_data1, inputs=None, outputs=[file_output, zip_text], queue=False,
api_name='zip_data' if allow_api else None)
s3up_event = s3up_btn.click(s3up, inputs=zip_text, outputs=s3up_text, queue=False,
api_name='s3up_data' if allow_api else None)
def clear_file_list():
return None
def make_non_interactive(*args):
if len(args) == 1:
return gr.update(interactive=False)
else:
return tuple([gr.update(interactive=False)] * len(args))
def make_interactive(*args):
if len(args) == 1:
return gr.update(interactive=True)
else:
return tuple([gr.update(interactive=True)] * len(args))
# Add to UserData or custom user db
update_db_func = functools.partial(update_user_db,
dbs=dbs,
db_type=db_type,
use_openai_embedding=use_openai_embedding,
hf_embedding_model=hf_embedding_model,
captions_model=captions_model,
enable_captions=enable_captions,
caption_loader=caption_loader,
enable_ocr=enable_ocr,
enable_pdf_ocr=enable_pdf_ocr,
verbose=kwargs['verbose'],
n_jobs=kwargs['n_jobs'],
)
add_file_outputs = [fileup_output, langchain_mode]
add_file_kwargs = dict(fn=update_db_func,
inputs=[fileup_output, my_db_state, selection_docs_state, chunk, chunk_size,
langchain_mode],
outputs=add_file_outputs + [sources_text, doc_exception_text],
queue=queue,
api_name='add_file' if allow_api and allow_upload_to_user_data else None)
# then no need for add buttons, only single changeable db
eventdb1a = fileup_output.upload(make_non_interactive, inputs=add_file_outputs, outputs=add_file_outputs,
show_progress='minimal')
eventdb1 = eventdb1a.then(**add_file_kwargs, show_progress='full')
eventdb1b = eventdb1.then(make_interactive, inputs=add_file_outputs, outputs=add_file_outputs,
show_progress='minimal')
# deal with challenge to have fileup_output itself as input
add_file_kwargs2 = dict(fn=update_db_func,
inputs=[fileup_output_text, my_db_state, selection_docs_state, chunk, chunk_size,
langchain_mode],
outputs=add_file_outputs + [sources_text, doc_exception_text],
queue=queue,
api_name='add_file_api' if allow_api and allow_upload_to_user_data else None)
eventdb1_api = fileup_output_text.submit(**add_file_kwargs2, show_progress='full')
# note for update_user_db_func output is ignored for db
def clear_textbox():
return gr.Textbox.update(value='')
update_user_db_url_func = functools.partial(update_db_func, is_url=True)
add_url_outputs = [url_text, langchain_mode]
add_url_kwargs = dict(fn=update_user_db_url_func,
inputs=[url_text, my_db_state, selection_docs_state, chunk, chunk_size,
langchain_mode],
outputs=add_url_outputs + [sources_text, doc_exception_text],
queue=queue,
api_name='add_url' if allow_api and allow_upload_to_user_data else None)
eventdb2a = url_text.submit(fn=dummy_fun, inputs=url_text, outputs=url_text, queue=queue,
show_progress='minimal')
# work around https://github.com/gradio-app/gradio/issues/4733
eventdb2b = eventdb2a.then(make_non_interactive, inputs=add_url_outputs, outputs=add_url_outputs,
show_progress='minimal')
eventdb2 = eventdb2b.then(**add_url_kwargs, show_progress='full')
eventdb2c = eventdb2.then(make_interactive, inputs=add_url_outputs, outputs=add_url_outputs,
show_progress='minimal')
update_user_db_txt_func = functools.partial(update_db_func, is_txt=True)
add_text_outputs = [user_text_text, langchain_mode]
add_text_kwargs = dict(fn=update_user_db_txt_func,
inputs=[user_text_text, my_db_state, selection_docs_state, chunk, chunk_size,
langchain_mode],
outputs=add_text_outputs + [sources_text, doc_exception_text],
queue=queue,
api_name='add_text' if allow_api and allow_upload_to_user_data else None
)
eventdb3a = user_text_text.submit(fn=dummy_fun, inputs=user_text_text, outputs=user_text_text, queue=queue,
show_progress='minimal')
eventdb3b = eventdb3a.then(make_non_interactive, inputs=add_text_outputs, outputs=add_text_outputs,
show_progress='minimal')
eventdb3 = eventdb3b.then(**add_text_kwargs, show_progress='full')
eventdb3c = eventdb3.then(make_interactive, inputs=add_text_outputs, outputs=add_text_outputs,
show_progress='minimal')
db_events = [eventdb1a, eventdb1, eventdb1b, eventdb1_api,
eventdb2a, eventdb2, eventdb2b, eventdb2c,
eventdb3a, eventdb3b, eventdb3, eventdb3c]
get_sources1 = functools.partial(get_sources, dbs=dbs, docs_state0=docs_state0)
# if change collection source, must clear doc selections from it to avoid inconsistency
def clear_doc_choice():
return gr.Dropdown.update(choices=docs_state0, value=DocumentChoice.ALL.value)
langchain_mode.change(clear_doc_choice, inputs=None, outputs=document_choice, queue=False)
def resize_col_tabs(x):
return gr.Dropdown.update(scale=x)
col_tabs_scale.change(fn=resize_col_tabs, inputs=col_tabs_scale, outputs=col_tabs, queue=False)
def resize_chatbots(x, num_model_lock=0):
if num_model_lock == 0:
num_model_lock = 3 # 2 + 1 (which is dup of first)
else:
num_model_lock = 2 + num_model_lock
return tuple([gr.update(height=x)] * num_model_lock)
resize_chatbots_func = functools.partial(resize_chatbots, num_model_lock=len(text_outputs))
text_outputs_height.change(fn=resize_chatbots_func, inputs=text_outputs_height,
outputs=[text_output, text_output2] + text_outputs, queue=False)
def update_dropdown(x):
return gr.Dropdown.update(choices=x, value=[docs_state0[0]])
get_sources_args = dict(fn=get_sources1, inputs=[my_db_state, langchain_mode],
outputs=[file_source, docs_state],
queue=queue,
api_name='get_sources' if allow_api else None)
eventdb7 = get_sources_btn.click(**get_sources_args) \
.then(fn=update_dropdown, inputs=docs_state, outputs=document_choice)
# show button, else only show when add. Could add to above get_sources for download/dropdown, but bit much maybe
show_sources1 = functools.partial(get_source_files_given_langchain_mode, dbs=dbs)
eventdb8 = show_sources_btn.click(fn=show_sources1, inputs=[my_db_state, langchain_mode], outputs=sources_text,
api_name='show_sources' if allow_api else None)
def update_viewable_dropdown(x):
return gr.Dropdown.update(choices=x,
value=viewable_docs_state0[0] if len(viewable_docs_state0) > 0 else None)
get_viewable_sources1 = functools.partial(get_sources, dbs=dbs, docs_state0=viewable_docs_state0)
get_viewable_sources_args = dict(fn=get_viewable_sources1, inputs=[my_db_state, langchain_mode],
outputs=[file_source, viewable_docs_state],
queue=queue,
api_name='get_viewable_sources' if allow_api else None)
eventdb12 = get_viewable_sources_btn.click(**get_viewable_sources_args) \
.then(fn=update_viewable_dropdown, inputs=viewable_docs_state,
outputs=view_document_choice)
def show_doc(file):
dummy1 = gr.update(visible=False, value=None)
dummy_ret = dummy1, dummy1, dummy1, dummy1
if not isinstance(file, str):
return dummy_ret
if file.endswith('.md'):
try:
with open(file, 'rt') as f:
content = f.read()
return dummy1, dummy1, dummy1, gr.update(visible=True, value=content)
except:
return dummy_ret
if file.endswith('.py'):
try:
with open(file, 'rt') as f:
content = f.read()
content = f"```python\n{content}\n```"
return dummy1, dummy1, dummy1, gr.update(visible=True, value=content)
except:
return dummy_ret
if file.endswith('.txt') or file.endswith('.rst') or file.endswith('.rtf') or file.endswith('.toml'):
try:
with open(file, 'rt') as f:
content = f.read()
content = f"```text\n{content}\n```"
return dummy1, dummy1, dummy1, gr.update(visible=True, value=content)
except:
return dummy_ret
func = None
if file.endswith(".csv"):
func = pd.read_csv
elif file.endswith(".pickle"):
func = pd.read_pickle
elif file.endswith(".xls") or file.endswith("xlsx"):
func = pd.read_excel
elif file.endswith('.json'):
func = pd.read_json
elif file.endswith('.xml'):
func = pd.read_xml
if func is not None:
try:
df = func(file).head(100)
except:
return dummy_ret
return dummy1, gr.update(visible=True, value=df), dummy1, dummy1
port = int(os.getenv('GRADIO_SERVER_PORT', '7860'))
import pathlib
absolute_path_string = os.path.abspath(file)
url_path = pathlib.Path(absolute_path_string).as_uri()
url = get_url(absolute_path_string, from_str=True)
img_url = url.replace("""<a href=""", """<img src=""")
if file.endswith('.png') or file.endswith('.jpg') or file.endswith('.jpeg'):
return gr.update(visible=True, value=img_url), dummy1, dummy1, dummy1
elif file.endswith('.pdf') or 'arxiv.org/pdf' in file:
if file.startswith('http') or file.startswith('https'):
# if file is online, then might as well use google(?)
document1 = file
return gr.update(visible=True,
value=f"""<iframe width="1000" height="800" src="https://docs.google.com/viewerng/viewer?url={document1}&embedded=true" frameborder="0" height="100%" width="100%">
</iframe>
"""), dummy1, dummy1, dummy1
else:
ip = get_local_ip()
document1 = url_path.replace('file://', f'http://{ip}:{port}/')
# document1 = url
return gr.update(visible=True, value=f"""<object data="{document1}" type="application/pdf">
<iframe src="https://docs.google.com/viewer?url={document1}&embedded=true"></iframe>
</object>"""), dummy1, dummy1, dummy1
else:
return dummy_ret
view_document_choice.select(fn=show_doc, inputs=view_document_choice,
outputs=[doc_view, doc_view2, doc_view3, doc_view4])
# Get inputs to evaluate() and make_db()
# don't deepcopy, can contain model itself
all_kwargs = kwargs.copy()
all_kwargs.update(locals())
refresh_sources1 = functools.partial(update_and_get_source_files_given_langchain_mode,
**get_kwargs(update_and_get_source_files_given_langchain_mode,
exclude_names=['db1s', 'langchain_mode', 'chunk',
'chunk_size'],
**all_kwargs))
eventdb9 = refresh_sources_btn.click(fn=refresh_sources1,
inputs=[my_db_state, langchain_mode, chunk, chunk_size],
outputs=sources_text,
api_name='refresh_sources' if allow_api else None)
def check_admin_pass(x):
return gr.update(visible=x == admin_pass)
def close_admin(x):
return gr.update(visible=not (x == admin_pass))
admin_pass_textbox.submit(check_admin_pass, inputs=admin_pass_textbox, outputs=system_row, queue=False) \
.then(close_admin, inputs=admin_pass_textbox, outputs=admin_row, queue=False)
def add_langchain_mode(db1s, selection_docs_state1, langchain_mode1, y):
for k in db1s:
set_userid(db1s[k])
langchain_modes = selection_docs_state1['langchain_modes']
langchain_mode_paths = selection_docs_state1['langchain_mode_paths']
visible_langchain_modes = selection_docs_state1['visible_langchain_modes']
user_path = None
valid = True
y2 = y.strip().replace(' ', '').split(',')
if len(y2) >= 1:
langchain_mode2 = y2[0]
if len(langchain_mode2) >= 3 and langchain_mode2.isalnum():
# real restriction is:
# ValueError: Expected collection name that (1) contains 3-63 characters, (2) starts and ends with an alphanumeric character, (3) otherwise contains only alphanumeric characters, underscores or hyphens (-), (4) contains no two consecutive periods (..) and (5) is not a valid IPv4 address, got me
# but just make simpler
user_path = y2[1] if len(y2) > 1 else None # assume scratch if don't have user_path
if user_path in ['', "''"]:
# for scratch spaces
user_path = None
if langchain_mode2 in langchain_modes_intrinsic:
user_path = None
textbox = "Invalid access to use internal name: %s" % langchain_mode2
valid = False
langchain_mode2 = langchain_mode1
elif user_path and allow_upload_to_user_data or not user_path and allow_upload_to_my_data:
langchain_mode_paths.update({langchain_mode2: user_path})
if langchain_mode2 not in visible_langchain_modes:
visible_langchain_modes.append(langchain_mode2)
if langchain_mode2 not in langchain_modes:
langchain_modes.append(langchain_mode2)
textbox = ''
if user_path:
makedirs(user_path, exist_ok=True)
else:
valid = False
langchain_mode2 = langchain_mode1
textbox = "Invalid access. user allowed: %s " \
"scratch allowed: %s" % (allow_upload_to_user_data, allow_upload_to_my_data)
else:
valid = False
langchain_mode2 = langchain_mode1
textbox = "Invalid, collection must be >=3 characters and alphanumeric"
else:
valid = False
langchain_mode2 = langchain_mode1
textbox = "Invalid, must be like UserData2, user_path2"
selection_docs_state1 = update_langchain_mode_paths(db1s, selection_docs_state1)
df_langchain_mode_paths1 = get_df_langchain_mode_paths(selection_docs_state1)
choices = get_langchain_choices(selection_docs_state1)
if valid and not user_path:
# needs to have key for it to make it known different from userdata case in _update_user_db()
db1s[langchain_mode2] = [None, None]
if valid:
save_collection_names(langchain_modes, visible_langchain_modes, langchain_mode_paths, LangChainMode,
db1s)
return db1s, selection_docs_state1, gr.update(choices=choices,
value=langchain_mode2), textbox, df_langchain_mode_paths1
def remove_langchain_mode(db1s, selection_docs_state1, langchain_mode1, langchain_mode2, dbsu=None):
for k in db1s:
set_userid(db1s[k])
assert dbsu is not None
langchain_modes = selection_docs_state1['langchain_modes']
langchain_mode_paths = selection_docs_state1['langchain_mode_paths']
visible_langchain_modes = selection_docs_state1['visible_langchain_modes']
if langchain_mode2 in db1s and not allow_upload_to_my_data or \
dbsu is not None and langchain_mode2 in dbsu and not allow_upload_to_user_data or \
langchain_mode2 in langchain_modes_intrinsic:
# NOTE: Doesn't fail if remove MyData, but didn't debug odd behavior seen with upload after gone
textbox = "Invalid access, cannot remove %s" % langchain_mode2
df_langchain_mode_paths1 = get_df_langchain_mode_paths(selection_docs_state1)
else:
# change global variables
if langchain_mode2 in visible_langchain_modes:
visible_langchain_modes.remove(langchain_mode2)
textbox = ""
else:
textbox = "%s was not visible" % langchain_mode2
if langchain_mode2 in langchain_modes:
langchain_modes.remove(langchain_mode2)
if langchain_mode2 in langchain_mode_paths:
langchain_mode_paths.pop(langchain_mode2)
if langchain_mode2 in db1s:
# remove db entirely, so not in list, else need to manage visible list in update_langchain_mode_paths()
# FIXME: Remove location?
if langchain_mode2 != LangChainMode.MY_DATA.value:
# don't remove last MyData, used as user hash
db1s.pop(langchain_mode2)
# only show
selection_docs_state1 = update_langchain_mode_paths(db1s, selection_docs_state1)
df_langchain_mode_paths1 = get_df_langchain_mode_paths(selection_docs_state1)
save_collection_names(langchain_modes, visible_langchain_modes, langchain_mode_paths, LangChainMode,
db1s)
return db1s, selection_docs_state1, \
gr.update(choices=get_langchain_choices(selection_docs_state1),
value=langchain_mode2), textbox, df_langchain_mode_paths1
new_langchain_mode_text.submit(fn=add_langchain_mode,
inputs=[my_db_state, selection_docs_state, langchain_mode,
new_langchain_mode_text],
outputs=[my_db_state, selection_docs_state, langchain_mode,
new_langchain_mode_text,
langchain_mode_path_text],
api_name='new_langchain_mode_text' if allow_api and allow_upload_to_user_data else None)
remove_langchain_mode_func = functools.partial(remove_langchain_mode, dbsu=dbs)
remove_langchain_mode_text.submit(fn=remove_langchain_mode_func,
inputs=[my_db_state, selection_docs_state, langchain_mode,
remove_langchain_mode_text],
outputs=[my_db_state, selection_docs_state, langchain_mode,
remove_langchain_mode_text,
langchain_mode_path_text],
api_name='remove_langchain_mode_text' if allow_api and allow_upload_to_user_data else None)
def update_langchain_gr(db1s, selection_docs_state1, langchain_mode1):
for k in db1s:
set_userid(db1s[k])
langchain_modes = selection_docs_state1['langchain_modes']
langchain_mode_paths = selection_docs_state1['langchain_mode_paths']
visible_langchain_modes = selection_docs_state1['visible_langchain_modes']
# in-place
# update user collaborative collections
update_langchain(langchain_modes, visible_langchain_modes, langchain_mode_paths, '')
# update scratch single-user collections
user_hash = db1s.get(LangChainMode.MY_DATA.value, '')[1]
update_langchain(langchain_modes, visible_langchain_modes, langchain_mode_paths, user_hash)
selection_docs_state1 = update_langchain_mode_paths(db1s, selection_docs_state1)
df_langchain_mode_paths1 = get_df_langchain_mode_paths(selection_docs_state1)
return selection_docs_state1, \
gr.update(choices=get_langchain_choices(selection_docs_state1),
value=langchain_mode1), df_langchain_mode_paths1
load_langchain.click(fn=update_langchain_gr,
inputs=[my_db_state, selection_docs_state, langchain_mode],
outputs=[selection_docs_state, langchain_mode, langchain_mode_path_text],
api_name='load_langchain' if allow_api and allow_upload_to_user_data else None)
inputs_list, inputs_dict = get_inputs_list(all_kwargs, kwargs['model_lower'], model_id=1)
inputs_list2, inputs_dict2 = get_inputs_list(all_kwargs, kwargs['model_lower'], model_id=2)
from functools import partial
kwargs_evaluate = {k: v for k, v in all_kwargs.items() if k in inputs_kwargs_list}
# ensure present
for k in inputs_kwargs_list:
assert k in kwargs_evaluate, "Missing %s" % k
def evaluate_nochat(*args1, default_kwargs1=None, str_api=False, **kwargs1):
args_list = list(args1)
if str_api:
user_kwargs = args_list[len(input_args_list)]
assert isinstance(user_kwargs, str)
user_kwargs = ast.literal_eval(user_kwargs)
else:
user_kwargs = {k: v for k, v in zip(eval_func_param_names, args_list[len(input_args_list):])}
# only used for submit_nochat_api
user_kwargs['chat'] = False
if 'stream_output' not in user_kwargs:
user_kwargs['stream_output'] = False
if 'langchain_mode' not in user_kwargs:
# if user doesn't specify, then assume disabled, not use default
user_kwargs['langchain_mode'] = 'Disabled'
if 'langchain_action' not in user_kwargs:
user_kwargs['langchain_action'] = LangChainAction.QUERY.value
if 'langchain_agents' not in user_kwargs:
user_kwargs['langchain_agents'] = []
set1 = set(list(default_kwargs1.keys()))
set2 = set(eval_func_param_names)
assert set1 == set2, "Set diff: %s %s: %s" % (set1, set2, set1.symmetric_difference(set2))
# correct ordering. Note some things may not be in default_kwargs, so can't be default of user_kwargs.get()
model_state1 = args_list[0]
my_db_state1 = args_list[1]
selection_docs_state1 = args_list[2]
args_list = [user_kwargs[k] if k in user_kwargs and user_kwargs[k] is not None else default_kwargs1[k] for k
in eval_func_param_names]
assert len(args_list) == len(eval_func_param_names)
args_list = [model_state1, my_db_state1, selection_docs_state1] + args_list
try:
for res_dict in evaluate(*tuple(args_list), **kwargs1):
if str_api:
# full return of dict
yield res_dict
elif kwargs['langchain_mode'] == 'Disabled':
yield fix_text_for_gradio(res_dict['response'])
else:
yield '<br>' + fix_text_for_gradio(res_dict['response'])
finally:
clear_torch_cache()
clear_embeddings(user_kwargs['langchain_mode'], my_db_state1)
fun = partial(evaluate_nochat,
default_kwargs1=default_kwargs,
str_api=False,
**kwargs_evaluate)
fun2 = partial(evaluate_nochat,
default_kwargs1=default_kwargs,
str_api=False,
**kwargs_evaluate)
fun_with_dict_str = partial(evaluate_nochat,
default_kwargs1=default_kwargs,
str_api=True,
**kwargs_evaluate
)
dark_mode_btn.click(
None,
None,
None,
_js=get_dark_js(),
api_name="dark" if allow_api else None,
queue=False,
)
def visible_toggle(x):
x = 'off' if x == 'on' else 'on'
return x, gr.Column.update(visible=True if x == 'on' else False)
side_bar_btn.click(fn=visible_toggle,
inputs=side_bar_text,
outputs=[side_bar_text, side_bar],
queue=False)
submit_buttons_btn.click(fn=visible_toggle,
inputs=submit_buttons_text,
outputs=[submit_buttons_text, submit_buttons],
queue=False)
# examples after submit or any other buttons for chat or no chat
if kwargs['examples'] is not None and kwargs['show_examples']:
gr.Examples(examples=kwargs['examples'], inputs=inputs_list)
# Score
def score_last_response(*args, nochat=False, num_model_lock=0):
try:
if num_model_lock > 0:
# then lock way
args_list = list(args).copy()
outputs = args_list[-num_model_lock:]
score_texts1 = []
for output in outputs:
# same input, put into form good for _score_last_response()
args_list[-1] = output
score_texts1.append(
_score_last_response(*tuple(args_list), nochat=nochat,
num_model_lock=num_model_lock, prefix=''))
if len(score_texts1) > 1:
return "Response Scores: %s" % ' '.join(score_texts1)
else:
return "Response Scores: %s" % score_texts1[0]
else:
return _score_last_response(*args, nochat=nochat, num_model_lock=num_model_lock)
finally:
clear_torch_cache()
def _score_last_response(*args, nochat=False, num_model_lock=0, prefix='Response Score: '):
""" Similar to user() """
args_list = list(args)
smodel = score_model_state0['model']
stokenizer = score_model_state0['tokenizer']
sdevice = score_model_state0['device']
if memory_restriction_level > 0:
max_length_tokenize = 768 - 256 if memory_restriction_level <= 2 else 512 - 256
elif hasattr(stokenizer, 'model_max_length'):
max_length_tokenize = stokenizer.model_max_length
else:
# limit to 1024, not worth OOMing on reward score
max_length_tokenize = 2048 - 1024
cutoff_len = max_length_tokenize * 4 # restrict deberta related to max for LLM
if not nochat:
history = args_list[-1]
if history is None:
history = []
if smodel is not None and \
stokenizer is not None and \
sdevice is not None and \
history is not None and len(history) > 0 and \
history[-1] is not None and \
len(history[-1]) >= 2:
os.environ['TOKENIZERS_PARALLELISM'] = 'false'
question = history[-1][0]
answer = history[-1][1]
else:
return '%sNA' % prefix
else:
answer = args_list[-1]
instruction_nochat_arg_id = eval_func_param_names.index('instruction_nochat')
question = args_list[instruction_nochat_arg_id]
if question is None:
return '%sBad Question' % prefix
if answer is None:
return '%sBad Answer' % prefix
try:
score = score_qa(smodel, stokenizer, max_length_tokenize, question, answer, cutoff_len)
finally:
clear_torch_cache()
if isinstance(score, str):
return '%sNA' % prefix
return '{}{:.1%}'.format(prefix, score)
def noop_score_last_response(*args, **kwargs):
return "Response Score: Disabled"
if kwargs['score_model']:
score_fun = score_last_response
else:
score_fun = noop_score_last_response
score_args = dict(fn=score_fun,
inputs=inputs_list + [text_output],
outputs=[score_text],
)
score_args2 = dict(fn=partial(score_fun),
inputs=inputs_list2 + [text_output2],
outputs=[score_text2],
)
score_fun_func = functools.partial(score_fun, num_model_lock=len(text_outputs))
all_score_args = dict(fn=score_fun_func,
inputs=inputs_list + text_outputs,
outputs=score_text,
)
score_args_nochat = dict(fn=partial(score_fun, nochat=True),
inputs=inputs_list + [text_output_nochat],
outputs=[score_text_nochat],
)
def update_history(*args, undo=False, retry=False, sanitize_user_prompt=False):
"""
User that fills history for bot
:param args:
:param undo:
:param retry:
:param sanitize_user_prompt:
:return:
"""
args_list = list(args)
user_message = args_list[eval_func_param_names.index('instruction')] # chat only
input1 = args_list[eval_func_param_names.index('iinput')] # chat only
prompt_type1 = args_list[eval_func_param_names.index('prompt_type')]
langchain_mode1 = args_list[eval_func_param_names.index('langchain_mode')]
langchain_action1 = args_list[eval_func_param_names.index('langchain_action')]
langchain_agents1 = args_list[eval_func_param_names.index('langchain_agents')]
document_subset1 = args_list[eval_func_param_names.index('document_subset')]
document_choice1 = args_list[eval_func_param_names.index('document_choice')]
if not prompt_type1:
# shouldn't have to specify if CLI launched model
prompt_type1 = kwargs['prompt_type']
# apply back
args_list[eval_func_param_names.index('prompt_type')] = prompt_type1
if input1 and not user_message.endswith(':'):
user_message1 = user_message + ":" + input1
elif input1:
user_message1 = user_message + input1
else:
user_message1 = user_message
if sanitize_user_prompt:
from better_profanity import profanity
user_message1 = profanity.censor(user_message1)
history = args_list[-1]
if history is None:
# bad history
history = []
history = history.copy()
if undo:
if len(history) > 0:
history.pop()
return history
if retry:
if history:
history[-1][1] = None
return history
if user_message1 in ['', None, '\n']:
if not allow_empty_instruction(langchain_mode1, document_subset1, langchain_action1):
# reject non-retry submit/enter
return history
user_message1 = fix_text_for_gradio(user_message1)
return history + [[user_message1, None]]
def user(*args, undo=False, retry=False, sanitize_user_prompt=False):
return update_history(*args, undo=undo, retry=retry, sanitize_user_prompt=sanitize_user_prompt)
def all_user(*args, undo=False, retry=False, sanitize_user_prompt=False, num_model_lock=0):
args_list = list(args)
history_list = args_list[-num_model_lock:]
assert len(history_list) > 0, "Bad history list: %s" % history_list
for hi, history in enumerate(history_list):
if num_model_lock > 0:
hargs = args_list[:-num_model_lock].copy()
else:
hargs = args_list.copy()
hargs += [history]
history_list[hi] = update_history(*hargs, undo=undo, retry=retry,
sanitize_user_prompt=sanitize_user_prompt)
if len(history_list) > 1:
return tuple(history_list)
else:
return history_list[0]
def get_model_max_length(model_state1):
if model_state1 and not isinstance(model_state1["tokenizer"], str):
tokenizer = model_state1["tokenizer"]
elif model_state0 and not isinstance(model_state0["tokenizer"], str):
tokenizer = model_state0["tokenizer"]
else:
tokenizer = None
if tokenizer is not None:
return tokenizer.model_max_length
else:
return 2000
def prep_bot(*args, retry=False, which_model=0):
"""
:param args:
:param retry:
:param which_model: identifies which model if doing model_lock
API only called for which_model=0, default for inputs_list, but rest should ignore inputs_list
:return: last element is True if should run bot, False if should just yield history
"""
isize = len(input_args_list) + 1 # states + chat history
# don't deepcopy, can contain model itself
args_list = list(args).copy()
model_state1 = args_list[-isize]
my_db_state1 = args_list[-isize + 1]
selection_docs_state1 = args_list[-isize + 2]
history = args_list[-1]
prompt_type1 = args_list[eval_func_param_names.index('prompt_type')]
prompt_dict1 = args_list[eval_func_param_names.index('prompt_dict')]
if model_state1['model'] is None or model_state1['model'] == no_model_str:
return history, None, None, None
args_list = args_list[:-isize] # only keep rest needed for evaluate()
langchain_mode1 = args_list[eval_func_param_names.index('langchain_mode')]
add_chat_history_to_context1 = args_list[eval_func_param_names.index('add_chat_history_to_context')]
langchain_action1 = args_list[eval_func_param_names.index('langchain_action')]
langchain_agents1 = args_list[eval_func_param_names.index('langchain_agents')]
document_subset1 = args_list[eval_func_param_names.index('document_subset')]
document_choice1 = args_list[eval_func_param_names.index('document_choice')]
if not history:
print("No history", flush=True)
history = []
return history, None, None, None
instruction1 = history[-1][0]
if retry and history:
# if retry, pop history and move onto bot stuff
instruction1 = history[-1][0]
history[-1][1] = None
elif not instruction1:
if not allow_empty_instruction(langchain_mode1, document_subset1, langchain_action1):
# if not retrying, then reject empty query
return history, None, None, None
elif len(history) > 0 and history[-1][1] not in [None, '']:
# reject submit button if already filled and not retrying
# None when not filling with '' to keep client happy
return history, None, None, None
# shouldn't have to specify in API prompt_type if CLI launched model, so prefer global CLI one if have it
prompt_type1, prompt_dict1 = update_prompt(prompt_type1, prompt_dict1, model_state1,
which_model=which_model)
# apply back to args_list for evaluate()
args_list[eval_func_param_names.index('prompt_type')] = prompt_type1
args_list[eval_func_param_names.index('prompt_dict')] = prompt_dict1
chat1 = args_list[eval_func_param_names.index('chat')]
model_max_length1 = get_model_max_length(model_state1)
context1 = history_to_context(history, langchain_mode1,
add_chat_history_to_context1,
prompt_type1, prompt_dict1, chat1,
model_max_length1, memory_restriction_level,
kwargs['keep_sources_in_context'])
args_list[0] = instruction1 # override original instruction with history from user
args_list[2] = context1
fun1 = partial(evaluate,
model_state1,
my_db_state1,
selection_docs_state1,
*tuple(args_list),
**kwargs_evaluate)
return history, fun1, langchain_mode1, my_db_state1
def get_response(fun1, history):
"""
bot that consumes history for user input
instruction (from input_list) itself is not consumed by bot
:return:
"""
if not fun1:
yield history, ''
return
try:
for output_fun in fun1():
output = output_fun['response']
extra = output_fun['sources'] # FIXME: can show sources in separate text box etc.
# ensure good visually, else markdown ignores multiple \n
bot_message = fix_text_for_gradio(output)
history[-1][1] = bot_message
yield history, ''
except StopIteration:
yield history, ''
except RuntimeError as e:
if "generator raised StopIteration" in str(e):
# assume last entry was bad, undo
history.pop()
yield history, ''
else:
if history and len(history) > 0 and len(history[0]) > 1 and history[-1][1] is None:
history[-1][1] = ''
yield history, str(e)
raise
except Exception as e:
# put error into user input
ex = "Exception: %s" % str(e)
if history and len(history) > 0 and len(history[0]) > 1 and history[-1][1] is None:
history[-1][1] = ''
yield history, ex
raise
finally:
clear_torch_cache()
return
def clear_embeddings(langchain_mode1, db1s):
# clear any use of embedding that sits on GPU, else keeps accumulating GPU usage even if clear torch cache
if db_type == 'chroma' and langchain_mode1 not in ['LLM', 'Disabled', None, '']:
from gpt_langchain import clear_embedding
db = dbs.get('langchain_mode1')
if db is not None and not isinstance(db, str):
clear_embedding(db)
if db1s is not None and langchain_mode1 in db1s:
db1 = db1s[langchain_mode1]
if len(db1) == 2:
clear_embedding(db1[0])
def bot(*args, retry=False):
history, fun1, langchain_mode1, db1 = prep_bot(*args, retry=retry)
try:
for res in get_response(fun1, history):
yield res
finally:
clear_torch_cache()
clear_embeddings(langchain_mode1, db1)
def all_bot(*args, retry=False, model_states1=None):
args_list = list(args).copy()
chatbots = args_list[-len(model_states1):]
args_list0 = args_list[:-len(model_states1)] # same for all models
exceptions = []
stream_output1 = args_list[eval_func_param_names.index('stream_output')]
max_time1 = args_list[eval_func_param_names.index('max_time')]
langchain_mode1 = args_list[eval_func_param_names.index('langchain_mode')]
isize = len(input_args_list) + 1 # states + chat history
db1s = None
try:
gen_list = []
for chatboti, (chatbot1, model_state1) in enumerate(zip(chatbots, model_states1)):
args_list1 = args_list0.copy()
args_list1.insert(-isize + 2,
model_state1) # insert at -2 so is at -3, and after chatbot1 added, at -4
# if at start, have None in response still, replace with '' so client etc. acts like normal
# assumes other parts of code treat '' and None as if no response yet from bot
# can't do this later in bot code as racy with threaded generators
if len(chatbot1) > 0 and len(chatbot1[-1]) == 2 and chatbot1[-1][1] is None:
chatbot1[-1][1] = ''
args_list1.append(chatbot1)
# so consistent with prep_bot()
# with model_state1 at -3, my_db_state1 at -2, and history(chatbot) at -1
# langchain_mode1 and my_db_state1 should be same for every bot
history, fun1, langchain_mode1, db1s = prep_bot(*tuple(args_list1), retry=retry,
which_model=chatboti)
gen1 = get_response(fun1, history)
if stream_output1:
gen1 = TimeoutIterator(gen1, timeout=0.01, sentinel=None, raise_on_exception=False)
# else timeout will truncate output for non-streaming case
gen_list.append(gen1)
bots_old = chatbots.copy()
exceptions_old = [''] * len(bots_old)
tgen0 = time.time()
for res1 in itertools.zip_longest(*gen_list):
if time.time() - tgen0 > max_time1:
print("Took too long: %s" % max_time1, flush=True)
break
bots = [x[0] if x is not None and not isinstance(x, BaseException) else y for x, y in
zip(res1, bots_old)]
bots_old = bots.copy()
def larger_str(x, y):
return x if len(x) > len(y) else y
exceptions = [x[1] if x is not None and not isinstance(x, BaseException) else larger_str(str(x), y)
for x, y in zip(res1, exceptions_old)]
exceptions_old = exceptions.copy()
def choose_exc(x):
# don't expose ports etc. to exceptions window
if is_public:
return "Endpoint unavailable or failed"
else:
return x
exceptions_str = '\n'.join(
['Model %s: %s' % (iix, choose_exc(x)) for iix, x in enumerate(exceptions) if
x not in [None, '', 'None']])
if len(bots) > 1:
yield tuple(bots + [exceptions_str])
else:
yield bots[0], exceptions_str
if exceptions:
exceptions = [x for x in exceptions if x not in ['', None, 'None']]
if exceptions:
print("Generate exceptions: %s" % exceptions, flush=True)
finally:
clear_torch_cache()
clear_embeddings(langchain_mode1, db1s)
# NORMAL MODEL
user_args = dict(fn=functools.partial(user, sanitize_user_prompt=kwargs['sanitize_user_prompt']),
inputs=inputs_list + [text_output],
outputs=text_output,
)
bot_args = dict(fn=bot,
inputs=inputs_list + [model_state, my_db_state, selection_docs_state] + [text_output],
outputs=[text_output, chat_exception_text],
)
retry_bot_args = dict(fn=functools.partial(bot, retry=True),
inputs=inputs_list + [model_state, my_db_state, selection_docs_state] + [text_output],
outputs=[text_output, chat_exception_text],
)
retry_user_args = dict(fn=functools.partial(user, retry=True),
inputs=inputs_list + [text_output],
outputs=text_output,
)
undo_user_args = dict(fn=functools.partial(user, undo=True),
inputs=inputs_list + [text_output],
outputs=text_output,
)
# MODEL2
user_args2 = dict(fn=functools.partial(user, sanitize_user_prompt=kwargs['sanitize_user_prompt']),
inputs=inputs_list2 + [text_output2],
outputs=text_output2,
)
bot_args2 = dict(fn=bot,
inputs=inputs_list2 + [model_state2, my_db_state, selection_docs_state] + [text_output2],
outputs=[text_output2, chat_exception_text],
)
retry_bot_args2 = dict(fn=functools.partial(bot, retry=True),
inputs=inputs_list2 + [model_state2, my_db_state, selection_docs_state] + [text_output2],
outputs=[text_output2, chat_exception_text],
)
retry_user_args2 = dict(fn=functools.partial(user, retry=True),
inputs=inputs_list2 + [text_output2],
outputs=text_output2,
)
undo_user_args2 = dict(fn=functools.partial(user, undo=True),
inputs=inputs_list2 + [text_output2],
outputs=text_output2,
)
# MODEL N
all_user_args = dict(fn=functools.partial(all_user,
sanitize_user_prompt=kwargs['sanitize_user_prompt'],
num_model_lock=len(text_outputs),
),
inputs=inputs_list + text_outputs,
outputs=text_outputs,
)
all_bot_args = dict(fn=functools.partial(all_bot, model_states1=model_states),
inputs=inputs_list + [my_db_state, selection_docs_state] + text_outputs,
outputs=text_outputs + [chat_exception_text],
)
all_retry_bot_args = dict(fn=functools.partial(all_bot, model_states1=model_states, retry=True),
inputs=inputs_list + [my_db_state, selection_docs_state] + text_outputs,
outputs=text_outputs + [chat_exception_text],
)
all_retry_user_args = dict(fn=functools.partial(all_user, retry=True,
sanitize_user_prompt=kwargs['sanitize_user_prompt'],
num_model_lock=len(text_outputs),
),
inputs=inputs_list + text_outputs,
outputs=text_outputs,
)
all_undo_user_args = dict(fn=functools.partial(all_user, undo=True,
sanitize_user_prompt=kwargs['sanitize_user_prompt'],
num_model_lock=len(text_outputs),
),
inputs=inputs_list + text_outputs,
outputs=text_outputs,
)
def clear_instruct():
return gr.Textbox.update(value='')
def deselect_radio_chats():
return gr.update(value=None)
def clear_all():
return gr.Textbox.update(value=''), gr.Textbox.update(value=''), gr.update(value=None), \
gr.Textbox.update(value=''), gr.Textbox.update(value='')
if kwargs['model_states']:
submits1 = submits2 = submits3 = []
submits4 = []
fun_source = [instruction.submit, submit.click, retry_btn.click]
fun_name = ['instruction', 'submit', 'retry']
user_args = [all_user_args, all_user_args, all_retry_user_args]
bot_args = [all_bot_args, all_bot_args, all_retry_bot_args]
for userargs1, botarg1, funn1, funs1 in zip(user_args, bot_args, fun_name, fun_source):
submit_event11 = funs1(fn=dummy_fun,
inputs=instruction, outputs=instruction, queue=queue)
submit_event1a = submit_event11.then(**userargs1, queue=queue,
api_name='%s' % funn1 if allow_api else None)
# if hit enter on new instruction for submitting new query, no longer the saved chat
submit_event1b = submit_event1a.then(clear_all, inputs=None,
outputs=[instruction, iinput, radio_chats, score_text,
score_text2],
queue=queue)
submit_event1c = submit_event1b.then(**botarg1,
api_name='%s_bot' % funn1 if allow_api else None,
queue=queue)
submit_event1d = submit_event1c.then(**all_score_args,
api_name='%s_bot_score' % funn1 if allow_api else None,
queue=queue)
submits1.extend([submit_event1a, submit_event1b, submit_event1c, submit_event1d])
# if undo, no longer the saved chat
submit_event4 = undo.click(fn=dummy_fun,
inputs=instruction, outputs=instruction, queue=queue) \
.then(**all_undo_user_args, api_name='undo' if allow_api else None) \
.then(clear_all, inputs=None, outputs=[instruction, iinput, radio_chats, score_text,
score_text2], queue=queue) \
.then(**all_score_args, api_name='undo_score' if allow_api else None)
submits4 = [submit_event4]
else:
# in case 2nd model, consume instruction first, so can clear quickly
# bot doesn't consume instruction itself, just history from user, so why works
submit_event11 = instruction.submit(fn=dummy_fun,
inputs=instruction, outputs=instruction, queue=queue)
submit_event1a = submit_event11.then(**user_args, queue=queue,
api_name='instruction' if allow_api else None)
# if hit enter on new instruction for submitting new query, no longer the saved chat
submit_event1a2 = submit_event1a.then(deselect_radio_chats, inputs=None, outputs=radio_chats, queue=queue)
submit_event1b = submit_event1a2.then(**user_args2, api_name='instruction2' if allow_api else None)
submit_event1c = submit_event1b.then(clear_instruct, None, instruction) \
.then(clear_instruct, None, iinput)
submit_event1d = submit_event1c.then(**bot_args, api_name='instruction_bot' if allow_api else None,
queue=queue)
submit_event1e = submit_event1d.then(**score_args,
api_name='instruction_bot_score' if allow_api else None,
queue=queue)
submit_event1f = submit_event1e.then(**bot_args2, api_name='instruction_bot2' if allow_api else None,
queue=queue)
submit_event1g = submit_event1f.then(**score_args2,
api_name='instruction_bot_score2' if allow_api else None, queue=queue)
submits1 = [submit_event1a, submit_event1a2, submit_event1b, submit_event1c, submit_event1d,
submit_event1e,
submit_event1f, submit_event1g]
submit_event21 = submit.click(fn=dummy_fun,
inputs=instruction, outputs=instruction, queue=queue)
submit_event2a = submit_event21.then(**user_args, api_name='submit' if allow_api else None)
# if submit new query, no longer the saved chat
submit_event2a2 = submit_event2a.then(deselect_radio_chats, inputs=None, outputs=radio_chats, queue=queue)
submit_event2b = submit_event2a2.then(**user_args2, api_name='submit2' if allow_api else None)
submit_event2c = submit_event2b.then(clear_all, inputs=None,
outputs=[instruction, iinput, radio_chats, score_text, score_text2],
queue=queue)
submit_event2d = submit_event2c.then(**bot_args, api_name='submit_bot' if allow_api else None, queue=queue)
submit_event2e = submit_event2d.then(**score_args,
api_name='submit_bot_score' if allow_api else None,
queue=queue)
submit_event2f = submit_event2e.then(**bot_args2, api_name='submit_bot2' if allow_api else None,
queue=queue)
submit_event2g = submit_event2f.then(**score_args2,
api_name='submit_bot_score2' if allow_api else None,
queue=queue)
submits2 = [submit_event2a, submit_event2a2, submit_event2b, submit_event2c, submit_event2d,
submit_event2e,
submit_event2f, submit_event2g]
submit_event31 = retry_btn.click(fn=dummy_fun,
inputs=instruction, outputs=instruction, queue=queue)
submit_event3a = submit_event31.then(**user_args, api_name='retry' if allow_api else None)
# if retry, no longer the saved chat
submit_event3a2 = submit_event3a.then(deselect_radio_chats, inputs=None, outputs=radio_chats, queue=queue)
submit_event3b = submit_event3a2.then(**user_args2, api_name='retry2' if allow_api else None)
submit_event3c = submit_event3b.then(clear_instruct, None, instruction) \
.then(clear_instruct, None, iinput)
submit_event3d = submit_event3c.then(**retry_bot_args, api_name='retry_bot' if allow_api else None,
queue=queue)
submit_event3e = submit_event3d.then(**score_args,
api_name='retry_bot_score' if allow_api else None,
queue=queue)
submit_event3f = submit_event3e.then(**retry_bot_args2, api_name='retry_bot2' if allow_api else None,
queue=queue)
submit_event3g = submit_event3f.then(**score_args2,
api_name='retry_bot_score2' if allow_api else None,
queue=queue)
submits3 = [submit_event3a, submit_event3a2, submit_event3b, submit_event3c, submit_event3d,
submit_event3e,
submit_event3f, submit_event3g]
# if undo, no longer the saved chat
submit_event4 = undo.click(fn=dummy_fun,
inputs=instruction, outputs=instruction, queue=queue) \
.then(**undo_user_args, api_name='undo' if allow_api else None) \
.then(**undo_user_args2, api_name='undo2' if allow_api else None) \
.then(clear_all, inputs=None, outputs=[instruction, iinput, radio_chats, score_text,
score_text2], queue=queue) \
.then(**score_args, api_name='undo_score' if allow_api else None) \
.then(**score_args2, api_name='undo_score2' if allow_api else None)
submits4 = [submit_event4]
# MANAGE CHATS
def dedup(short_chat, short_chats):
if short_chat not in short_chats:
return short_chat
for i in range(1, 1000):
short_chat_try = short_chat + "_" + str(i)
if short_chat_try not in short_chats:
return short_chat_try
# fallback and hope for best
short_chat = short_chat + "_" + str(random.random())
return short_chat
def get_short_chat(x, short_chats, short_len=20, words=4):
if x and len(x[0]) == 2 and x[0][0] is not None:
short_chat = ' '.join(x[0][0][:short_len].split(' ')[:words]).strip()
if not short_chat:
# e.g.summarization, try using answer
short_chat = ' '.join(x[0][1][:short_len].split(' ')[:words]).strip()
if not short_chat:
short_chat = 'Unk'
short_chat = dedup(short_chat, short_chats)
else:
short_chat = None
return short_chat
def is_chat_same(x, y):
# <p> etc. added in chat, try to remove some of that to help avoid dup entries when hit new conversation
is_same = True
# length of conversation has to be same
if len(x) != len(y):
return False
if len(x) != len(y):
return False
for stepx, stepy in zip(x, y):
if len(stepx) != len(stepy):
# something off with a conversation
return False
for stepxx, stepyy in zip(stepx, stepy):
if len(stepxx) != len(stepyy):
# something off with a conversation
return False
if len(stepxx) != 2:
# something off
return False
if len(stepyy) != 2:
# something off
return False
questionx = stepxx[0].replace('<p>', '').replace('</p>', '') if stepxx[0] is not None else None
answerx = stepxx[1].replace('<p>', '').replace('</p>', '') if stepxx[1] is not None else None
questiony = stepyy[0].replace('<p>', '').replace('</p>', '') if stepyy[0] is not None else None
answery = stepyy[1].replace('<p>', '').replace('</p>', '') if stepyy[1] is not None else None
if questionx != questiony or answerx != answery:
return False
return is_same
def save_chat(*args, chat_is_list=False):
args_list = list(args)
if not chat_is_list:
# list of chatbot histories,
# can't pass in list with list of chatbot histories and state due to gradio limits
chat_list = args_list[:-1]
else:
assert len(args_list) == 2
chat_list = args_list[0]
# if old chat file with single chatbot, get into shape
if isinstance(chat_list, list) and len(chat_list) > 0 and isinstance(chat_list[0], list) and len(
chat_list[0]) == 2 and isinstance(chat_list[0][0], str) and isinstance(chat_list[0][1], str):
chat_list = [chat_list]
# remove None histories
chat_list_not_none = [x for x in chat_list if x and len(x) > 0 and len(x[0]) == 2 and x[0][1] is not None]
chat_list_none = [x for x in chat_list if x not in chat_list_not_none]
if len(chat_list_none) > 0 and len(chat_list_not_none) == 0:
raise ValueError("Invalid chat file")
# dict with keys of short chat names, values of list of list of chatbot histories
chat_state1 = args_list[-1]
short_chats = list(chat_state1.keys())
if len(chat_list_not_none) > 0:
# make short_chat key from only first history, based upon question that is same anyways
chat_first = chat_list_not_none[0]
short_chat = get_short_chat(chat_first, short_chats)
if short_chat:
old_chat_lists = list(chat_state1.values())
already_exists = any([is_chat_same(chat_list, x) for x in old_chat_lists])
if not already_exists:
chat_state1[short_chat] = chat_list.copy()
# reverse so newest at top
choices = list(chat_state1.keys()).copy()
choices.reverse()
return chat_state1, gr.update(choices=choices, value=None)
def switch_chat(chat_key, chat_state1, num_model_lock=0):
chosen_chat = chat_state1[chat_key]
# deal with possible different size of chat list vs. current list
ret_chat = [None] * (2 + num_model_lock)
for chati in range(0, 2 + num_model_lock):
ret_chat[chati % len(ret_chat)] = chosen_chat[chati % len(chosen_chat)]
return tuple(ret_chat)
def clear_texts(*args):
return tuple([gr.Textbox.update(value='')] * len(args))
def clear_scores():
return gr.Textbox.update(value=res_value), \
gr.Textbox.update(value='Response Score: NA'), \
gr.Textbox.update(value='Response Score: NA')
switch_chat_fun = functools.partial(switch_chat, num_model_lock=len(text_outputs))
radio_chats.input(switch_chat_fun,
inputs=[radio_chats, chat_state],
outputs=[text_output, text_output2] + text_outputs) \
.then(clear_scores, outputs=[score_text, score_text2, score_text_nochat])
def remove_chat(chat_key, chat_state1):
if isinstance(chat_key, str):
chat_state1.pop(chat_key, None)
return gr.update(choices=list(chat_state1.keys()), value=None), chat_state1
remove_chat_event = remove_chat_btn.click(remove_chat,
inputs=[radio_chats, chat_state], outputs=[radio_chats, chat_state],
queue=False, api_name='remove_chat')
def get_chats1(chat_state1):
base = 'chats'
makedirs(base, exist_ok=True)
filename = os.path.join(base, 'chats_%s.json' % str(uuid.uuid4()))
with open(filename, "wt") as f:
f.write(json.dumps(chat_state1, indent=2))
return filename
export_chat_event = export_chats_btn.click(get_chats1, inputs=chat_state, outputs=chats_file, queue=False,
api_name='export_chats' if allow_api else None)
def add_chats_from_file(file, chat_state1, radio_chats1, chat_exception_text1):
if not file:
return None, chat_state1, gr.update(choices=list(chat_state1.keys()), value=None), chat_exception_text1
if isinstance(file, str):
files = [file]
else:
files = file
if not files:
return None, chat_state1, gr.update(choices=list(chat_state1.keys()), value=None), chat_exception_text1
chat_exception_list = []
for file1 in files:
try:
if hasattr(file1, 'name'):
file1 = file1.name
with open(file1, "rt") as f:
new_chats = json.loads(f.read())
for chat1_k, chat1_v in new_chats.items():
# ignore chat1_k, regenerate and de-dup to avoid loss
chat_state1, _ = save_chat(chat1_v, chat_state1, chat_is_list=True)
except BaseException as e:
t, v, tb = sys.exc_info()
ex = ''.join(traceback.format_exception(t, v, tb))
ex_str = "File %s exception: %s" % (file1, str(e))
print(ex_str, flush=True)
chat_exception_list.append(ex_str)
chat_exception_text1 = '\n'.join(chat_exception_list)
return None, chat_state1, gr.update(choices=list(chat_state1.keys()), value=None), chat_exception_text1
# note for update_user_db_func output is ignored for db
chatup_change_event = chatsup_output.change(add_chats_from_file,
inputs=[chatsup_output, chat_state, radio_chats,
chat_exception_text],
outputs=[chatsup_output, chat_state, radio_chats,
chat_exception_text],
queue=False,
api_name='add_to_chats' if allow_api else None)
clear_chat_event = clear_chat_btn.click(fn=clear_texts,
inputs=[text_output, text_output2] + text_outputs,
outputs=[text_output, text_output2] + text_outputs,
queue=False, api_name='clear' if allow_api else None) \
.then(deselect_radio_chats, inputs=None, outputs=radio_chats, queue=False) \
.then(clear_scores, outputs=[score_text, score_text2, score_text_nochat])
clear_event = save_chat_btn.click(save_chat,
inputs=[text_output, text_output2] + text_outputs + [chat_state],
outputs=[chat_state, radio_chats],
api_name='save_chat' if allow_api else None)
if kwargs['score_model']:
clear_event2 = clear_event.then(clear_scores, outputs=[score_text, score_text2, score_text_nochat])
# NOTE: clear of instruction/iinput for nochat has to come after score,
# because score for nochat consumes actual textbox, while chat consumes chat history filled by user()
no_chat_args = dict(fn=fun,
inputs=[model_state, my_db_state, selection_docs_state] + inputs_list,
outputs=text_output_nochat,
queue=queue,
)
submit_event_nochat = submit_nochat.click(**no_chat_args, api_name='submit_nochat' if allow_api else None) \
.then(clear_torch_cache) \
.then(**score_args_nochat, api_name='instruction_bot_score_nochat' if allow_api else None, queue=queue) \
.then(clear_instruct, None, instruction_nochat) \
.then(clear_instruct, None, iinput_nochat) \
.then(clear_torch_cache)
# copy of above with text box submission
submit_event_nochat2 = instruction_nochat.submit(**no_chat_args) \
.then(clear_torch_cache) \
.then(**score_args_nochat, queue=queue) \
.then(clear_instruct, None, instruction_nochat) \
.then(clear_instruct, None, iinput_nochat) \
.then(clear_torch_cache)
submit_event_nochat_api = submit_nochat_api.click(fun_with_dict_str,
inputs=[model_state, my_db_state, selection_docs_state,
inputs_dict_str],
outputs=text_output_nochat_api,
queue=True, # required for generator
api_name='submit_nochat_api' if allow_api else None) \
.then(clear_torch_cache)
def load_model(model_name, lora_weights, server_name, model_state_old, prompt_type_old, load_8bit,
use_gpu_id, gpu_id):
# ensure no API calls reach here
if is_public:
raise RuntimeError("Illegal access for %s" % model_name)
# ensure old model removed from GPU memory
if kwargs['debug']:
print("Pre-switch pre-del GPU memory: %s" % get_torch_allocated(), flush=True)
model0 = model_state0['model']
if isinstance(model_state_old['model'], str) and model0 is not None:
# best can do, move model loaded at first to CPU
model0.cpu()
if model_state_old['model'] is not None and not isinstance(model_state_old['model'], str):
try:
model_state_old['model'].cpu()
except Exception as e:
# sometimes hit NotImplementedError: Cannot copy out of meta tensor; no data!
print("Unable to put model on CPU: %s" % str(e), flush=True)
del model_state_old['model']
model_state_old['model'] = None
if model_state_old['tokenizer'] is not None and not isinstance(model_state_old['tokenizer'], str):
del model_state_old['tokenizer']
model_state_old['tokenizer'] = None
clear_torch_cache()
if kwargs['debug']:
print("Pre-switch post-del GPU memory: %s" % get_torch_allocated(), flush=True)
if model_name is None or model_name == no_model_str:
# no-op if no model, just free memory
# no detranscribe needed for model, never go into evaluate
lora_weights = no_lora_str
server_name = no_server_str
return [None, None, None, model_name, server_name], \
model_name, lora_weights, server_name, prompt_type_old, \
gr.Slider.update(maximum=256), \
gr.Slider.update(maximum=256)
# don't deepcopy, can contain model itself
all_kwargs1 = all_kwargs.copy()
all_kwargs1['base_model'] = model_name.strip()
all_kwargs1['load_8bit'] = load_8bit
all_kwargs1['use_gpu_id'] = use_gpu_id
all_kwargs1['gpu_id'] = int(gpu_id) # detranscribe
model_lower = model_name.strip().lower()
if model_lower in inv_prompt_type_to_model_lower:
prompt_type1 = inv_prompt_type_to_model_lower[model_lower]
else:
prompt_type1 = prompt_type_old
# detranscribe
if lora_weights == no_lora_str:
lora_weights = ''
all_kwargs1['lora_weights'] = lora_weights.strip()
if server_name == no_server_str:
server_name = ''
all_kwargs1['inference_server'] = server_name.strip()
model1, tokenizer1, device1 = get_model(reward_type=False,
**get_kwargs(get_model, exclude_names=['reward_type'],
**all_kwargs1))
clear_torch_cache()
tokenizer_base_model = model_name
prompt_dict1, error0 = get_prompt(prompt_type1, '',
chat=False, context='', reduced=False, making_context=False,
return_dict=True)
model_state_new = dict(model=model1, tokenizer=tokenizer1, device=device1,
base_model=model_name, tokenizer_base_model=tokenizer_base_model,
lora_weights=lora_weights, inference_server=server_name,
prompt_type=prompt_type1, prompt_dict=prompt_dict1,
)
max_max_new_tokens1 = get_max_max_new_tokens(model_state_new, **kwargs)
if kwargs['debug']:
print("Post-switch GPU memory: %s" % get_torch_allocated(), flush=True)
return model_state_new, model_name, lora_weights, server_name, prompt_type1, \
gr.Slider.update(maximum=max_max_new_tokens1), \
gr.Slider.update(maximum=max_max_new_tokens1)
def get_prompt_str(prompt_type1, prompt_dict1, which=0):
if prompt_type1 in ['', None]:
print("Got prompt_type %s: %s" % (which, prompt_type1), flush=True)
return str({})
prompt_dict1, prompt_dict_error = get_prompt(prompt_type1, prompt_dict1, chat=False, context='',
reduced=False, making_context=False, return_dict=True)
if prompt_dict_error:
return str(prompt_dict_error)
else:
# return so user can manipulate if want and use as custom
return str(prompt_dict1)
get_prompt_str_func1 = functools.partial(get_prompt_str, which=1)
get_prompt_str_func2 = functools.partial(get_prompt_str, which=2)
prompt_type.change(fn=get_prompt_str_func1, inputs=[prompt_type, prompt_dict], outputs=prompt_dict, queue=False)
prompt_type2.change(fn=get_prompt_str_func2, inputs=[prompt_type2, prompt_dict2], outputs=prompt_dict2,
queue=False)
def dropdown_prompt_type_list(x):
return gr.Dropdown.update(value=x)
def chatbot_list(x, model_used_in):
return gr.Textbox.update(label=f'h2oGPT [Model: {model_used_in}]')
load_model_args = dict(fn=load_model,
inputs=[model_choice, lora_choice, server_choice, model_state, prompt_type,
model_load8bit_checkbox, model_use_gpu_id_checkbox, model_gpu],
outputs=[model_state, model_used, lora_used, server_used,
# if prompt_type changes, prompt_dict will change via change rule
prompt_type, max_new_tokens, min_new_tokens,
])
prompt_update_args = dict(fn=dropdown_prompt_type_list, inputs=prompt_type, outputs=prompt_type)
chatbot_update_args = dict(fn=chatbot_list, inputs=[text_output, model_used], outputs=text_output)
nochat_update_args = dict(fn=chatbot_list, inputs=[text_output_nochat, model_used], outputs=text_output_nochat)
load_model_event = load_model_button.click(**load_model_args,
api_name='load_model' if allow_api and is_public else None) \
.then(**prompt_update_args) \
.then(**chatbot_update_args) \
.then(**nochat_update_args) \
.then(clear_torch_cache)
load_model_args2 = dict(fn=load_model,
inputs=[model_choice2, lora_choice2, server_choice2, model_state2, prompt_type2,
model_load8bit_checkbox2, model_use_gpu_id_checkbox2, model_gpu2],
outputs=[model_state2, model_used2, lora_used2, server_used2,
# if prompt_type2 changes, prompt_dict2 will change via change rule
prompt_type2, max_new_tokens2, min_new_tokens2
])
prompt_update_args2 = dict(fn=dropdown_prompt_type_list, inputs=prompt_type2, outputs=prompt_type2)
chatbot_update_args2 = dict(fn=chatbot_list, inputs=[text_output2, model_used2], outputs=text_output2)
load_model_event2 = load_model_button2.click(**load_model_args2,
api_name='load_model2' if allow_api and is_public else None) \
.then(**prompt_update_args2) \
.then(**chatbot_update_args2) \
.then(clear_torch_cache)
def dropdown_model_lora_server_list(model_list0, model_x,
lora_list0, lora_x,
server_list0, server_x,
model_used1, lora_used1, server_used1,
model_used2, lora_used2, server_used2,
):
model_new_state = [model_list0[0] + [model_x]]
model_new_options = [*model_new_state[0]]
x1 = model_x if model_used1 == no_model_str else model_used1
x2 = model_x if model_used2 == no_model_str else model_used2
ret1 = [gr.Dropdown.update(value=x1, choices=model_new_options),
gr.Dropdown.update(value=x2, choices=model_new_options),
'', model_new_state]
lora_new_state = [lora_list0[0] + [lora_x]]
lora_new_options = [*lora_new_state[0]]
# don't switch drop-down to added lora if already have model loaded
x1 = lora_x if model_used1 == no_model_str else lora_used1
x2 = lora_x if model_used2 == no_model_str else lora_used2
ret2 = [gr.Dropdown.update(value=x1, choices=lora_new_options),
gr.Dropdown.update(value=x2, choices=lora_new_options),
'', lora_new_state]
server_new_state = [server_list0[0] + [server_x]]
server_new_options = [*server_new_state[0]]
# don't switch drop-down to added server if already have model loaded
x1 = server_x if model_used1 == no_model_str else server_used1
x2 = server_x if model_used2 == no_model_str else server_used2
ret3 = [gr.Dropdown.update(value=x1, choices=server_new_options),
gr.Dropdown.update(value=x2, choices=server_new_options),
'', server_new_state]
return tuple(ret1 + ret2 + ret3)
add_model_lora_server_event = \
add_model_lora_server_button.click(fn=dropdown_model_lora_server_list,
inputs=[model_options_state, new_model] +
[lora_options_state, new_lora] +
[server_options_state, new_server] +
[model_used, lora_used, server_used] +
[model_used2, lora_used2, server_used2],
outputs=[model_choice, model_choice2, new_model, model_options_state] +
[lora_choice, lora_choice2, new_lora, lora_options_state] +
[server_choice, server_choice2, new_server,
server_options_state],
queue=False)
go_event = go_btn.click(lambda: gr.update(visible=False), None, go_btn, api_name="go" if allow_api else None,
queue=False) \
.then(lambda: gr.update(visible=True), None, normal_block, queue=False) \
.then(**load_model_args, queue=False).then(**prompt_update_args, queue=False)
def compare_textbox_fun(x):
return gr.Textbox.update(visible=x)
def compare_column_fun(x):
return gr.Column.update(visible=x)
def compare_prompt_fun(x):
return gr.Dropdown.update(visible=x)
def slider_fun(x):
return gr.Slider.update(visible=x)
compare_checkbox.select(compare_textbox_fun, compare_checkbox, text_output2,
api_name="compare_checkbox" if allow_api else None) \
.then(compare_column_fun, compare_checkbox, col_model2) \
.then(compare_prompt_fun, compare_checkbox, prompt_type2) \
.then(compare_textbox_fun, compare_checkbox, score_text2) \
.then(slider_fun, compare_checkbox, max_new_tokens2) \
.then(slider_fun, compare_checkbox, min_new_tokens2)
# FIXME: add score_res2 in condition, but do better
# callback for logging flagged input/output
callback.setup(inputs_list + [text_output, text_output2] + text_outputs, "flagged_data_points")
flag_btn.click(lambda *args: callback.flag(args), inputs_list + [text_output, text_output2] + text_outputs,
None,
preprocess=False,
api_name='flag' if allow_api else None, queue=False)
flag_btn_nochat.click(lambda *args: callback.flag(args), inputs_list + [text_output_nochat], None,
preprocess=False,
api_name='flag_nochat' if allow_api else None, queue=False)
def get_system_info():
if is_public:
time.sleep(10) # delay to avoid spam since queue=False
return gr.Textbox.update(value=system_info_print())
system_event = system_btn.click(get_system_info, outputs=system_text,
api_name='system_info' if allow_api else None, queue=False)
def get_system_info_dict(system_input1, **kwargs1):
if system_input1 != os.getenv("ADMIN_PASS", ""):
return json.dumps({})
exclude_list = ['admin_pass', 'examples']
sys_dict = {k: v for k, v in kwargs1.items() if
isinstance(v, (str, int, bool, float)) and k not in exclude_list}
try:
sys_dict.update(system_info())
except Exception as e:
# protection
print("Exception: %s" % str(e), flush=True)
return json.dumps(sys_dict)
system_kwargs = all_kwargs.copy()
system_kwargs.update(dict(command=str(' '.join(sys.argv))))
get_system_info_dict_func = functools.partial(get_system_info_dict, **all_kwargs)
system_dict_event = system_btn2.click(get_system_info_dict_func,
inputs=system_input,
outputs=system_text2,
api_name='system_info_dict' if allow_api else None,
queue=False, # queue to avoid spam
)
def get_hash():
return kwargs['git_hash']
system_event = system_btn3.click(get_hash,
outputs=system_text3,
api_name='system_hash' if allow_api else None,
queue=False,
)
def count_chat_tokens(model_state1, chat1, prompt_type1, prompt_dict1,
memory_restriction_level1=0,
keep_sources_in_context1=False,
):
if model_state1 and not isinstance(model_state1['tokenizer'], str):
tokenizer = model_state1['tokenizer']
elif model_state0 and not isinstance(model_state0['tokenizer'], str):
tokenizer = model_state0['tokenizer']
else:
tokenizer = None
if tokenizer is not None:
langchain_mode1 = 'LLM'
add_chat_history_to_context1 = True
# fake user message to mimic bot()
chat1 = copy.deepcopy(chat1)
chat1 = chat1 + [['user_message1', None]]
model_max_length1 = tokenizer.model_max_length
context1 = history_to_context(chat1, langchain_mode1,
add_chat_history_to_context1,
prompt_type1, prompt_dict1, chat1,
model_max_length1,
memory_restriction_level1, keep_sources_in_context1)
return str(tokenizer(context1, return_tensors="pt")['input_ids'].shape[1])
else:
return "N/A"
count_chat_tokens_func = functools.partial(count_chat_tokens,
memory_restriction_level1=memory_restriction_level,
keep_sources_in_context1=kwargs['keep_sources_in_context'])
count_tokens_event = count_chat_tokens_btn.click(fn=count_chat_tokens,
inputs=[model_state, text_output, prompt_type, prompt_dict],
outputs=chat_token_count,
api_name='count_tokens' if allow_api else None)
# don't pass text_output, don't want to clear output, just stop it
# cancel only stops outer generation, not inner generation or non-generation
stop_btn.click(lambda: None, None, None,
cancels=submits1 + submits2 + submits3 + submits4 +
[submit_event_nochat, submit_event_nochat2] +
[eventdb1, eventdb2, eventdb3] +
[eventdb7, eventdb8, eventdb9, eventdb12] +
db_events +
[clear_event] +
[submit_event_nochat_api, submit_event_nochat] +
[load_model_event, load_model_event2] +
[count_tokens_event]
,
queue=False, api_name='stop' if allow_api else None).then(clear_torch_cache, queue=False)
demo.load(None, None, None, _js=get_dark_js() if kwargs['dark'] else None)
demo.queue(concurrency_count=kwargs['concurrency_count'], api_open=kwargs['api_open'])
favicon_path = "h2o-logo.svg"
if not os.path.isfile(favicon_path):
print("favicon_path=%s not found" % favicon_path, flush=True)
favicon_path = None
scheduler = BackgroundScheduler()
scheduler.add_job(func=clear_torch_cache, trigger="interval", seconds=20)
if is_public and \
kwargs['base_model'] not in non_hf_types:
# FIXME: disable for gptj, langchain or gpt4all modify print itself
# FIXME: and any multi-threaded/async print will enter model output!
scheduler.add_job(func=ping, trigger="interval", seconds=60)
if is_public or os.getenv('PING_GPU'):
scheduler.add_job(func=ping_gpu, trigger="interval", seconds=60 * 10)
scheduler.start()
# import control
if kwargs['langchain_mode'] == 'Disabled' and \
os.environ.get("TEST_LANGCHAIN_IMPORT") and \
kwargs['base_model'] not in non_hf_types:
assert 'gpt_langchain' not in sys.modules, "Dev bug, import of langchain when should not have"
assert 'langchain' not in sys.modules, "Dev bug, import of langchain when should not have"
demo.launch(share=kwargs['share'], server_name="0.0.0.0", show_error=True,
favicon_path=favicon_path, prevent_thread_lock=True,
auth=kwargs['auth'])
if kwargs['verbose']:
print("Started GUI", flush=True)
if kwargs['block_gradio_exit']:
demo.block_thread()
def get_inputs_list(inputs_dict, model_lower, model_id=1):
"""
map gradio objects in locals() to inputs for evaluate().
:param inputs_dict:
:param model_lower:
:param model_id: Which model (1 or 2) of 2
:return:
"""
inputs_list_names = list(inspect.signature(evaluate).parameters)
inputs_list = []
inputs_dict_out = {}
for k in inputs_list_names:
if k == 'kwargs':
continue
if k in input_args_list + inputs_kwargs_list:
# these are added at use time for args or partial for kwargs, not taken as input
continue
if 'mbart-' not in model_lower and k in ['src_lang', 'tgt_lang']:
continue
if model_id == 2:
if k == 'prompt_type':
k = 'prompt_type2'
if k == 'prompt_used':
k = 'prompt_used2'
if k == 'max_new_tokens':
k = 'max_new_tokens2'
if k == 'min_new_tokens':
k = 'min_new_tokens2'
inputs_list.append(inputs_dict[k])
inputs_dict_out[k] = inputs_dict[k]
return inputs_list, inputs_dict_out
def get_sources(db1s, langchain_mode, dbs=None, docs_state0=None):
for k in db1s:
set_userid(db1s[k])
if langchain_mode in ['LLM']:
source_files_added = "NA"
source_list = []
elif langchain_mode in ['wiki_full']:
source_files_added = "Not showing wiki_full, takes about 20 seconds and makes 4MB file." \
" Ask jon.mckinney@h2o.ai for file if required."
source_list = []
elif langchain_mode in db1s and len(db1s[langchain_mode]) == 2 and db1s[langchain_mode][0] is not None:
db1 = db1s[langchain_mode]
from gpt_langchain import get_metadatas
metadatas = get_metadatas(db1[0])
source_list = sorted(set([x['source'] for x in metadatas]))
source_files_added = '\n'.join(source_list)
elif langchain_mode in dbs and dbs[langchain_mode] is not None:
from gpt_langchain import get_metadatas
db1 = dbs[langchain_mode]
metadatas = get_metadatas(db1)
source_list = sorted(set([x['source'] for x in metadatas]))
source_files_added = '\n'.join(source_list)
else:
source_list = []
source_files_added = "None"
sources_dir = "sources_dir"
makedirs(sources_dir)
sources_file = os.path.join(sources_dir, 'sources_%s_%s' % (langchain_mode, str(uuid.uuid4())))
with open(sources_file, "wt") as f:
f.write(source_files_added)
source_list = docs_state0 + source_list
return sources_file, source_list
def set_userid(db1):
# can only call this after function called so for specific userr, not in gr.State() that occurs during app init
assert db1 is not None and len(db1) == 2
if db1[1] is None:
# uuid in db is used as user ID
db1[1] = str(uuid.uuid4())
def update_user_db(file, db1s, selection_docs_state1, chunk, chunk_size, langchain_mode, dbs=None, **kwargs):
kwargs.update(selection_docs_state1)
if file is None:
raise RuntimeError("Don't use change, use input")
try:
return _update_user_db(file, db1s=db1s, chunk=chunk, chunk_size=chunk_size,
langchain_mode=langchain_mode, dbs=dbs,
**kwargs)
except BaseException as e:
print(traceback.format_exc(), flush=True)
# gradio has issues if except, so fail semi-gracefully, else would hang forever in processing textbox
ex_str = "Exception: %s" % str(e)
source_files_added = """\
<html>
<body>
<p>
Sources: <br>
</p>
<div style="overflow-y: auto;height:400px">
{0}
</div>
</body>
</html>
""".format(ex_str)
doc_exception_text = str(e)
return None, langchain_mode, source_files_added, doc_exception_text
finally:
clear_torch_cache()
def get_lock_file(db1, langchain_mode):
set_userid(db1)
assert len(db1) == 2 and db1[1] is not None and isinstance(db1[1], str)
user_id = db1[1]
base_path = 'locks'
makedirs(base_path)
lock_file = os.path.join(base_path, "db_%s_%s.lock" % (langchain_mode.replace(' ', '_'), user_id))
return lock_file
def _update_user_db(file,
db1s=None,
chunk=None, chunk_size=None,
dbs=None, db_type=None,
langchain_mode='UserData',
langchain_modes=None, # unused but required as part of selection_docs_state1
langchain_mode_paths=None,
visible_langchain_modes=None,
use_openai_embedding=None,
hf_embedding_model=None,
caption_loader=None,
enable_captions=None,
captions_model=None,
enable_ocr=None,
enable_pdf_ocr=None,
verbose=None,
n_jobs=-1,
is_url=None, is_txt=None,
):
assert db1s is not None
assert chunk is not None
assert chunk_size is not None
assert use_openai_embedding is not None
assert hf_embedding_model is not None
assert caption_loader is not None
assert enable_captions is not None
assert captions_model is not None
assert enable_ocr is not None
assert enable_pdf_ocr is not None
assert verbose is not None
if dbs is None:
dbs = {}
assert isinstance(dbs, dict), "Wrong type for dbs: %s" % str(type(dbs))
# assert db_type in ['faiss', 'chroma'], "db_type %s not supported" % db_type
from gpt_langchain import add_to_db, get_db, path_to_docs
# handle case of list of temp buffer
if isinstance(file, list) and len(file) > 0 and hasattr(file[0], 'name'):
file = [x.name for x in file]
# handle single file of temp buffer
if hasattr(file, 'name'):
file = file.name
if not isinstance(file, (list, tuple, typing.Generator)) and isinstance(file, str):
file = [file]
if langchain_mode == LangChainMode.DISABLED.value:
return None, langchain_mode, get_source_files(), ""
if langchain_mode in [LangChainMode.LLM.value]:
# then switch to MyData, so langchain_mode also becomes way to select where upload goes
# but default to mydata if nothing chosen, since safest
if LangChainMode.MY_DATA.value in visible_langchain_modes:
langchain_mode = LangChainMode.MY_DATA.value
if langchain_mode_paths is None:
langchain_mode_paths = {}
user_path = langchain_mode_paths.get(langchain_mode)
# UserData or custom, which has to be from user's disk
if user_path is not None:
# move temp files from gradio upload to stable location
for fili, fil in enumerate(file):
if isinstance(fil, str) and os.path.isfile(fil): # not url, text
new_fil = os.path.normpath(os.path.join(user_path, os.path.basename(fil)))
if os.path.normpath(os.path.abspath(fil)) != os.path.normpath(os.path.abspath(new_fil)):
if os.path.isfile(new_fil):
remove(new_fil)
try:
shutil.move(fil, new_fil)
except FileExistsError:
pass
file[fili] = new_fil
if verbose:
print("Adding %s" % file, flush=True)
sources = path_to_docs(file if not is_url and not is_txt else None,
verbose=verbose,
n_jobs=n_jobs,
chunk=chunk, chunk_size=chunk_size,
url=file if is_url else None,
text=file if is_txt else None,
enable_captions=enable_captions,
captions_model=captions_model,
enable_ocr=enable_ocr,
enable_pdf_ocr=enable_pdf_ocr,
caption_loader=caption_loader,
)
exceptions = [x for x in sources if x.metadata.get('exception')]
exceptions_strs = [x.metadata['exception'] for x in exceptions]
sources = [x for x in sources if 'exception' not in x.metadata]
# below must at least come after langchain_mode is modified in case was LLM -> MyData,
# so original langchain mode changed
for k in db1s:
set_userid(db1s[k])
db1 = get_db1(db1s, langchain_mode)
lock_file = get_lock_file(db1s[LangChainMode.MY_DATA.value], langchain_mode) # user-level lock, not db-level lock
with filelock.FileLock(lock_file):
if langchain_mode in db1s:
if db1[0] is not None:
# then add
db, num_new_sources, new_sources_metadata = add_to_db(db1[0], sources, db_type=db_type,
use_openai_embedding=use_openai_embedding,
hf_embedding_model=hf_embedding_model)
else:
# in testing expect:
# assert len(db1) == 2 and db1[1] is None, "Bad MyData db: %s" % db1
# for production hit, when user gets clicky:
assert len(db1) == 2, "Bad %s db: %s" % (langchain_mode, db1)
assert db1[1] is not None, "db hash was None, not allowed"
# then create
# if added has to original state and didn't change, then would be shared db for all users
persist_directory = os.path.join(scratch_base_dir, 'db_dir_%s_%s' % (langchain_mode, db1[1]))
db = get_db(sources, use_openai_embedding=use_openai_embedding,
db_type=db_type,
persist_directory=persist_directory,
langchain_mode=langchain_mode,
hf_embedding_model=hf_embedding_model)
if db is not None:
db1[0] = db
source_files_added = get_source_files(db=db1[0], exceptions=exceptions)
return None, langchain_mode, source_files_added, '\n'.join(exceptions_strs)
else:
from gpt_langchain import get_persist_directory
persist_directory = get_persist_directory(langchain_mode)
if langchain_mode in dbs and dbs[langchain_mode] is not None:
# then add
db, num_new_sources, new_sources_metadata = add_to_db(dbs[langchain_mode], sources, db_type=db_type,
use_openai_embedding=use_openai_embedding,
hf_embedding_model=hf_embedding_model)
else:
# then create. Or might just be that dbs is unfilled, then it will fill, then add
db = get_db(sources, use_openai_embedding=use_openai_embedding,
db_type=db_type,
persist_directory=persist_directory,
langchain_mode=langchain_mode,
hf_embedding_model=hf_embedding_model)
dbs[langchain_mode] = db
# NOTE we do not return db, because function call always same code path
# return dbs[langchain_mode]
# db in this code path is updated in place
source_files_added = get_source_files(db=dbs[langchain_mode], exceptions=exceptions)
return None, langchain_mode, source_files_added, '\n'.join(exceptions_strs)
def get_db(db1s, langchain_mode, dbs=None):
db1 = get_db1(db1s, langchain_mode)
lock_file = get_lock_file(db1s[LangChainMode.MY_DATA.value], langchain_mode)
with filelock.FileLock(lock_file):
if langchain_mode in ['wiki_full']:
# NOTE: avoid showing full wiki. Takes about 30 seconds over about 90k entries, but not useful for now
db = None
elif langchain_mode in db1s and len(db1) == 2 and db1[0] is not None:
db = db1[0]
elif dbs is not None and langchain_mode in dbs and dbs[langchain_mode] is not None:
db = dbs[langchain_mode]
else:
db = None
return db
def get_source_files_given_langchain_mode(db1s, langchain_mode='UserData', dbs=None):
db = get_db(db1s, langchain_mode, dbs=dbs)
if langchain_mode in ['LLM'] or db is None:
return "Sources: N/A"
return get_source_files(db=db, exceptions=None)
def get_source_files(db=None, exceptions=None, metadatas=None):
if exceptions is None:
exceptions = []
# only should be one source, not confused
# assert db is not None or metadatas is not None
# clicky user
if db is None and metadatas is None:
return "No Sources at all"
if metadatas is None:
source_label = "Sources:"
if db is not None:
from gpt_langchain import get_metadatas
metadatas = get_metadatas(db)
else:
metadatas = []
adding_new = False
else:
source_label = "New Sources:"
adding_new = True
# below automatically de-dups
from gpt_langchain import get_url
small_dict = {get_url(x['source'], from_str=True, short_name=True): get_short_name(x.get('head')) for x in
metadatas}
# if small_dict is empty dict, that's ok
df = pd.DataFrame(small_dict.items(), columns=['source', 'head'])
df.index = df.index + 1
df.index.name = 'index'
source_files_added = tabulate.tabulate(df, headers='keys', tablefmt='unsafehtml')
if exceptions:
exception_metadatas = [x.metadata for x in exceptions]
small_dict = {get_url(x['source'], from_str=True, short_name=True): get_short_name(x.get('exception')) for x in
exception_metadatas}
# if small_dict is empty dict, that's ok
df = pd.DataFrame(small_dict.items(), columns=['source', 'exception'])
df.index = df.index + 1
df.index.name = 'index'
exceptions_html = tabulate.tabulate(df, headers='keys', tablefmt='unsafehtml')
else:
exceptions_html = ''
if metadatas and exceptions:
source_files_added = """\
<html>
<body>
<p>
{0} <br>
</p>
<div style="overflow-y: auto;height:400px">
{1}
{2}
</div>
</body>
</html>
""".format(source_label, source_files_added, exceptions_html)
elif metadatas:
source_files_added = """\
<html>
<body>
<p>
{0} <br>
</p>
<div style="overflow-y: auto;height:400px">
{1}
</div>
</body>
</html>
""".format(source_label, source_files_added)
elif exceptions_html:
source_files_added = """\
<html>
<body>
<p>
Exceptions: <br>
</p>
<div style="overflow-y: auto;height:400px">
{0}
</div>
</body>
</html>
""".format(exceptions_html)
else:
if adding_new:
source_files_added = "No New Sources"
else:
source_files_added = "No Sources"
return source_files_added
def update_and_get_source_files_given_langchain_mode(db1s, langchain_mode, chunk, chunk_size,
dbs=None, first_para=None,
text_limit=None,
langchain_mode_paths=None, db_type=None, load_db_if_exists=None,
n_jobs=None, verbose=None):
has_path = {k: v for k, v in langchain_mode_paths.items() if v}
if langchain_mode in [LangChainMode.LLM.value, LangChainMode.MY_DATA.value]:
# then assume user really meant UserData, to avoid extra clicks in UI,
# since others can't be on disk, except custom user modes, which they should then select to query it
if LangChainMode.USER_DATA.value in has_path:
langchain_mode = LangChainMode.USER_DATA.value
db = get_db(db1s, langchain_mode, dbs=dbs)
from gpt_langchain import make_db
db, num_new_sources, new_sources_metadata = make_db(use_openai_embedding=False,
hf_embedding_model="sentence-transformers/all-MiniLM-L6-v2",
first_para=first_para, text_limit=text_limit,
chunk=chunk,
chunk_size=chunk_size,
langchain_mode=langchain_mode,
langchain_mode_paths=langchain_mode_paths,
db_type=db_type,
load_db_if_exists=load_db_if_exists,
db=db,
n_jobs=n_jobs,
verbose=verbose)
# during refreshing, might have "created" new db since not in dbs[] yet, so insert back just in case
# so even if persisted, not kept up-to-date with dbs memory
if langchain_mode in db1s:
db1s[langchain_mode][0] = db
else:
dbs[langchain_mode] = db
# return only new sources with text saying such
return get_source_files(db=None, exceptions=None, metadatas=new_sources_metadata)
def get_db1(db1s, langchain_mode1):
if langchain_mode1 in db1s:
db1 = db1s[langchain_mode1]
else:
# indicates to code that not scratch database
db1 = [None, None]
return db1
|