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Browse files- app (8).py +1652 -0
- requirements (4).txt +6 -0
app (8).py
ADDED
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@@ -0,0 +1,1652 @@
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|
| 1 |
+
from __future__ import annotations
|
| 2 |
+
|
| 3 |
+
import json
|
| 4 |
+
import os
|
| 5 |
+
import re
|
| 6 |
+
import shutil
|
| 7 |
+
import threading
|
| 8 |
+
from dataclasses import dataclass
|
| 9 |
+
from pathlib import Path
|
| 10 |
+
from typing import Any, Dict, List, Optional, Tuple
|
| 11 |
+
|
| 12 |
+
import gradio as gr
|
| 13 |
+
import pandas as pd
|
| 14 |
+
from sklearn.feature_extraction.text import TfidfVectorizer
|
| 15 |
+
from sklearn.metrics.pairwise import cosine_similarity
|
| 16 |
+
from sklearn.neighbors import NearestNeighbors
|
| 17 |
+
import numpy as np
|
| 18 |
+
|
| 19 |
+
|
| 20 |
+
# ============================================================
|
| 21 |
+
# Configuration
|
| 22 |
+
# ============================================================
|
| 23 |
+
APP_TITLE = "QuoteForge"
|
| 24 |
+
APP_SUBTITLE = "Industrial Quote Intelligence Platform"
|
| 25 |
+
DEFAULT_MODEL = os.getenv("CLAUDE_MODEL", "claude-sonnet-4-6")
|
| 26 |
+
MAIN_SHEET = "Sheet1"
|
| 27 |
+
NOTES_SHEET = "SME_Notes"
|
| 28 |
+
HEADERS = ["Request", "Information Extracted", "Design"]
|
| 29 |
+
DATA_LOCK = threading.Lock()
|
| 30 |
+
ADMIN_PASSWORD = os.getenv("ADMIN_PASSWORD", "admin1234")
|
| 31 |
+
|
| 32 |
+
FONTS = "https://fonts.googleapis.com/css2?family=Bebas+Neue&family=DM+Mono:ital,wght@0,300;0,400;0,500;1,300&family=DM+Sans:wght@300;400;500;600&display=swap"
|
| 33 |
+
|
| 34 |
+
CUSTOM_CSS = f"""
|
| 35 |
+
@import url('{FONTS}');
|
| 36 |
+
|
| 37 |
+
:root {{
|
| 38 |
+
--forge-black: #0a0a0b;
|
| 39 |
+
--forge-dark: #111114;
|
| 40 |
+
--forge-panel: #18181d;
|
| 41 |
+
--forge-border: #2a2a35;
|
| 42 |
+
--forge-border-bright: #3d3d50;
|
| 43 |
+
--forge-amber: #f59e0b;
|
| 44 |
+
--forge-amber-dim: #92610a;
|
| 45 |
+
--forge-amber-glow: rgba(245,158,11,0.15);
|
| 46 |
+
--forge-red: #ef4444;
|
| 47 |
+
--forge-green: #22c55e;
|
| 48 |
+
--forge-blue: #3b82f6;
|
| 49 |
+
--forge-text: #e8e8f0;
|
| 50 |
+
--forge-muted: #6b6b80;
|
| 51 |
+
--forge-mono: 'DM Mono', monospace;
|
| 52 |
+
--forge-display: 'Bebas Neue', sans-serif;
|
| 53 |
+
--forge-body: 'DM Sans', sans-serif;
|
| 54 |
+
}}
|
| 55 |
+
|
| 56 |
+
/* ── Global reset ── */
|
| 57 |
+
*, *::before, *::after {{ box-sizing: border-box; }}
|
| 58 |
+
|
| 59 |
+
.gradio-container {{
|
| 60 |
+
max-width: 100% !important;
|
| 61 |
+
padding: 0 !important;
|
| 62 |
+
margin: 0 !important;
|
| 63 |
+
background: var(--forge-black) !important;
|
| 64 |
+
font-family: var(--forge-body) !important;
|
| 65 |
+
min-height: 100vh;
|
| 66 |
+
}}
|
| 67 |
+
|
| 68 |
+
body, .dark {{
|
| 69 |
+
background: var(--forge-black) !important;
|
| 70 |
+
}}
|
| 71 |
+
|
| 72 |
+
/* ── Hide default gradio chrome ── */
|
| 73 |
+
footer {{ display: none !important; }}
|
| 74 |
+
.svelte-1ipelgc {{ display: none !important; }}
|
| 75 |
+
|
| 76 |
+
/* ── Header ── */
|
| 77 |
+
.forge-header {{
|
| 78 |
+
background: var(--forge-dark);
|
| 79 |
+
border-bottom: 1px solid var(--forge-border);
|
| 80 |
+
padding: 0 2rem;
|
| 81 |
+
display: flex;
|
| 82 |
+
align-items: center;
|
| 83 |
+
justify-content: space-between;
|
| 84 |
+
height: 64px;
|
| 85 |
+
position: sticky;
|
| 86 |
+
top: 0;
|
| 87 |
+
z-index: 100;
|
| 88 |
+
}}
|
| 89 |
+
|
| 90 |
+
.forge-logo {{
|
| 91 |
+
display: flex;
|
| 92 |
+
align-items: baseline;
|
| 93 |
+
gap: 0.75rem;
|
| 94 |
+
}}
|
| 95 |
+
|
| 96 |
+
.forge-logo-primary {{
|
| 97 |
+
font-family: var(--forge-display);
|
| 98 |
+
font-size: 2rem;
|
| 99 |
+
letter-spacing: 0.08em;
|
| 100 |
+
color: var(--forge-amber);
|
| 101 |
+
line-height: 1;
|
| 102 |
+
}}
|
| 103 |
+
|
| 104 |
+
.forge-logo-sub {{
|
| 105 |
+
font-family: var(--forge-mono);
|
| 106 |
+
font-size: 0.7rem;
|
| 107 |
+
color: var(--forge-muted);
|
| 108 |
+
letter-spacing: 0.2em;
|
| 109 |
+
text-transform: uppercase;
|
| 110 |
+
}}
|
| 111 |
+
|
| 112 |
+
.forge-badge {{
|
| 113 |
+
font-family: var(--forge-mono);
|
| 114 |
+
font-size: 0.65rem;
|
| 115 |
+
padding: 0.25rem 0.6rem;
|
| 116 |
+
border: 1px solid var(--forge-amber-dim);
|
| 117 |
+
color: var(--forge-amber);
|
| 118 |
+
letter-spacing: 0.15em;
|
| 119 |
+
text-transform: uppercase;
|
| 120 |
+
background: var(--forge-amber-glow);
|
| 121 |
+
}}
|
| 122 |
+
|
| 123 |
+
/* ── Tab navigation override ── */
|
| 124 |
+
.tab-nav {{
|
| 125 |
+
background: var(--forge-dark) !important;
|
| 126 |
+
border-bottom: 1px solid var(--forge-border) !important;
|
| 127 |
+
padding: 0 2rem !important;
|
| 128 |
+
gap: 0 !important;
|
| 129 |
+
}}
|
| 130 |
+
|
| 131 |
+
.tab-nav button {{
|
| 132 |
+
font-family: var(--forge-mono) !important;
|
| 133 |
+
font-size: 0.72rem !important;
|
| 134 |
+
letter-spacing: 0.12em !important;
|
| 135 |
+
text-transform: uppercase !important;
|
| 136 |
+
color: var(--forge-muted) !important;
|
| 137 |
+
background: transparent !important;
|
| 138 |
+
border: none !important;
|
| 139 |
+
border-bottom: 2px solid transparent !important;
|
| 140 |
+
padding: 1rem 1.5rem !important;
|
| 141 |
+
margin: 0 !important;
|
| 142 |
+
transition: all 0.2s !important;
|
| 143 |
+
border-radius: 0 !important;
|
| 144 |
+
}}
|
| 145 |
+
|
| 146 |
+
.tab-nav button:hover {{
|
| 147 |
+
color: var(--forge-text) !important;
|
| 148 |
+
background: transparent !important;
|
| 149 |
+
}}
|
| 150 |
+
|
| 151 |
+
.tab-nav button.selected {{
|
| 152 |
+
color: var(--forge-amber) !important;
|
| 153 |
+
border-bottom-color: var(--forge-amber) !important;
|
| 154 |
+
background: transparent !important;
|
| 155 |
+
}}
|
| 156 |
+
|
| 157 |
+
/* ── Page sections ── */
|
| 158 |
+
.forge-page {{
|
| 159 |
+
padding: 2.5rem 2rem;
|
| 160 |
+
max-width: 1400px;
|
| 161 |
+
margin: 0 auto;
|
| 162 |
+
}}
|
| 163 |
+
|
| 164 |
+
/* ── Section headers ── */
|
| 165 |
+
.forge-section-label {{
|
| 166 |
+
font-family: var(--forge-mono);
|
| 167 |
+
font-size: 0.65rem;
|
| 168 |
+
letter-spacing: 0.2em;
|
| 169 |
+
text-transform: uppercase;
|
| 170 |
+
color: var(--forge-amber);
|
| 171 |
+
margin-bottom: 0.5rem;
|
| 172 |
+
display: flex;
|
| 173 |
+
align-items: center;
|
| 174 |
+
gap: 0.5rem;
|
| 175 |
+
}}
|
| 176 |
+
|
| 177 |
+
.forge-section-label::after {{
|
| 178 |
+
content: '';
|
| 179 |
+
flex: 1;
|
| 180 |
+
height: 1px;
|
| 181 |
+
background: var(--forge-border);
|
| 182 |
+
}}
|
| 183 |
+
|
| 184 |
+
.forge-section-title {{
|
| 185 |
+
font-family: var(--forge-display);
|
| 186 |
+
font-size: 3rem;
|
| 187 |
+
color: var(--forge-text);
|
| 188 |
+
letter-spacing: 0.05em;
|
| 189 |
+
line-height: 1;
|
| 190 |
+
margin-bottom: 0.75rem;
|
| 191 |
+
}}
|
| 192 |
+
|
| 193 |
+
.forge-section-desc {{
|
| 194 |
+
font-family: var(--forge-body);
|
| 195 |
+
font-size: 0.95rem;
|
| 196 |
+
color: var(--forge-muted);
|
| 197 |
+
line-height: 1.7;
|
| 198 |
+
max-width: 560px;
|
| 199 |
+
margin-bottom: 2rem;
|
| 200 |
+
}}
|
| 201 |
+
|
| 202 |
+
/* ── Cards / panels ── */
|
| 203 |
+
.forge-card {{
|
| 204 |
+
background: var(--forge-panel);
|
| 205 |
+
border: 1px solid var(--forge-border);
|
| 206 |
+
padding: 1.5rem;
|
| 207 |
+
position: relative;
|
| 208 |
+
}}
|
| 209 |
+
|
| 210 |
+
.forge-card::before {{
|
| 211 |
+
content: '';
|
| 212 |
+
position: absolute;
|
| 213 |
+
top: 0; left: 0;
|
| 214 |
+
width: 3px; height: 100%;
|
| 215 |
+
background: var(--forge-amber);
|
| 216 |
+
}}
|
| 217 |
+
|
| 218 |
+
/* ── Inputs ── */
|
| 219 |
+
label {{
|
| 220 |
+
font-family: var(--forge-mono) !important;
|
| 221 |
+
font-size: 0.68rem !important;
|
| 222 |
+
letter-spacing: 0.14em !important;
|
| 223 |
+
text-transform: uppercase !important;
|
| 224 |
+
color: var(--forge-muted) !important;
|
| 225 |
+
margin-bottom: 0.4rem !important;
|
| 226 |
+
}}
|
| 227 |
+
|
| 228 |
+
textarea, input[type=text], input[type=password], input[type=number] {{
|
| 229 |
+
font-family: var(--forge-mono) !important;
|
| 230 |
+
font-size: 0.85rem !important;
|
| 231 |
+
background: var(--forge-black) !important;
|
| 232 |
+
border: 1px solid var(--forge-border) !important;
|
| 233 |
+
color: var(--forge-text) !important;
|
| 234 |
+
border-radius: 0 !important;
|
| 235 |
+
transition: border-color 0.2s !important;
|
| 236 |
+
}}
|
| 237 |
+
|
| 238 |
+
textarea:focus, input:focus {{
|
| 239 |
+
border-color: var(--forge-amber) !important;
|
| 240 |
+
outline: none !important;
|
| 241 |
+
box-shadow: 0 0 0 1px var(--forge-amber-dim) !important;
|
| 242 |
+
}}
|
| 243 |
+
|
| 244 |
+
/* ── Buttons ── */
|
| 245 |
+
button.primary {{
|
| 246 |
+
font-family: var(--forge-mono) !important;
|
| 247 |
+
font-size: 0.75rem !important;
|
| 248 |
+
letter-spacing: 0.15em !important;
|
| 249 |
+
text-transform: uppercase !important;
|
| 250 |
+
background: var(--forge-amber) !important;
|
| 251 |
+
color: var(--forge-black) !important;
|
| 252 |
+
border: none !important;
|
| 253 |
+
border-radius: 0 !important;
|
| 254 |
+
padding: 0.75rem 1.5rem !important;
|
| 255 |
+
font-weight: 600 !important;
|
| 256 |
+
transition: all 0.2s !important;
|
| 257 |
+
cursor: pointer !important;
|
| 258 |
+
}}
|
| 259 |
+
|
| 260 |
+
button.primary:hover {{
|
| 261 |
+
background: #fbbf24 !important;
|
| 262 |
+
transform: translateY(-1px) !important;
|
| 263 |
+
box-shadow: 0 4px 20px rgba(245,158,11,0.3) !important;
|
| 264 |
+
}}
|
| 265 |
+
|
| 266 |
+
button.secondary {{
|
| 267 |
+
font-family: var(--forge-mono) !important;
|
| 268 |
+
font-size: 0.72rem !important;
|
| 269 |
+
letter-spacing: 0.12em !important;
|
| 270 |
+
text-transform: uppercase !important;
|
| 271 |
+
background: transparent !important;
|
| 272 |
+
color: var(--forge-text) !important;
|
| 273 |
+
border: 1px solid var(--forge-border-bright) !important;
|
| 274 |
+
border-radius: 0 !important;
|
| 275 |
+
padding: 0.65rem 1.25rem !important;
|
| 276 |
+
transition: all 0.2s !important;
|
| 277 |
+
}}
|
| 278 |
+
|
| 279 |
+
button.secondary:hover {{
|
| 280 |
+
border-color: var(--forge-amber) !important;
|
| 281 |
+
color: var(--forge-amber) !important;
|
| 282 |
+
}}
|
| 283 |
+
|
| 284 |
+
/* ── Status / alert banners ── */
|
| 285 |
+
.forge-alert {{
|
| 286 |
+
border: 1px solid;
|
| 287 |
+
padding: 1rem 1.25rem;
|
| 288 |
+
font-family: var(--forge-mono);
|
| 289 |
+
font-size: 0.78rem;
|
| 290 |
+
letter-spacing: 0.06em;
|
| 291 |
+
display: flex;
|
| 292 |
+
align-items: flex-start;
|
| 293 |
+
gap: 0.75rem;
|
| 294 |
+
margin-bottom: 1.5rem;
|
| 295 |
+
}}
|
| 296 |
+
|
| 297 |
+
.forge-alert.warn {{
|
| 298 |
+
border-color: var(--forge-amber-dim);
|
| 299 |
+
background: var(--forge-amber-glow);
|
| 300 |
+
color: var(--forge-amber);
|
| 301 |
+
}}
|
| 302 |
+
|
| 303 |
+
.forge-alert.error {{
|
| 304 |
+
border-color: #7f1d1d;
|
| 305 |
+
background: rgba(239,68,68,0.08);
|
| 306 |
+
color: var(--forge-red);
|
| 307 |
+
}}
|
| 308 |
+
|
| 309 |
+
.forge-alert.success {{
|
| 310 |
+
border-color: #14532d;
|
| 311 |
+
background: rgba(34,197,94,0.08);
|
| 312 |
+
color: var(--forge-green);
|
| 313 |
+
}}
|
| 314 |
+
|
| 315 |
+
.forge-alert.info {{
|
| 316 |
+
border-color: var(--forge-border-bright);
|
| 317 |
+
background: rgba(59,130,246,0.08);
|
| 318 |
+
color: #93c5fd;
|
| 319 |
+
}}
|
| 320 |
+
|
| 321 |
+
/* ── API key prompt ── */
|
| 322 |
+
#api-key-banner {{
|
| 323 |
+
background: linear-gradient(135deg, rgba(245,158,11,0.12), rgba(245,158,11,0.04));
|
| 324 |
+
border: 1px solid var(--forge-amber-dim);
|
| 325 |
+
padding: 1.5rem 2rem;
|
| 326 |
+
margin-bottom: 2rem;
|
| 327 |
+
display: flex;
|
| 328 |
+
align-items: center;
|
| 329 |
+
gap: 1.5rem;
|
| 330 |
+
flex-wrap: wrap;
|
| 331 |
+
}}
|
| 332 |
+
|
| 333 |
+
/* ── Data tables ── */
|
| 334 |
+
.gradio-dataframe {{
|
| 335 |
+
background: var(--forge-panel) !important;
|
| 336 |
+
border: 1px solid var(--forge-border) !important;
|
| 337 |
+
border-radius: 0 !important;
|
| 338 |
+
}}
|
| 339 |
+
|
| 340 |
+
.gradio-dataframe table th {{
|
| 341 |
+
font-family: var(--forge-mono) !important;
|
| 342 |
+
font-size: 0.65rem !important;
|
| 343 |
+
letter-spacing: 0.15em !important;
|
| 344 |
+
text-transform: uppercase !important;
|
| 345 |
+
color: var(--forge-amber) !important;
|
| 346 |
+
background: var(--forge-dark) !important;
|
| 347 |
+
border-bottom: 1px solid var(--forge-border) !important;
|
| 348 |
+
padding: 0.75rem 1rem !important;
|
| 349 |
+
}}
|
| 350 |
+
|
| 351 |
+
.gradio-dataframe table td {{
|
| 352 |
+
font-family: var(--forge-mono) !important;
|
| 353 |
+
font-size: 0.8rem !important;
|
| 354 |
+
color: var(--forge-text) !important;
|
| 355 |
+
background: transparent !important;
|
| 356 |
+
border-bottom: 1px solid var(--forge-border) !important;
|
| 357 |
+
padding: 0.65rem 1rem !important;
|
| 358 |
+
}}
|
| 359 |
+
|
| 360 |
+
.gradio-dataframe table tr:hover td {{
|
| 361 |
+
background: rgba(245,158,11,0.04) !important;
|
| 362 |
+
}}
|
| 363 |
+
|
| 364 |
+
/* ── Sliders ── */
|
| 365 |
+
input[type=range] {{
|
| 366 |
+
accent-color: var(--forge-amber) !important;
|
| 367 |
+
}}
|
| 368 |
+
|
| 369 |
+
/* ── Dropdown ── */
|
| 370 |
+
.wrap-inner {{
|
| 371 |
+
background: var(--forge-black) !important;
|
| 372 |
+
border: 1px solid var(--forge-border) !important;
|
| 373 |
+
border-radius: 0 !important;
|
| 374 |
+
font-family: var(--forge-mono) !important;
|
| 375 |
+
font-size: 0.82rem !important;
|
| 376 |
+
color: var(--forge-text) !important;
|
| 377 |
+
}}
|
| 378 |
+
|
| 379 |
+
/* ── File upload ── */
|
| 380 |
+
.upload-btn {{
|
| 381 |
+
border: 1px dashed var(--forge-border-bright) !important;
|
| 382 |
+
background: var(--forge-black) !important;
|
| 383 |
+
border-radius: 0 !important;
|
| 384 |
+
color: var(--forge-muted) !important;
|
| 385 |
+
font-family: var(--forge-mono) !important;
|
| 386 |
+
font-size: 0.78rem !important;
|
| 387 |
+
}}
|
| 388 |
+
|
| 389 |
+
/* ── Stat grid (admin) ── */
|
| 390 |
+
.forge-stat-grid {{
|
| 391 |
+
display: grid;
|
| 392 |
+
grid-template-columns: repeat(auto-fit, minmax(160px, 1fr));
|
| 393 |
+
gap: 1px;
|
| 394 |
+
background: var(--forge-border);
|
| 395 |
+
border: 1px solid var(--forge-border);
|
| 396 |
+
margin-bottom: 2rem;
|
| 397 |
+
}}
|
| 398 |
+
|
| 399 |
+
.forge-stat {{
|
| 400 |
+
background: var(--forge-panel);
|
| 401 |
+
padding: 1.25rem 1.5rem;
|
| 402 |
+
display: flex;
|
| 403 |
+
flex-direction: column;
|
| 404 |
+
gap: 0.25rem;
|
| 405 |
+
}}
|
| 406 |
+
|
| 407 |
+
.forge-stat-value {{
|
| 408 |
+
font-family: var(--forge-display);
|
| 409 |
+
font-size: 2.2rem;
|
| 410 |
+
color: var(--forge-amber);
|
| 411 |
+
letter-spacing: 0.04em;
|
| 412 |
+
line-height: 1;
|
| 413 |
+
}}
|
| 414 |
+
|
| 415 |
+
.forge-stat-label {{
|
| 416 |
+
font-family: var(--forge-mono);
|
| 417 |
+
font-size: 0.62rem;
|
| 418 |
+
letter-spacing: 0.18em;
|
| 419 |
+
text-transform: uppercase;
|
| 420 |
+
color: var(--forge-muted);
|
| 421 |
+
}}
|
| 422 |
+
|
| 423 |
+
/* ── SML indicator ── */
|
| 424 |
+
.sml-badge {{
|
| 425 |
+
display: inline-flex;
|
| 426 |
+
align-items: center;
|
| 427 |
+
gap: 0.4rem;
|
| 428 |
+
font-family: var(--forge-mono);
|
| 429 |
+
font-size: 0.65rem;
|
| 430 |
+
letter-spacing: 0.12em;
|
| 431 |
+
text-transform: uppercase;
|
| 432 |
+
padding: 0.3rem 0.7rem;
|
| 433 |
+
border: 1px solid;
|
| 434 |
+
}}
|
| 435 |
+
|
| 436 |
+
.sml-badge.llm {{
|
| 437 |
+
border-color: #14532d;
|
| 438 |
+
color: var(--forge-green);
|
| 439 |
+
background: rgba(34,197,94,0.08);
|
| 440 |
+
}}
|
| 441 |
+
|
| 442 |
+
.sml-badge.sml {{
|
| 443 |
+
border-color: var(--forge-amber-dim);
|
| 444 |
+
color: var(--forge-amber);
|
| 445 |
+
background: var(--forge-amber-glow);
|
| 446 |
+
}}
|
| 447 |
+
|
| 448 |
+
/* ── Hero section ── */
|
| 449 |
+
.forge-hero {{
|
| 450 |
+
padding: 4rem 2rem 3rem;
|
| 451 |
+
max-width: 1400px;
|
| 452 |
+
margin: 0 auto;
|
| 453 |
+
display: grid;
|
| 454 |
+
grid-template-columns: 1fr 1fr;
|
| 455 |
+
gap: 4rem;
|
| 456 |
+
align-items: start;
|
| 457 |
+
}}
|
| 458 |
+
|
| 459 |
+
.forge-hero-visual {{
|
| 460 |
+
display: flex;
|
| 461 |
+
flex-direction: column;
|
| 462 |
+
gap: 1.5rem;
|
| 463 |
+
padding-top: 1rem;
|
| 464 |
+
}}
|
| 465 |
+
|
| 466 |
+
.forge-metric-row {{
|
| 467 |
+
display: flex;
|
| 468 |
+
gap: 1px;
|
| 469 |
+
background: var(--forge-border);
|
| 470 |
+
}}
|
| 471 |
+
|
| 472 |
+
.forge-metric {{
|
| 473 |
+
flex: 1;
|
| 474 |
+
background: var(--forge-panel);
|
| 475 |
+
padding: 1rem 1.25rem;
|
| 476 |
+
display: flex;
|
| 477 |
+
flex-direction: column;
|
| 478 |
+
gap: 0.2rem;
|
| 479 |
+
}}
|
| 480 |
+
|
| 481 |
+
.forge-metric-val {{
|
| 482 |
+
font-family: var(--forge-display);
|
| 483 |
+
font-size: 1.8rem;
|
| 484 |
+
color: var(--forge-amber);
|
| 485 |
+
}}
|
| 486 |
+
|
| 487 |
+
.forge-metric-key {{
|
| 488 |
+
font-family: var(--forge-mono);
|
| 489 |
+
font-size: 0.6rem;
|
| 490 |
+
color: var(--forge-muted);
|
| 491 |
+
letter-spacing: 0.15em;
|
| 492 |
+
text-transform: uppercase;
|
| 493 |
+
}}
|
| 494 |
+
|
| 495 |
+
.forge-divider {{
|
| 496 |
+
height: 1px;
|
| 497 |
+
background: var(--forge-border);
|
| 498 |
+
margin: 2rem 0;
|
| 499 |
+
}}
|
| 500 |
+
|
| 501 |
+
/* ── Admin terminal ── */
|
| 502 |
+
.forge-terminal-header {{
|
| 503 |
+
background: var(--forge-dark);
|
| 504 |
+
border: 1px solid var(--forge-border);
|
| 505 |
+
border-bottom: none;
|
| 506 |
+
padding: 0.75rem 1rem;
|
| 507 |
+
display: flex;
|
| 508 |
+
align-items: center;
|
| 509 |
+
gap: 0.5rem;
|
| 510 |
+
}}
|
| 511 |
+
|
| 512 |
+
.terminal-dot {{
|
| 513 |
+
width: 10px; height: 10px;
|
| 514 |
+
border-radius: 50%;
|
| 515 |
+
}}
|
| 516 |
+
|
| 517 |
+
.forge-terminal-body {{
|
| 518 |
+
background: var(--forge-black);
|
| 519 |
+
border: 1px solid var(--forge-border);
|
| 520 |
+
padding: 1.25rem;
|
| 521 |
+
font-family: var(--forge-mono);
|
| 522 |
+
font-size: 0.8rem;
|
| 523 |
+
color: var(--forge-text);
|
| 524 |
+
min-height: 60px;
|
| 525 |
+
line-height: 1.8;
|
| 526 |
+
}}
|
| 527 |
+
|
| 528 |
+
/* ── Responsive ── */
|
| 529 |
+
@media (max-width: 900px) {{
|
| 530 |
+
.forge-hero {{
|
| 531 |
+
grid-template-columns: 1fr;
|
| 532 |
+
}}
|
| 533 |
+
}}
|
| 534 |
+
"""
|
| 535 |
+
|
| 536 |
+
|
| 537 |
+
# ============================================================
|
| 538 |
+
# Paths
|
| 539 |
+
# ============================================================
|
| 540 |
+
REPO_ROOT = Path(__file__).resolve().parent
|
| 541 |
+
REPO_DATA_DIR = REPO_ROOT / "data"
|
| 542 |
+
REPO_DATA_DIR.mkdir(parents=True, exist_ok=True)
|
| 543 |
+
SEED_WORKBOOK = REPO_DATA_DIR / "quote_request_training.xlsx"
|
| 544 |
+
|
| 545 |
+
if Path("/data").exists():
|
| 546 |
+
APP_DATA_DIR = Path("/data") / "quote_request_handler"
|
| 547 |
+
else:
|
| 548 |
+
APP_DATA_DIR = REPO_DATA_DIR
|
| 549 |
+
|
| 550 |
+
APP_DATA_DIR.mkdir(parents=True, exist_ok=True)
|
| 551 |
+
EXPORT_DIR = APP_DATA_DIR / "exports"
|
| 552 |
+
EXPORT_DIR.mkdir(parents=True, exist_ok=True)
|
| 553 |
+
DATA_PATH = APP_DATA_DIR / "quote_request_training.xlsx"
|
| 554 |
+
|
| 555 |
+
DEFAULT_NOTES = [
|
| 556 |
+
"fan curves and AI selects fans",
|
| 557 |
+
"quote should call out unknowns clearly when application details are missing",
|
| 558 |
+
]
|
| 559 |
+
|
| 560 |
+
|
| 561 |
+
# ============================================================
|
| 562 |
+
# Utilities
|
| 563 |
+
# ============================================================
|
| 564 |
+
def clean_text(value: Any) -> str:
|
| 565 |
+
if value is None:
|
| 566 |
+
return ""
|
| 567 |
+
if isinstance(value, float) and pd.isna(value):
|
| 568 |
+
return ""
|
| 569 |
+
return str(value).strip()
|
| 570 |
+
|
| 571 |
+
|
| 572 |
+
def summarize_text(text: str, limit: int = 90) -> str:
|
| 573 |
+
text = clean_text(text).replace("\n", " ")
|
| 574 |
+
return text if len(text) <= limit else text[: limit - 3] + "..."
|
| 575 |
+
|
| 576 |
+
|
| 577 |
+
def safe_bool_text(flag: bool) -> str:
|
| 578 |
+
return "Yes" if flag else "No"
|
| 579 |
+
|
| 580 |
+
|
| 581 |
+
def strip_code_fences(text: str) -> str:
|
| 582 |
+
text = clean_text(text)
|
| 583 |
+
if text.startswith("```"):
|
| 584 |
+
text = re.sub(r"^```(?:json)?\s*", "", text, flags=re.IGNORECASE)
|
| 585 |
+
text = re.sub(r"\s*```$", "", text)
|
| 586 |
+
return text.strip()
|
| 587 |
+
|
| 588 |
+
|
| 589 |
+
def extract_first_balanced_json(text: str) -> str:
|
| 590 |
+
text = strip_code_fences(text)
|
| 591 |
+
start = text.find("{")
|
| 592 |
+
if start == -1:
|
| 593 |
+
raise ValueError(f"No JSON object found:\n{text}")
|
| 594 |
+
depth, in_string, escape = 0, False, False
|
| 595 |
+
for idx in range(start, len(text)):
|
| 596 |
+
char = text[idx]
|
| 597 |
+
if in_string:
|
| 598 |
+
if escape:
|
| 599 |
+
escape = False
|
| 600 |
+
elif char == "\\":
|
| 601 |
+
escape = True
|
| 602 |
+
elif char == '"':
|
| 603 |
+
in_string = False
|
| 604 |
+
continue
|
| 605 |
+
if char == '"':
|
| 606 |
+
in_string = True
|
| 607 |
+
elif char == "{":
|
| 608 |
+
depth += 1
|
| 609 |
+
elif char == "}":
|
| 610 |
+
depth -= 1
|
| 611 |
+
if depth == 0:
|
| 612 |
+
return text[start: idx + 1]
|
| 613 |
+
raise ValueError(f"JSON truncated:\n{text}")
|
| 614 |
+
|
| 615 |
+
|
| 616 |
+
def normalize_list(value: Any) -> List[str]:
|
| 617 |
+
if isinstance(value, list):
|
| 618 |
+
return [clean_text(v) for v in value if clean_text(v)]
|
| 619 |
+
if isinstance(value, str):
|
| 620 |
+
lines = [re.sub(r"^[-*\d.)\s]+", "", line).strip() for line in value.splitlines()]
|
| 621 |
+
return [line for line in lines if line]
|
| 622 |
+
return []
|
| 623 |
+
|
| 624 |
+
|
| 625 |
+
def ensure_seed_exists(path: Path) -> None:
|
| 626 |
+
if path.exists():
|
| 627 |
+
return
|
| 628 |
+
seed_df = pd.DataFrame([
|
| 629 |
+
{
|
| 630 |
+
"Request": "15000 CFM pharmaceutical powder, corrosive dust, need fan and collector recommendation",
|
| 631 |
+
"Information Extracted": "Pharmaceutical powder; corrosive dust; 15000 CFM; high-efficiency filtration, corrosion-resistant construction, combustibility review needed.",
|
| 632 |
+
"Design": "Recommend cartridge/pulse-jet collector with PTFE media, stainless construction, fan review, NFPA combustibility confirmation before final quote.",
|
| 633 |
+
},
|
| 634 |
+
{
|
| 635 |
+
"Request": "Need a dust collection upgrade for metal grinding line, 8000 CFM, sparks possible",
|
| 636 |
+
"Information Extracted": "Metal grinding dust; 8000 CFM; spark risk; abrasion-resistant design, spark mitigation, combustible metal hazard review.",
|
| 637 |
+
"Design": "Collector with spark control, abrasion-resistant internals, combustible metals safety review before quoting.",
|
| 638 |
+
},
|
| 639 |
+
])
|
| 640 |
+
notes_df = pd.DataFrame([[note] for note in DEFAULT_NOTES])
|
| 641 |
+
with pd.ExcelWriter(path, engine="openpyxl") as writer:
|
| 642 |
+
seed_df.to_excel(writer, sheet_name=MAIN_SHEET, index=False)
|
| 643 |
+
notes_df.to_excel(writer, sheet_name=NOTES_SHEET, index=False, header=False)
|
| 644 |
+
|
| 645 |
+
|
| 646 |
+
# ============================================================
|
| 647 |
+
# Workbook store
|
| 648 |
+
# ============================================================
|
| 649 |
+
@dataclass
|
| 650 |
+
class WorkbookBundle:
|
| 651 |
+
dataset: pd.DataFrame
|
| 652 |
+
extra_sheets: Dict[str, pd.DataFrame]
|
| 653 |
+
|
| 654 |
+
|
| 655 |
+
class WorkbookStore:
|
| 656 |
+
def __init__(self, data_path: Path, seed_path: Optional[Path] = None):
|
| 657 |
+
self.path = data_path
|
| 658 |
+
self.seed_path = seed_path
|
| 659 |
+
self.ensure_exists()
|
| 660 |
+
|
| 661 |
+
def ensure_exists(self) -> None:
|
| 662 |
+
if self.path.exists():
|
| 663 |
+
return
|
| 664 |
+
if self.seed_path and self.seed_path.exists() and self.seed_path.resolve() != self.path.resolve():
|
| 665 |
+
shutil.copy2(self.seed_path, self.path)
|
| 666 |
+
return
|
| 667 |
+
ensure_seed_exists(self.path)
|
| 668 |
+
|
| 669 |
+
def load_bundle(self) -> WorkbookBundle:
|
| 670 |
+
self.ensure_exists()
|
| 671 |
+
xls = pd.ExcelFile(self.path)
|
| 672 |
+
main = pd.read_excel(self.path, sheet_name=xls.sheet_names[0])
|
| 673 |
+
main.columns = [clean_text(c) for c in main.columns]
|
| 674 |
+
for col in HEADERS:
|
| 675 |
+
if col not in main.columns:
|
| 676 |
+
main[col] = ""
|
| 677 |
+
main = main[HEADERS].copy()
|
| 678 |
+
for col in HEADERS:
|
| 679 |
+
main[col] = main[col].map(clean_text)
|
| 680 |
+
|
| 681 |
+
extra_sheets: Dict[str, pd.DataFrame] = {}
|
| 682 |
+
for sheet in xls.sheet_names[1:]:
|
| 683 |
+
extra_sheets[sheet] = pd.read_excel(self.path, sheet_name=sheet, header=None)
|
| 684 |
+
if NOTES_SHEET not in extra_sheets:
|
| 685 |
+
extra_sheets[NOTES_SHEET] = pd.DataFrame([[note] for note in DEFAULT_NOTES])
|
| 686 |
+
|
| 687 |
+
return WorkbookBundle(dataset=main, extra_sheets=extra_sheets)
|
| 688 |
+
|
| 689 |
+
def save_bundle(self, bundle: WorkbookBundle) -> None:
|
| 690 |
+
bundle.dataset = bundle.dataset.fillna("")
|
| 691 |
+
with pd.ExcelWriter(self.path, engine="openpyxl") as writer:
|
| 692 |
+
bundle.dataset.to_excel(writer, sheet_name=MAIN_SHEET, index=False)
|
| 693 |
+
for sheet_name, df in bundle.extra_sheets.items():
|
| 694 |
+
df.to_excel(writer, sheet_name=sheet_name, index=False, header=False)
|
| 695 |
+
|
| 696 |
+
def replace_from_upload(self, uploaded_path: str) -> None:
|
| 697 |
+
xls = pd.ExcelFile(uploaded_path)
|
| 698 |
+
main = pd.read_excel(uploaded_path, sheet_name=xls.sheet_names[0])
|
| 699 |
+
main.columns = [clean_text(c) for c in main.columns]
|
| 700 |
+
for col in HEADERS:
|
| 701 |
+
if col not in main.columns:
|
| 702 |
+
main[col] = ""
|
| 703 |
+
main = main[HEADERS].copy()
|
| 704 |
+
for col in HEADERS:
|
| 705 |
+
main[col] = main[col].map(clean_text)
|
| 706 |
+
extras: Dict[str, pd.DataFrame] = {}
|
| 707 |
+
for sheet in xls.sheet_names[1:]:
|
| 708 |
+
extras[sheet] = pd.read_excel(uploaded_path, sheet_name=sheet, header=None)
|
| 709 |
+
if NOTES_SHEET not in extras:
|
| 710 |
+
extras[NOTES_SHEET] = pd.DataFrame([[note] for note in DEFAULT_NOTES])
|
| 711 |
+
self.save_bundle(WorkbookBundle(dataset=main, extra_sheets=extras))
|
| 712 |
+
|
| 713 |
+
|
| 714 |
+
# ============================================================
|
| 715 |
+
# SML (Small Machine Learning) Backend
|
| 716 |
+
# — Runs entirely locally, no API key required
|
| 717 |
+
# — Uses TF-IDF retrieval + rule-based extraction + template generation
|
| 718 |
+
# ============================================================
|
| 719 |
+
class SMLBackend:
|
| 720 |
+
"""
|
| 721 |
+
Lightweight local inference engine.
|
| 722 |
+
Extracts structured fields via regex + keyword heuristics,
|
| 723 |
+
then generates quote guidance by template-blending the
|
| 724 |
+
top-k nearest historical examples.
|
| 725 |
+
"""
|
| 726 |
+
|
| 727 |
+
AIRFLOW_PATTERN = re.compile(r"(\d[\d,]*)\s*(?:cfm|acfm|scfm)", re.IGNORECASE)
|
| 728 |
+
MATERIAL_KEYWORDS = {
|
| 729 |
+
"pharmaceutical": ["pharma", "pharmaceutical", "drug", "api ", "gmp"],
|
| 730 |
+
"metal grinding": ["grind", "metal grind", "steel grind", "aluminum grind"],
|
| 731 |
+
"wood dust": ["wood", "sawdust", "lumber", "mdf", "plywood"],
|
| 732 |
+
"chemical": ["chemical", "solvent", "acid", "caustic", "reactive"],
|
| 733 |
+
"food": ["food", "grain", "flour", "sugar", "starch", "spice"],
|
| 734 |
+
"plastic": ["plastic", "polymer", "pellet", "resin", "pvc"],
|
| 735 |
+
"cement/mineral": ["cement", "concrete", "lime", "silica", "mineral"],
|
| 736 |
+
"general industrial": [],
|
| 737 |
+
}
|
| 738 |
+
HAZARD_KEYWORDS = {
|
| 739 |
+
"combustible": ["combustible", "flammable", "explosive", "deflagration", "nfpa 652", "nfpa 654"],
|
| 740 |
+
"corrosive": ["corrosive", "corrosion", "acid", "caustic", "hcl", "h2so4", "stainless"],
|
| 741 |
+
"spark risk": ["spark", "sparks", "ignition", "hot work", "grinding", "welding"],
|
| 742 |
+
"toxic": ["toxic", "carcinogen", "hazmat", "osha", "pel ", "tlv "],
|
| 743 |
+
"high humidity": ["humid", "moisture", "wet", "condensation", "steam"],
|
| 744 |
+
}
|
| 745 |
+
COLLECTOR_KEYWORDS = {
|
| 746 |
+
"cartridge collector": ["cartridge", "nano", "nanofiber", "pleated"],
|
| 747 |
+
"baghouse": ["baghouse", "bag house", "pulse jet", "pulse-jet", "shaker", "reverse air"],
|
| 748 |
+
"cyclone": ["cyclone", "centrifugal", "pre-separator"],
|
| 749 |
+
"wet scrubber": ["wet scrubber", "scrubber", "venturi", "wet collector"],
|
| 750 |
+
"electrostatic": ["esp", "electrostatic", "precipitator"],
|
| 751 |
+
}
|
| 752 |
+
|
| 753 |
+
def __init__(self, dataset: pd.DataFrame, notes: List[str]):
|
| 754 |
+
self.dataset = dataset
|
| 755 |
+
self.notes = notes
|
| 756 |
+
self.vectorizer: Optional[TfidfVectorizer] = None
|
| 757 |
+
self.matrix = None
|
| 758 |
+
self.examples = dataset[
|
| 759 |
+
(dataset["Request"].map(clean_text) != "") &
|
| 760 |
+
((dataset["Information Extracted"].map(clean_text) != "") |
|
| 761 |
+
(dataset["Design"].map(clean_text) != ""))
|
| 762 |
+
].reset_index(drop=True)
|
| 763 |
+
self._build_index()
|
| 764 |
+
|
| 765 |
+
def _build_index(self) -> None:
|
| 766 |
+
if self.examples.empty:
|
| 767 |
+
return
|
| 768 |
+
corpus = (
|
| 769 |
+
self.examples["Request"].fillna("") + " || " +
|
| 770 |
+
self.examples["Information Extracted"].fillna("") + " || " +
|
| 771 |
+
self.examples["Design"].fillna("")
|
| 772 |
+
).tolist()
|
| 773 |
+
self.vectorizer = TfidfVectorizer(ngram_range=(1, 2), stop_words="english", max_features=5000)
|
| 774 |
+
self.matrix = self.vectorizer.fit_transform(corpus)
|
| 775 |
+
|
| 776 |
+
def _match_keywords(self, text: str, kw_dict: Dict[str, List[str]]) -> List[str]:
|
| 777 |
+
text_lower = text.lower()
|
| 778 |
+
matches = []
|
| 779 |
+
for category, keywords in kw_dict.items():
|
| 780 |
+
if any(kw in text_lower for kw in keywords):
|
| 781 |
+
matches.append(category)
|
| 782 |
+
return matches
|
| 783 |
+
|
| 784 |
+
def _extract_cfm(self, text: str) -> str:
|
| 785 |
+
m = self.AIRFLOW_PATTERN.search(text)
|
| 786 |
+
return m.group(0).upper() if m else "Not specified — confirm with customer"
|
| 787 |
+
|
| 788 |
+
def _detect_material(self, text: str) -> str:
|
| 789 |
+
text_lower = text.lower()
|
| 790 |
+
for material, keywords in self.MATERIAL_KEYWORDS.items():
|
| 791 |
+
if material == "general industrial":
|
| 792 |
+
continue
|
| 793 |
+
if any(kw in text_lower for kw in keywords):
|
| 794 |
+
return material
|
| 795 |
+
return "General industrial dust"
|
| 796 |
+
|
| 797 |
+
def _detect_hazards(self, text: str) -> List[str]:
|
| 798 |
+
return self._match_keywords(text, self.HAZARD_KEYWORDS) or ["No specific hazard keywords detected — verify with SME"]
|
| 799 |
+
|
| 800 |
+
def _suggest_collector(self, text: str, material: str, hazards: List[str]) -> str:
|
| 801 |
+
text_lower = text.lower()
|
| 802 |
+
for ctype, keywords in self.COLLECTOR_KEYWORDS.items():
|
| 803 |
+
if any(kw in text_lower for kw in keywords):
|
| 804 |
+
return ctype
|
| 805 |
+
# heuristic fallback by material
|
| 806 |
+
if "pharma" in material:
|
| 807 |
+
return "cartridge collector (PTFE media recommended for pharma)"
|
| 808 |
+
if "metal" in material:
|
| 809 |
+
return "cartridge or baghouse with spark arrestor"
|
| 810 |
+
if "wood" in material:
|
| 811 |
+
return "baghouse or cartridge collector (check NFPA 652/664)"
|
| 812 |
+
if "cement" in material or "mineral" in material:
|
| 813 |
+
return "pulse-jet baghouse"
|
| 814 |
+
return "pulse-jet cartridge collector (general recommendation)"
|
| 815 |
+
|
| 816 |
+
def retrieve(self, request_text: str, sme_text: str, top_k: int = 4) -> pd.DataFrame:
|
| 817 |
+
if self.vectorizer is None or self.matrix is None:
|
| 818 |
+
return pd.DataFrame(columns=["Request", "Information Extracted", "Design", "Similarity"])
|
| 819 |
+
query = f"{clean_text(request_text)} || {clean_text(sme_text)}"
|
| 820 |
+
qv = self.vectorizer.transform([query])
|
| 821 |
+
scores = cosine_similarity(qv, self.matrix).ravel()
|
| 822 |
+
top_idx = scores.argsort()[::-1][:max(1, min(top_k, len(scores)))]
|
| 823 |
+
out = self.examples.iloc[top_idx].copy()
|
| 824 |
+
out["Similarity"] = scores[top_idx]
|
| 825 |
+
out = out[["Request", "Information Extracted", "Design", "Similarity"]].reset_index(drop=True)
|
| 826 |
+
out["Similarity"] = out["Similarity"].map(lambda x: round(float(x), 4))
|
| 827 |
+
return out
|
| 828 |
+
|
| 829 |
+
def generate(self, request_text: str, sme_text: str = "", top_k: int = 4) -> Dict[str, Any]:
|
| 830 |
+
combined = f"{request_text} {sme_text}"
|
| 831 |
+
cfm = self._extract_cfm(combined)
|
| 832 |
+
material = self._detect_material(combined)
|
| 833 |
+
hazards = self._detect_hazards(combined)
|
| 834 |
+
collector = self._suggest_collector(combined, material, hazards)
|
| 835 |
+
retrieved = self.retrieve(request_text, sme_text, top_k)
|
| 836 |
+
|
| 837 |
+
# Build information_extracted by blending extraction + top example context
|
| 838 |
+
info_parts = [
|
| 839 |
+
f"Application: {material}.",
|
| 840 |
+
f"Airflow: {cfm}.",
|
| 841 |
+
f"Detected hazards: {'; '.join(hazards)}.",
|
| 842 |
+
]
|
| 843 |
+
if sme_text:
|
| 844 |
+
info_parts.append(f"SME notes: {sme_text.strip('.')}.")
|
| 845 |
+
if self.notes:
|
| 846 |
+
info_parts.append(f"Business context: {'; '.join(self.notes[:3])}.")
|
| 847 |
+
if not retrieved.empty:
|
| 848 |
+
best = retrieved.iloc[0]
|
| 849 |
+
if best["Similarity"] > 0.05 and clean_text(best["Information Extracted"]):
|
| 850 |
+
info_parts.append(f"Similar prior case: {summarize_text(best['Information Extracted'], 120)}")
|
| 851 |
+
information_extracted = " ".join(info_parts)
|
| 852 |
+
|
| 853 |
+
# Design guidance
|
| 854 |
+
design_parts = [
|
| 855 |
+
f"Recommend a {collector}.",
|
| 856 |
+
]
|
| 857 |
+
if "combustible" in hazards:
|
| 858 |
+
design_parts.append("Include NFPA combustibility review and explosion protection before quoting final scope.")
|
| 859 |
+
if "corrosive" in hazards:
|
| 860 |
+
design_parts.append("Specify corrosion-resistant construction (304/316 SS or coated carbon steel); confirm chemical compatibility.")
|
| 861 |
+
if "spark risk" in hazards:
|
| 862 |
+
design_parts.append("Add spark detection and suppression or pre-separator spark arrestor.")
|
| 863 |
+
if "pharma" in material:
|
| 864 |
+
design_parts.append("GMP cleanability, PTFE filter media, and cGMP documentation package required.")
|
| 865 |
+
if not retrieved.empty:
|
| 866 |
+
best = retrieved.iloc[0]
|
| 867 |
+
if best["Similarity"] > 0.05 and clean_text(best["Design"]):
|
| 868 |
+
design_parts.append(f"Informed by similar case: {summarize_text(best['Design'], 120)}")
|
| 869 |
+
design_parts.append("Confirm all open questions with customer before issuing formal quote.")
|
| 870 |
+
design = " ".join(design_parts)
|
| 871 |
+
|
| 872 |
+
open_questions = ["Confirm airflow (CFM) if not specified", "Verify inlet concentration and particle size", "Confirm electrical classification (Class/Div or Zone)"]
|
| 873 |
+
if cfm == "Not specified — confirm with customer":
|
| 874 |
+
open_questions.insert(0, "Airflow CFM not found in request — must be confirmed")
|
| 875 |
+
|
| 876 |
+
assumptions = [
|
| 877 |
+
"SML local inference used — no LLM API key configured.",
|
| 878 |
+
f"Material classification: {material} (keyword-based, verify with SME).",
|
| 879 |
+
f"Collector suggestion: {collector} (heuristic, review before quoting).",
|
| 880 |
+
"All outputs are draft guidance only and require SME validation.",
|
| 881 |
+
]
|
| 882 |
+
|
| 883 |
+
return {
|
| 884 |
+
"information_extracted": information_extracted,
|
| 885 |
+
"design": design,
|
| 886 |
+
"quote_inputs": {
|
| 887 |
+
"application": material,
|
| 888 |
+
"airflow_cfm": cfm,
|
| 889 |
+
"dust_or_material": material,
|
| 890 |
+
"collector_type": collector,
|
| 891 |
+
"fan_notes": "Fan selection pending CFM and static pressure confirmation.",
|
| 892 |
+
"material_of_construction": "TBD — depends on hazard/corrosion review",
|
| 893 |
+
"filter_media": "TBD — depends on application",
|
| 894 |
+
"safety_notes": "; ".join(hazards),
|
| 895 |
+
"open_questions": open_questions,
|
| 896 |
+
},
|
| 897 |
+
"assumptions": assumptions,
|
| 898 |
+
"retrieved_examples": retrieved,
|
| 899 |
+
"raw_model_output": f"[SML Backend] material={material}, cfm={cfm}, hazards={hazards}, collector={collector}",
|
| 900 |
+
"backend": "sml",
|
| 901 |
+
}
|
| 902 |
+
|
| 903 |
+
|
| 904 |
+
# ============================================================
|
| 905 |
+
# LLM + Engine
|
| 906 |
+
# ============================================================
|
| 907 |
+
def _get_anthropic_client(api_key_override: str = ""):
|
| 908 |
+
try:
|
| 909 |
+
from anthropic import Anthropic
|
| 910 |
+
except ImportError:
|
| 911 |
+
return None
|
| 912 |
+
key = api_key_override.strip() or os.getenv("ANTHROPIC_API_KEY", "").strip()
|
| 913 |
+
if not key:
|
| 914 |
+
return None
|
| 915 |
+
try:
|
| 916 |
+
return Anthropic(api_key=key)
|
| 917 |
+
except Exception:
|
| 918 |
+
return None
|
| 919 |
+
|
| 920 |
+
|
| 921 |
+
class QuoteRequestEngine:
|
| 922 |
+
def __init__(self, store: WorkbookStore):
|
| 923 |
+
self.store = store
|
| 924 |
+
self.reload()
|
| 925 |
+
|
| 926 |
+
def reload(self) -> None:
|
| 927 |
+
bundle = self.store.load_bundle()
|
| 928 |
+
self.bundle = bundle
|
| 929 |
+
self.dataset = bundle.dataset.copy().reset_index(drop=True)
|
| 930 |
+
self.notes = self._flatten_notes(bundle.extra_sheets)
|
| 931 |
+
self.examples = self.dataset[
|
| 932 |
+
(self.dataset["Request"].map(clean_text) != "") &
|
| 933 |
+
((self.dataset["Information Extracted"].map(clean_text) != "") |
|
| 934 |
+
(self.dataset["Design"].map(clean_text) != ""))
|
| 935 |
+
].reset_index(drop=True)
|
| 936 |
+
|
| 937 |
+
self.vectorizer: Optional[TfidfVectorizer] = None
|
| 938 |
+
self.matrix = None
|
| 939 |
+
if not self.examples.empty:
|
| 940 |
+
corpus = (
|
| 941 |
+
self.examples["Request"].fillna("") + " || " +
|
| 942 |
+
self.examples["Information Extracted"].fillna("") + " || " +
|
| 943 |
+
self.examples["Design"].fillna("")
|
| 944 |
+
).tolist()
|
| 945 |
+
self.vectorizer = TfidfVectorizer(ngram_range=(1, 2), stop_words="english")
|
| 946 |
+
self.matrix = self.vectorizer.fit_transform(corpus)
|
| 947 |
+
|
| 948 |
+
self._sml = SMLBackend(self.dataset, self.notes)
|
| 949 |
+
|
| 950 |
+
@staticmethod
|
| 951 |
+
def _flatten_notes(extra_sheets: Dict[str, pd.DataFrame]) -> List[str]:
|
| 952 |
+
notes: List[str] = []
|
| 953 |
+
for df in extra_sheets.values():
|
| 954 |
+
for item in df.fillna("").astype(str).values.ravel().tolist():
|
| 955 |
+
item = clean_text(item)
|
| 956 |
+
if item and item.lower() != "nan":
|
| 957 |
+
notes.append(item)
|
| 958 |
+
return notes
|
| 959 |
+
|
| 960 |
+
def retrieve_examples(self, request_text: str, sme_text: str, top_k: int = 4) -> pd.DataFrame:
|
| 961 |
+
if self.vectorizer is None or self.matrix is None or self.examples.empty:
|
| 962 |
+
return pd.DataFrame(columns=["Request", "Information Extracted", "Design", "Similarity"])
|
| 963 |
+
query = f"{clean_text(request_text)} || {clean_text(sme_text)}"
|
| 964 |
+
qv = self.vectorizer.transform([query])
|
| 965 |
+
scores = cosine_similarity(qv, self.matrix).ravel()
|
| 966 |
+
top_idx = scores.argsort()[::-1][:max(1, min(top_k, len(scores)))]
|
| 967 |
+
out = self.examples.iloc[top_idx].copy()
|
| 968 |
+
out["Similarity"] = scores[top_idx]
|
| 969 |
+
out = out[["Request", "Information Extracted", "Design", "Similarity"]].reset_index(drop=True)
|
| 970 |
+
out["Similarity"] = out["Similarity"].map(lambda x: round(float(x), 4))
|
| 971 |
+
return out
|
| 972 |
+
|
| 973 |
+
def _build_messages(self, request_text: str, sme_text: str, retrieved: pd.DataFrame) -> Tuple[str, str]:
|
| 974 |
+
system_prompt = """
|
| 975 |
+
You are an industrial quote-request handler for a future quote automation system.
|
| 976 |
+
Return only valid JSON with this exact schema:
|
| 977 |
+
{
|
| 978 |
+
"information_extracted": "string",
|
| 979 |
+
"design": "string",
|
| 980 |
+
"quote_inputs": {
|
| 981 |
+
"application": "string",
|
| 982 |
+
"airflow_cfm": "string",
|
| 983 |
+
"dust_or_material": "string",
|
| 984 |
+
"collector_type": "string",
|
| 985 |
+
"fan_notes": "string",
|
| 986 |
+
"material_of_construction": "string",
|
| 987 |
+
"filter_media": "string",
|
| 988 |
+
"safety_notes": "string",
|
| 989 |
+
"open_questions": ["string"]
|
| 990 |
+
},
|
| 991 |
+
"assumptions": ["string"]
|
| 992 |
+
}
|
| 993 |
+
Rules: treat requests as customer language that may be incomplete. SME notes are authoritative. Make design output quote-ready. Do not invent pricing or lead times. Clearly state unknowns. Do not wrap in markdown.
|
| 994 |
+
""".strip()
|
| 995 |
+
|
| 996 |
+
if retrieved.empty:
|
| 997 |
+
examples_text = "No prior examples available."
|
| 998 |
+
else:
|
| 999 |
+
blocks = []
|
| 1000 |
+
for idx, row in retrieved.iterrows():
|
| 1001 |
+
blocks.append(f"Example {idx + 1}\nRequest: {row['Request']}\nSME: {row['Information Extracted']}\nDesign: {row['Design']}\nSimilarity: {row['Similarity']}")
|
| 1002 |
+
examples_text = "\n\n".join(blocks)
|
| 1003 |
+
|
| 1004 |
+
notes_block = "\n".join(f"- {n}" for n in self.notes[:30]) if self.notes else "- None"
|
| 1005 |
+
|
| 1006 |
+
user_prompt = f"""
|
| 1007 |
+
Customer Request: {clean_text(request_text) or '[Not provided]'}
|
| 1008 |
+
SME Knowledge: {clean_text(sme_text) or '[Not provided]'}
|
| 1009 |
+
Global SME Notes:\n{notes_block}
|
| 1010 |
+
Historical Examples:\n{examples_text}
|
| 1011 |
+
Generate quote-ready response using the schema exactly.
|
| 1012 |
+
""".strip()
|
| 1013 |
+
return system_prompt, user_prompt
|
| 1014 |
+
|
| 1015 |
+
def _repair_json(self, broken: str, client) -> Dict[str, Any]:
|
| 1016 |
+
response = client.messages.create(
|
| 1017 |
+
model=DEFAULT_MODEL, max_tokens=1600, temperature=0,
|
| 1018 |
+
system="Repair malformed JSON. Return only valid JSON.",
|
| 1019 |
+
messages=[{"role": "user", "content": f"Repair into valid JSON, no markdown:\n{broken}"}],
|
| 1020 |
+
)
|
| 1021 |
+
raw = "".join(b.text for b in response.content if getattr(b, "type", None) == "text").strip()
|
| 1022 |
+
return self._parse_json(raw, client=client, allow_repair=False)
|
| 1023 |
+
|
| 1024 |
+
def _parse_json(self, text: str, client=None, allow_repair: bool = True) -> Dict[str, Any]:
|
| 1025 |
+
text = strip_code_fences(text)
|
| 1026 |
+
try:
|
| 1027 |
+
data = json.loads(extract_first_balanced_json(text))
|
| 1028 |
+
except Exception:
|
| 1029 |
+
if allow_repair and client:
|
| 1030 |
+
data = self._repair_json(text, client)
|
| 1031 |
+
else:
|
| 1032 |
+
raise
|
| 1033 |
+
data.setdefault("information_extracted", "")
|
| 1034 |
+
data.setdefault("design", "")
|
| 1035 |
+
data.setdefault("quote_inputs", {})
|
| 1036 |
+
data.setdefault("assumptions", [])
|
| 1037 |
+
data["information_extracted"] = clean_text(data["information_extracted"])
|
| 1038 |
+
data["design"] = clean_text(data["design"])
|
| 1039 |
+
if not isinstance(data.get("quote_inputs"), dict):
|
| 1040 |
+
data["quote_inputs"] = {}
|
| 1041 |
+
data["assumptions"] = normalize_list(data["assumptions"])
|
| 1042 |
+
return data
|
| 1043 |
+
|
| 1044 |
+
def generate_quote(
|
| 1045 |
+
self,
|
| 1046 |
+
request_text: str,
|
| 1047 |
+
sme_text: str = "",
|
| 1048 |
+
top_k: int = 4,
|
| 1049 |
+
temperature: float = 0.1,
|
| 1050 |
+
api_key_override: str = "",
|
| 1051 |
+
) -> Dict[str, Any]:
|
| 1052 |
+
request_text = clean_text(request_text)
|
| 1053 |
+
sme_text = clean_text(sme_text)
|
| 1054 |
+
if not request_text and not sme_text:
|
| 1055 |
+
raise ValueError("Provide a request or SME notes before generating.")
|
| 1056 |
+
|
| 1057 |
+
client = _get_anthropic_client(api_key_override)
|
| 1058 |
+
|
| 1059 |
+
# ── LLM path ──
|
| 1060 |
+
if client:
|
| 1061 |
+
retrieved = self.retrieve_examples(request_text, sme_text, top_k)
|
| 1062 |
+
system_prompt, user_prompt = self._build_messages(request_text, sme_text, retrieved)
|
| 1063 |
+
response = client.messages.create(
|
| 1064 |
+
model=DEFAULT_MODEL,
|
| 1065 |
+
max_tokens=1800,
|
| 1066 |
+
temperature=float(temperature),
|
| 1067 |
+
system=system_prompt,
|
| 1068 |
+
messages=[{"role": "user", "content": user_prompt}],
|
| 1069 |
+
)
|
| 1070 |
+
raw = "".join(b.text for b in response.content if getattr(b, "type", None) == "text").strip()
|
| 1071 |
+
parsed = self._parse_json(raw, client=client, allow_repair=True)
|
| 1072 |
+
parsed["raw_model_output"] = raw
|
| 1073 |
+
parsed["retrieved_examples"] = retrieved
|
| 1074 |
+
parsed["request"] = request_text
|
| 1075 |
+
parsed["sml_input"] = sme_text
|
| 1076 |
+
parsed["backend"] = "llm"
|
| 1077 |
+
return parsed
|
| 1078 |
+
|
| 1079 |
+
# ── SML fallback ──
|
| 1080 |
+
return self._sml.generate(request_text, sme_text, top_k)
|
| 1081 |
+
|
| 1082 |
+
|
| 1083 |
+
# ============================================================
|
| 1084 |
+
# Global store + engine
|
| 1085 |
+
# ============================================================
|
| 1086 |
+
store = WorkbookStore(DATA_PATH, seed_path=SEED_WORKBOOK if SEED_WORKBOOK.exists() else None)
|
| 1087 |
+
engine = QuoteRequestEngine(store)
|
| 1088 |
+
|
| 1089 |
+
|
| 1090 |
+
# ============================================================
|
| 1091 |
+
# Helper functions for UI
|
| 1092 |
+
# ============================================================
|
| 1093 |
+
def get_dataset_preview() -> pd.DataFrame:
|
| 1094 |
+
engine.reload()
|
| 1095 |
+
df = engine.dataset.copy().reset_index(drop=True)
|
| 1096 |
+
if df.empty:
|
| 1097 |
+
return pd.DataFrame(columns=["row_id"] + HEADERS)
|
| 1098 |
+
df.insert(0, "row_id", df.index + 1)
|
| 1099 |
+
return df
|
| 1100 |
+
|
| 1101 |
+
|
| 1102 |
+
def get_note_preview() -> pd.DataFrame:
|
| 1103 |
+
engine.reload()
|
| 1104 |
+
if not engine.notes:
|
| 1105 |
+
return pd.DataFrame({"note_id": [], "SME Note": []})
|
| 1106 |
+
return pd.DataFrame({"note_id": list(range(1, len(engine.notes) + 1)), "SME Note": engine.notes})
|
| 1107 |
+
|
| 1108 |
+
|
| 1109 |
+
def get_row_choices() -> List[Tuple[str, int]]:
|
| 1110 |
+
df = get_dataset_preview()
|
| 1111 |
+
if df.empty:
|
| 1112 |
+
return []
|
| 1113 |
+
return [(f"{int(r.row_id)} | {summarize_text(r.Request, 80)}", int(r.row_id)) for r in df.itertuples(index=False)]
|
| 1114 |
+
|
| 1115 |
+
|
| 1116 |
+
def get_downloadable_path() -> str:
|
| 1117 |
+
store.ensure_exists()
|
| 1118 |
+
return str(DATA_PATH)
|
| 1119 |
+
|
| 1120 |
+
|
| 1121 |
+
def api_key_active(override: str = "") -> bool:
|
| 1122 |
+
return bool(_get_anthropic_client(override))
|
| 1123 |
+
|
| 1124 |
+
|
| 1125 |
+
def backend_label(override: str = "") -> str:
|
| 1126 |
+
if api_key_active(override):
|
| 1127 |
+
return '<span class="sml-badge llm">⬤ LLM · Claude Active</span>'
|
| 1128 |
+
return '<span class="sml-badge sml">⬤ SML · Local Inference</span>'
|
| 1129 |
+
|
| 1130 |
+
|
| 1131 |
+
def status_html() -> str:
|
| 1132 |
+
rows = len(engine.dataset)
|
| 1133 |
+
notes = len(engine.notes)
|
| 1134 |
+
backend = "LLM (Claude)" if api_key_active() else "SML (Local)"
|
| 1135 |
+
return f"""<div class="forge-stat-grid">
|
| 1136 |
+
<div class="forge-stat"><div class="forge-stat-value">{rows}</div><div class="forge-stat-label">Training Rows</div></div>
|
| 1137 |
+
<div class="forge-stat"><div class="forge-stat-value">{notes}</div><div class="forge-stat-label">SME Notes</div></div>
|
| 1138 |
+
<div class="forge-stat"><div class="forge-stat-value">{"✓" if api_key_active() else "—"}</div><div class="forge-stat-label">API Key</div></div>
|
| 1139 |
+
<div class="forge-stat"><div class="forge-stat-value" style="font-size:1.1rem;padding-top:0.5rem">{backend}</div><div class="forge-stat-label">Active Backend</div></div>
|
| 1140 |
+
</div>"""
|
| 1141 |
+
|
| 1142 |
+
|
| 1143 |
+
def format_quote_inputs(qi: Dict[str, Any]) -> str:
|
| 1144 |
+
if not qi:
|
| 1145 |
+
return ""
|
| 1146 |
+
keys = ["application", "airflow_cfm", "dust_or_material", "collector_type",
|
| 1147 |
+
"fan_notes", "material_of_construction", "filter_media", "safety_notes", "open_questions"]
|
| 1148 |
+
blocks = []
|
| 1149 |
+
for key in keys:
|
| 1150 |
+
val = qi.get(key, "")
|
| 1151 |
+
if isinstance(val, list):
|
| 1152 |
+
val = "\n".join(f" · {clean_text(v)}" for v in val if clean_text(v))
|
| 1153 |
+
else:
|
| 1154 |
+
val = clean_text(val)
|
| 1155 |
+
blocks.append(f"{key.replace('_',' ').upper()}\n{val or '[Not provided]'}")
|
| 1156 |
+
return "\n\n".join(blocks)
|
| 1157 |
+
|
| 1158 |
+
|
| 1159 |
+
def format_assumptions(items: List[str]) -> str:
|
| 1160 |
+
if not items:
|
| 1161 |
+
return "[None listed]"
|
| 1162 |
+
return "\n".join(f"· {clean_text(i)}" for i in items if clean_text(i))
|
| 1163 |
+
|
| 1164 |
+
|
| 1165 |
+
# ============================================================
|
| 1166 |
+
# Action handlers
|
| 1167 |
+
# ============================================================
|
| 1168 |
+
def generate_quote_action(request_text, sme_text, top_k, temperature, api_key_override):
|
| 1169 |
+
try:
|
| 1170 |
+
result = engine.generate_quote(
|
| 1171 |
+
request_text=request_text, sme_text=sme_text,
|
| 1172 |
+
top_k=int(top_k), temperature=float(temperature),
|
| 1173 |
+
api_key_override=api_key_override,
|
| 1174 |
+
)
|
| 1175 |
+
be = result.get("backend", "sml")
|
| 1176 |
+
be_label = "⬤ LLM · Claude" if be == "llm" else "⬤ SML · Local Inference"
|
| 1177 |
+
return (
|
| 1178 |
+
result.get("information_extracted", ""),
|
| 1179 |
+
result.get("design", ""),
|
| 1180 |
+
format_quote_inputs(result.get("quote_inputs", {})),
|
| 1181 |
+
format_assumptions(result.get("assumptions", [])),
|
| 1182 |
+
result.get("retrieved_examples", pd.DataFrame()),
|
| 1183 |
+
result.get("raw_model_output", ""),
|
| 1184 |
+
f'<div class="forge-alert {"success" if be == "llm" else "warn"}">{be_label}</div>',
|
| 1185 |
+
)
|
| 1186 |
+
except Exception as e:
|
| 1187 |
+
empty = pd.DataFrame(columns=["Request", "Information Extracted", "Design", "Similarity"])
|
| 1188 |
+
return "", "", "", str(e), empty, "", f'<div class="forge-alert error">Error: {e}</div>'
|
| 1189 |
+
|
| 1190 |
+
|
| 1191 |
+
def save_generated_row(request_text, info, design):
|
| 1192 |
+
request_text, info, design = clean_text(request_text), clean_text(info), clean_text(design)
|
| 1193 |
+
if not any([request_text, info, design]):
|
| 1194 |
+
raise gr.Error("Nothing to save.")
|
| 1195 |
+
with DATA_LOCK:
|
| 1196 |
+
bundle = store.load_bundle()
|
| 1197 |
+
bundle.dataset = pd.concat([bundle.dataset, pd.DataFrame([{"Request": request_text, "Information Extracted": info, "Design": design}])], ignore_index=True)
|
| 1198 |
+
store.save_bundle(bundle)
|
| 1199 |
+
engine.reload()
|
| 1200 |
+
return "✓ Row saved.", get_dataset_preview(), get_note_preview(), gr.Dropdown(choices=get_row_choices(), value=None), get_downloadable_path(), status_html()
|
| 1201 |
+
|
| 1202 |
+
|
| 1203 |
+
def add_request_only(request_text):
|
| 1204 |
+
request_text = clean_text(request_text)
|
| 1205 |
+
if not request_text:
|
| 1206 |
+
raise gr.Error("Enter a request.")
|
| 1207 |
+
with DATA_LOCK:
|
| 1208 |
+
bundle = store.load_bundle()
|
| 1209 |
+
bundle.dataset = pd.concat([bundle.dataset, pd.DataFrame([{"Request": request_text, "Information Extracted": "", "Design": ""}])], ignore_index=True)
|
| 1210 |
+
store.save_bundle(bundle)
|
| 1211 |
+
engine.reload()
|
| 1212 |
+
return "✓ Request appended.", get_dataset_preview(), get_note_preview(), gr.Dropdown(choices=get_row_choices(), value=None), get_downloadable_path(), status_html()
|
| 1213 |
+
|
| 1214 |
+
|
| 1215 |
+
def add_full_training_row(req, sme, design):
|
| 1216 |
+
req = clean_text(req)
|
| 1217 |
+
if not req:
|
| 1218 |
+
raise gr.Error("Request field required.")
|
| 1219 |
+
with DATA_LOCK:
|
| 1220 |
+
bundle = store.load_bundle()
|
| 1221 |
+
bundle.dataset = pd.concat([bundle.dataset, pd.DataFrame([{"Request": req, "Information Extracted": clean_text(sme), "Design": clean_text(design)}])], ignore_index=True)
|
| 1222 |
+
store.save_bundle(bundle)
|
| 1223 |
+
engine.reload()
|
| 1224 |
+
return "✓ Full row added.", get_dataset_preview(), get_note_preview(), gr.Dropdown(choices=get_row_choices(), value=None), get_downloadable_path(), status_html()
|
| 1225 |
+
|
| 1226 |
+
|
| 1227 |
+
def add_global_sme_note(note_text):
|
| 1228 |
+
note_text = clean_text(note_text)
|
| 1229 |
+
if not note_text:
|
| 1230 |
+
raise gr.Error("Enter a note.")
|
| 1231 |
+
with DATA_LOCK:
|
| 1232 |
+
bundle = store.load_bundle()
|
| 1233 |
+
notes_df = bundle.extra_sheets.get(NOTES_SHEET, pd.DataFrame(columns=[0]))
|
| 1234 |
+
notes_df = pd.concat([notes_df, pd.DataFrame([[note_text]])], ignore_index=True)
|
| 1235 |
+
bundle.extra_sheets[NOTES_SHEET] = notes_df
|
| 1236 |
+
store.save_bundle(bundle)
|
| 1237 |
+
engine.reload()
|
| 1238 |
+
return "✓ Note added.", get_dataset_preview(), get_note_preview(), gr.Dropdown(choices=get_row_choices(), value=None), get_downloadable_path(), status_html()
|
| 1239 |
+
|
| 1240 |
+
|
| 1241 |
+
def load_row_for_edit(row_id):
|
| 1242 |
+
if row_id is None:
|
| 1243 |
+
return "", "", ""
|
| 1244 |
+
df = get_dataset_preview()
|
| 1245 |
+
row = df[df["row_id"] == int(row_id)]
|
| 1246 |
+
if row.empty:
|
| 1247 |
+
return "", "", ""
|
| 1248 |
+
r = row.iloc[0]
|
| 1249 |
+
return r["Request"], r["Information Extracted"], r["Design"]
|
| 1250 |
+
|
| 1251 |
+
|
| 1252 |
+
def update_row_fields(row_id, req, sme, design):
|
| 1253 |
+
if row_id is None:
|
| 1254 |
+
raise gr.Error("Select a row.")
|
| 1255 |
+
with DATA_LOCK:
|
| 1256 |
+
bundle = store.load_bundle()
|
| 1257 |
+
df = bundle.dataset.copy().reset_index(drop=True)
|
| 1258 |
+
idx = int(row_id) - 1
|
| 1259 |
+
if idx < 0 or idx >= len(df):
|
| 1260 |
+
raise gr.Error("Row out of range.")
|
| 1261 |
+
df.at[idx, "Request"] = clean_text(req)
|
| 1262 |
+
df.at[idx, "Information Extracted"] = clean_text(sme)
|
| 1263 |
+
df.at[idx, "Design"] = clean_text(design)
|
| 1264 |
+
bundle.dataset = df
|
| 1265 |
+
store.save_bundle(bundle)
|
| 1266 |
+
engine.reload()
|
| 1267 |
+
return f"✓ Row {row_id} updated.", get_dataset_preview(), get_note_preview(), gr.Dropdown(choices=get_row_choices(), value=row_id), get_downloadable_path(), status_html()
|
| 1268 |
+
|
| 1269 |
+
|
| 1270 |
+
def append_sme_to_row(row_id, sme_text):
|
| 1271 |
+
if row_id is None:
|
| 1272 |
+
raise gr.Error("Select a row.")
|
| 1273 |
+
sme_text = clean_text(sme_text)
|
| 1274 |
+
if not sme_text:
|
| 1275 |
+
raise gr.Error("Enter SME knowledge.")
|
| 1276 |
+
with DATA_LOCK:
|
| 1277 |
+
bundle = store.load_bundle()
|
| 1278 |
+
df = bundle.dataset.copy().reset_index(drop=True)
|
| 1279 |
+
idx = int(row_id) - 1
|
| 1280 |
+
if idx < 0 or idx >= len(df):
|
| 1281 |
+
raise gr.Error("Row out of range.")
|
| 1282 |
+
existing = clean_text(df.at[idx, "Information Extracted"])
|
| 1283 |
+
df.at[idx, "Information Extracted"] = f"{existing}\n{sme_text}".strip() if existing else sme_text
|
| 1284 |
+
bundle.dataset = df
|
| 1285 |
+
store.save_bundle(bundle)
|
| 1286 |
+
engine.reload()
|
| 1287 |
+
return f"✓ SME appended to row {row_id}.", get_dataset_preview(), get_note_preview(), gr.Dropdown(choices=get_row_choices(), value=row_id), get_downloadable_path(), status_html()
|
| 1288 |
+
|
| 1289 |
+
|
| 1290 |
+
def replace_dataset(uploaded_file):
|
| 1291 |
+
if not uploaded_file:
|
| 1292 |
+
raise gr.Error("Upload a .xlsx file first.")
|
| 1293 |
+
with DATA_LOCK:
|
| 1294 |
+
store.replace_from_upload(uploaded_file)
|
| 1295 |
+
engine.reload()
|
| 1296 |
+
return "✓ Workbook replaced.", get_dataset_preview(), get_note_preview(), gr.Dropdown(choices=get_row_choices(), value=None), get_downloadable_path(), status_html()
|
| 1297 |
+
|
| 1298 |
+
|
| 1299 |
+
def export_training_assets():
|
| 1300 |
+
engine.reload()
|
| 1301 |
+
df = engine.dataset.copy()
|
| 1302 |
+
csv_path = EXPORT_DIR / "quote_request_training.csv"
|
| 1303 |
+
jsonl_path = EXPORT_DIR / "quote_request_training.jsonl"
|
| 1304 |
+
df.to_csv(csv_path, index=False)
|
| 1305 |
+
with open(jsonl_path, "w", encoding="utf-8") as f:
|
| 1306 |
+
for _, row in df.iterrows():
|
| 1307 |
+
f.write(json.dumps({"request": clean_text(row.get("Request", "")), "sme_knowledge": clean_text(row.get("Information Extracted", "")), "design": clean_text(row.get("Design", ""))}, ensure_ascii=False) + "\n")
|
| 1308 |
+
return f"✓ Exported {len(df)} rows.", str(csv_path), str(jsonl_path)
|
| 1309 |
+
|
| 1310 |
+
|
| 1311 |
+
def admin_login(password):
|
| 1312 |
+
if password == ADMIN_PASSWORD:
|
| 1313 |
+
return gr.update(visible=False), gr.update(visible=True), "✓ Access granted."
|
| 1314 |
+
return gr.update(visible=True), gr.update(visible=False), "✗ Invalid password."
|
| 1315 |
+
|
| 1316 |
+
|
| 1317 |
+
def set_api_key_session(key, override_state):
|
| 1318 |
+
key = key.strip()
|
| 1319 |
+
if not key:
|
| 1320 |
+
return override_state, '<div class="forge-alert error">⚠ Enter an API key.</div>'
|
| 1321 |
+
client = _get_anthropic_client(key)
|
| 1322 |
+
if client:
|
| 1323 |
+
return key, '<div class="forge-alert success">✓ API key accepted — LLM backend active.</div>'
|
| 1324 |
+
return override_state, '<div class="forge-alert error">✗ Key rejected or Anthropic SDK not available.</div>'
|
| 1325 |
+
|
| 1326 |
+
|
| 1327 |
+
def refresh_all():
|
| 1328 |
+
return get_dataset_preview(), get_note_preview(), gr.Dropdown(choices=get_row_choices(), value=None), get_downloadable_path(), status_html()
|
| 1329 |
+
|
| 1330 |
+
|
| 1331 |
+
# ============================================================
|
| 1332 |
+
# UI
|
| 1333 |
+
# ============================================================
|
| 1334 |
+
with gr.Blocks(title=APP_TITLE, css=CUSTOM_CSS, theme=gr.themes.Base()) as demo:
|
| 1335 |
+
|
| 1336 |
+
api_key_state = gr.State("")
|
| 1337 |
+
|
| 1338 |
+
# ── Header ──
|
| 1339 |
+
gr.HTML(f"""
|
| 1340 |
+
<div class="forge-header">
|
| 1341 |
+
<div class="forge-logo">
|
| 1342 |
+
<span class="forge-logo-primary">{APP_TITLE}</span>
|
| 1343 |
+
<span class="forge-logo-sub">{APP_SUBTITLE}</span>
|
| 1344 |
+
</div>
|
| 1345 |
+
<div style="display:flex;align-items:center;gap:1rem;">
|
| 1346 |
+
<span class="forge-badge">MVP v2</span>
|
| 1347 |
+
<span class="forge-badge" id="hdr-backend">{"LLM ACTIVE" if api_key_active() else "SML MODE"}</span>
|
| 1348 |
+
</div>
|
| 1349 |
+
</div>
|
| 1350 |
+
""")
|
| 1351 |
+
|
| 1352 |
+
with gr.Tabs(elem_classes="main-tabs"):
|
| 1353 |
+
|
| 1354 |
+
# ════════════════════════════════════════════════════
|
| 1355 |
+
# TAB 1 · SUBMIT A REQUEST (public intake)
|
| 1356 |
+
# ════════════════════════════════════════════════════
|
| 1357 |
+
with gr.Tab("REQUEST INTAKE"):
|
| 1358 |
+
gr.HTML("""
|
| 1359 |
+
<div class="forge-hero">
|
| 1360 |
+
<div>
|
| 1361 |
+
<div class="forge-section-label">Quote Intelligence</div>
|
| 1362 |
+
<div class="forge-section-title">SUBMIT YOUR<br>QUOTE REQUEST</div>
|
| 1363 |
+
<div class="forge-section-desc">
|
| 1364 |
+
Paste your customer request below. Our engine — powered by Claude LLM or our local SML inference model — will extract application details, identify hazards, and generate quote-ready design guidance in seconds.
|
| 1365 |
+
</div>
|
| 1366 |
+
</div>
|
| 1367 |
+
<div class="forge-hero-visual">
|
| 1368 |
+
<div class="forge-metric-row">
|
| 1369 |
+
<div class="forge-metric"><div class="forge-metric-val">A→C</div><div class="forge-metric-key">Request to Design</div></div>
|
| 1370 |
+
<div class="forge-metric"><div class="forge-metric-val">SML</div><div class="forge-metric-key">Offline Fallback</div></div>
|
| 1371 |
+
<div class="forge-metric"><div class="forge-metric-val">∞</div><div class="forge-metric-key">Training Loop</div></div>
|
| 1372 |
+
</div>
|
| 1373 |
+
<div class="forge-card" style="font-family:var(--forge-mono);font-size:0.78rem;color:var(--forge-muted);line-height:1.9;">
|
| 1374 |
+
<div style="color:var(--forge-amber);margin-bottom:0.5rem;font-size:0.65rem;letter-spacing:0.2em;">HOW IT WORKS</div>
|
| 1375 |
+
01 · Paste customer request<br>
|
| 1376 |
+
02 · Add optional SME context<br>
|
| 1377 |
+
03 · Engine retrieves similar cases<br>
|
| 1378 |
+
04 · LLM or SML generates guidance<br>
|
| 1379 |
+
05 · Review → save to training set
|
| 1380 |
+
</div>
|
| 1381 |
+
</div>
|
| 1382 |
+
</div>
|
| 1383 |
+
<div style="max-width:1400px;margin:0 auto;padding:0 2rem;">
|
| 1384 |
+
""")
|
| 1385 |
+
|
| 1386 |
+
# API key banner (shown when no key in env)
|
| 1387 |
+
api_key_banner_visible = not api_key_active()
|
| 1388 |
+
with gr.Group(visible=api_key_banner_visible, elem_id="api-key-section") as api_key_section:
|
| 1389 |
+
gr.HTML("""
|
| 1390 |
+
<div class="forge-alert warn" style="margin-bottom:0.75rem;">
|
| 1391 |
+
⚠ <strong>No Anthropic API key detected.</strong> Running in SML (local inference) mode.
|
| 1392 |
+
Enter a key below to enable Claude LLM backend. Or continue — SML works offline.
|
| 1393 |
+
</div>
|
| 1394 |
+
""")
|
| 1395 |
+
with gr.Row():
|
| 1396 |
+
api_key_input = gr.Textbox(
|
| 1397 |
+
label="Anthropic API Key (session only — not stored)",
|
| 1398 |
+
placeholder="sk-ant-...",
|
| 1399 |
+
type="password",
|
| 1400 |
+
scale=4,
|
| 1401 |
+
)
|
| 1402 |
+
api_key_btn = gr.Button("Activate LLM", variant="primary", scale=1)
|
| 1403 |
+
api_key_status = gr.HTML("")
|
| 1404 |
+
|
| 1405 |
+
with gr.Row():
|
| 1406 |
+
with gr.Column(scale=3):
|
| 1407 |
+
request_input = gr.Textbox(
|
| 1408 |
+
label="Customer Request",
|
| 1409 |
+
lines=7,
|
| 1410 |
+
placeholder="e.g. 15000 CFM pharmaceutical powder, corrosive dust, need fan and collector recommendation",
|
| 1411 |
+
)
|
| 1412 |
+
sme_input = gr.Textbox(
|
| 1413 |
+
label="SME Knowledge / Domain Notes (optional)",
|
| 1414 |
+
lines=5,
|
| 1415 |
+
placeholder="Add expert context that should influence design guidance...",
|
| 1416 |
+
)
|
| 1417 |
+
with gr.Row():
|
| 1418 |
+
top_k_input = gr.Slider(1, 8, value=4, step=1, label="Historical Examples")
|
| 1419 |
+
temperature_input = gr.Slider(0.0, 1.0, value=0.1, step=0.05, label="Temperature (LLM only)")
|
| 1420 |
+
with gr.Row():
|
| 1421 |
+
generate_btn = gr.Button("Generate Quote Guidance", variant="primary")
|
| 1422 |
+
save_generated_btn = gr.Button("Save as Training Row", variant="secondary")
|
| 1423 |
+
|
| 1424 |
+
save_generated_status = gr.HTML("")
|
| 1425 |
+
|
| 1426 |
+
with gr.Column(scale=3):
|
| 1427 |
+
backend_indicator = gr.HTML(f'<div class="forge-alert info" style="margin-bottom:1rem;">Select backend and submit request to begin.</div>')
|
| 1428 |
+
info_output = gr.Textbox(label="Information Extracted (Col B)", lines=7)
|
| 1429 |
+
design_output = gr.Textbox(label="Design / Quote Guidance (Col C)", lines=8)
|
| 1430 |
+
|
| 1431 |
+
with gr.Row():
|
| 1432 |
+
with gr.Column():
|
| 1433 |
+
quote_inputs_output = gr.Textbox(label="Structured Quote Inputs", lines=14)
|
| 1434 |
+
with gr.Column():
|
| 1435 |
+
assumptions_output = gr.Textbox(label="Assumptions & Unknowns", lines=10)
|
| 1436 |
+
|
| 1437 |
+
retrieved_output = gr.Dataframe(
|
| 1438 |
+
headers=["Request", "Information Extracted", "Design", "Similarity"],
|
| 1439 |
+
datatype=["str", "str", "str", "number"],
|
| 1440 |
+
label="Retrieved Historical Examples",
|
| 1441 |
+
interactive=False,
|
| 1442 |
+
wrap=True,
|
| 1443 |
+
)
|
| 1444 |
+
raw_output = gr.Textbox(label="Raw Model Output (debug)", lines=5, visible=False)
|
| 1445 |
+
gr.HTML("</div>") # close forge-page
|
| 1446 |
+
|
| 1447 |
+
# ════════════════════════════════════════════════════
|
| 1448 |
+
# TAB 2 · ADMIN TERMINAL
|
| 1449 |
+
# ════════════════════════════════════════════════════
|
| 1450 |
+
with gr.Tab("ADMIN TERMINAL"):
|
| 1451 |
+
|
| 1452 |
+
# ── Login gate ──
|
| 1453 |
+
with gr.Group(visible=True) as admin_login_panel:
|
| 1454 |
+
gr.HTML("""
|
| 1455 |
+
<div class="forge-page" style="max-width:480px;">
|
| 1456 |
+
<div class="forge-terminal-header">
|
| 1457 |
+
<div class="terminal-dot" style="background:#ef4444"></div>
|
| 1458 |
+
<div class="terminal-dot" style="background:#f59e0b"></div>
|
| 1459 |
+
<div class="terminal-dot" style="background:#22c55e"></div>
|
| 1460 |
+
<span style="font-family:var(--forge-mono);font-size:0.7rem;color:var(--forge-muted);margin-left:0.5rem;">QUOTEFORGE ADMIN TERMINAL — RESTRICTED ACCESS</span>
|
| 1461 |
+
</div>
|
| 1462 |
+
<div class="forge-terminal-body">
|
| 1463 |
+
<span style="color:var(--forge-amber);">QuoteForge</span> <span style="color:var(--forge-muted);">v2.0</span> — Authentication required<br>
|
| 1464 |
+
<span style="color:var(--forge-muted);">Set ADMIN_PASSWORD env var to change default (admin1234)</span>
|
| 1465 |
+
</div>
|
| 1466 |
+
</div>
|
| 1467 |
+
""")
|
| 1468 |
+
with gr.Row(elem_classes="forge-page"):
|
| 1469 |
+
admin_pw_input = gr.Textbox(label="Admin Password", type="password", placeholder="Enter password...", scale=3)
|
| 1470 |
+
admin_login_btn = gr.Button("Authenticate", variant="primary", scale=1)
|
| 1471 |
+
admin_login_status = gr.HTML("")
|
| 1472 |
+
|
| 1473 |
+
# ── Admin workspace (hidden until auth) ──
|
| 1474 |
+
with gr.Group(visible=False) as admin_workspace:
|
| 1475 |
+
gr.HTML("""
|
| 1476 |
+
<div class="forge-page">
|
| 1477 |
+
<div class="forge-terminal-header">
|
| 1478 |
+
<div class="terminal-dot" style="background:#ef4444"></div>
|
| 1479 |
+
<div class="terminal-dot" style="background:#f59e0b"></div>
|
| 1480 |
+
<div class="terminal-dot" style="background:#22c55e"></div>
|
| 1481 |
+
<span style="font-family:var(--forge-mono);font-size:0.7rem;color:var(--forge-muted);margin-left:0.5rem;">QUOTEFORGE ADMIN TERMINAL — SESSION ACTIVE</span>
|
| 1482 |
+
</div>
|
| 1483 |
+
""")
|
| 1484 |
+
|
| 1485 |
+
with gr.Row(elem_classes="forge-page"):
|
| 1486 |
+
admin_status_html = gr.HTML(status_html())
|
| 1487 |
+
refresh_admin_btn = gr.Button("↻ Refresh", variant="secondary")
|
| 1488 |
+
|
| 1489 |
+
gr.HTML('<div class="forge-page"><div class="forge-section-label">Dataset Management</div></div>')
|
| 1490 |
+
|
| 1491 |
+
with gr.Tabs(elem_classes="forge-page"):
|
| 1492 |
+
|
| 1493 |
+
with gr.Tab("Add Request Only"):
|
| 1494 |
+
add_request_box = gr.Textbox(label="New Request (Column A)", lines=6)
|
| 1495 |
+
add_request_btn = gr.Button("Append to Column A", variant="primary")
|
| 1496 |
+
add_request_status = gr.HTML("")
|
| 1497 |
+
|
| 1498 |
+
with gr.Tab("Add Full A/B/C Row"):
|
| 1499 |
+
full_request_box = gr.Textbox(label="Request (A)", lines=4)
|
| 1500 |
+
full_sme_box = gr.Textbox(label="Information Extracted (B)", lines=5)
|
| 1501 |
+
full_design_box = gr.Textbox(label="Design Guidance (C)", lines=6)
|
| 1502 |
+
add_full_btn = gr.Button("Append Full Row", variant="primary")
|
| 1503 |
+
add_full_status = gr.HTML("")
|
| 1504 |
+
|
| 1505 |
+
with gr.Tab("Append SME to Row"):
|
| 1506 |
+
row_selector = gr.Dropdown(choices=get_row_choices(), label="Select Row", value=None)
|
| 1507 |
+
append_sme_box = gr.Textbox(label="SME Knowledge to Append", lines=6)
|
| 1508 |
+
append_sme_btn = gr.Button("Append to Selected Row", variant="primary")
|
| 1509 |
+
append_sme_status = gr.HTML("")
|
| 1510 |
+
|
| 1511 |
+
with gr.Tab("Edit Row"):
|
| 1512 |
+
load_row_btn = gr.Button("Load Selected Row", variant="secondary")
|
| 1513 |
+
edit_request_box = gr.Textbox(label="Request (A)", lines=4)
|
| 1514 |
+
edit_sme_box = gr.Textbox(label="Information Extracted (B)", lines=5)
|
| 1515 |
+
edit_design_box = gr.Textbox(label="Design Guidance (C)", lines=6)
|
| 1516 |
+
update_row_btn = gr.Button("Save Changes", variant="primary")
|
| 1517 |
+
update_row_status = gr.HTML("")
|
| 1518 |
+
|
| 1519 |
+
with gr.Tab("Global SME Notes"):
|
| 1520 |
+
global_note_box = gr.Textbox(label="New Global SME Note", lines=4)
|
| 1521 |
+
add_note_btn = gr.Button("Save Note", variant="primary")
|
| 1522 |
+
add_note_status = gr.HTML("")
|
| 1523 |
+
|
| 1524 |
+
gr.HTML('<div class="forge-page"><div class="forge-section-label">Dataset Viewer & Export</div></div>')
|
| 1525 |
+
|
| 1526 |
+
with gr.Row(elem_classes="forge-page"):
|
| 1527 |
+
with gr.Column(scale=3):
|
| 1528 |
+
dataset_preview = gr.Dataframe(
|
| 1529 |
+
value=get_dataset_preview(),
|
| 1530 |
+
headers=["row_id"] + HEADERS,
|
| 1531 |
+
datatype=["number", "str", "str", "str"],
|
| 1532 |
+
label="Training Dataset",
|
| 1533 |
+
interactive=False,
|
| 1534 |
+
wrap=True,
|
| 1535 |
+
)
|
| 1536 |
+
notes_preview = gr.Dataframe(
|
| 1537 |
+
value=get_note_preview(),
|
| 1538 |
+
headers=["note_id", "SME Note"],
|
| 1539 |
+
datatype=["number", "str"],
|
| 1540 |
+
label="Global SME Notes",
|
| 1541 |
+
interactive=False,
|
| 1542 |
+
wrap=True,
|
| 1543 |
+
)
|
| 1544 |
+
with gr.Column(scale=1):
|
| 1545 |
+
upload_file = gr.File(label="Upload Replacement Workbook (.xlsx)", file_types=[".xlsx"], type="filepath")
|
| 1546 |
+
replace_btn = gr.Button("Replace Dataset", variant="secondary")
|
| 1547 |
+
replace_status = gr.HTML("")
|
| 1548 |
+
dataset_download = gr.File(label="Download Current Workbook", value=get_downloadable_path(), interactive=False)
|
| 1549 |
+
export_btn = gr.Button("Export ML Assets", variant="primary")
|
| 1550 |
+
export_status = gr.HTML("")
|
| 1551 |
+
export_csv_file = gr.File(label="CSV Export", interactive=False)
|
| 1552 |
+
export_jsonl_file = gr.File(label="JSONL Export", interactive=False)
|
| 1553 |
+
|
| 1554 |
+
gr.HTML("</div>") # close terminal body div
|
| 1555 |
+
|
| 1556 |
+
# ════════════════════════════════════════════════════
|
| 1557 |
+
# Event wiring
|
| 1558 |
+
# ════════════════════════════════════════════════════
|
| 1559 |
+
|
| 1560 |
+
# API key activation
|
| 1561 |
+
api_key_btn.click(
|
| 1562 |
+
fn=set_api_key_session,
|
| 1563 |
+
inputs=[api_key_input, api_key_state],
|
| 1564 |
+
outputs=[api_key_state, api_key_status],
|
| 1565 |
+
)
|
| 1566 |
+
|
| 1567 |
+
# Generate
|
| 1568 |
+
generate_btn.click(
|
| 1569 |
+
fn=generate_quote_action,
|
| 1570 |
+
inputs=[request_input, sme_input, top_k_input, temperature_input, api_key_state],
|
| 1571 |
+
outputs=[info_output, design_output, quote_inputs_output, assumptions_output, retrieved_output, raw_output, backend_indicator],
|
| 1572 |
+
)
|
| 1573 |
+
|
| 1574 |
+
# Save generated row
|
| 1575 |
+
save_generated_btn.click(
|
| 1576 |
+
fn=save_generated_row,
|
| 1577 |
+
inputs=[request_input, info_output, design_output],
|
| 1578 |
+
outputs=[save_generated_status, dataset_preview, notes_preview, row_selector, dataset_download, admin_status_html],
|
| 1579 |
+
)
|
| 1580 |
+
|
| 1581 |
+
# Admin login
|
| 1582 |
+
admin_login_btn.click(
|
| 1583 |
+
fn=admin_login,
|
| 1584 |
+
inputs=[admin_pw_input],
|
| 1585 |
+
outputs=[admin_login_panel, admin_workspace, admin_login_status],
|
| 1586 |
+
)
|
| 1587 |
+
|
| 1588 |
+
# Admin actions
|
| 1589 |
+
add_request_btn.click(
|
| 1590 |
+
fn=add_request_only,
|
| 1591 |
+
inputs=[add_request_box],
|
| 1592 |
+
outputs=[add_request_status, dataset_preview, notes_preview, row_selector, dataset_download, admin_status_html],
|
| 1593 |
+
)
|
| 1594 |
+
|
| 1595 |
+
add_full_btn.click(
|
| 1596 |
+
fn=add_full_training_row,
|
| 1597 |
+
inputs=[full_request_box, full_sme_box, full_design_box],
|
| 1598 |
+
outputs=[add_full_status, dataset_preview, notes_preview, row_selector, dataset_download, admin_status_html],
|
| 1599 |
+
)
|
| 1600 |
+
|
| 1601 |
+
append_sme_btn.click(
|
| 1602 |
+
fn=append_sme_to_row,
|
| 1603 |
+
inputs=[row_selector, append_sme_box],
|
| 1604 |
+
outputs=[append_sme_status, dataset_preview, notes_preview, row_selector, dataset_download, admin_status_html],
|
| 1605 |
+
)
|
| 1606 |
+
|
| 1607 |
+
load_row_btn.click(
|
| 1608 |
+
fn=load_row_for_edit,
|
| 1609 |
+
inputs=[row_selector],
|
| 1610 |
+
outputs=[edit_request_box, edit_sme_box, edit_design_box],
|
| 1611 |
+
)
|
| 1612 |
+
|
| 1613 |
+
update_row_btn.click(
|
| 1614 |
+
fn=update_row_fields,
|
| 1615 |
+
inputs=[row_selector, edit_request_box, edit_sme_box, edit_design_box],
|
| 1616 |
+
outputs=[update_row_status, dataset_preview, notes_preview, row_selector, dataset_download, admin_status_html],
|
| 1617 |
+
)
|
| 1618 |
+
|
| 1619 |
+
add_note_btn.click(
|
| 1620 |
+
fn=add_global_sme_note,
|
| 1621 |
+
inputs=[global_note_box],
|
| 1622 |
+
outputs=[add_note_status, dataset_preview, notes_preview, row_selector, dataset_download, admin_status_html],
|
| 1623 |
+
)
|
| 1624 |
+
|
| 1625 |
+
replace_btn.click(
|
| 1626 |
+
fn=replace_dataset,
|
| 1627 |
+
inputs=[upload_file],
|
| 1628 |
+
outputs=[replace_status, dataset_preview, notes_preview, row_selector, dataset_download, admin_status_html],
|
| 1629 |
+
)
|
| 1630 |
+
|
| 1631 |
+
export_btn.click(
|
| 1632 |
+
fn=export_training_assets,
|
| 1633 |
+
inputs=[],
|
| 1634 |
+
outputs=[export_status, export_csv_file, export_jsonl_file],
|
| 1635 |
+
)
|
| 1636 |
+
|
| 1637 |
+
refresh_admin_btn.click(
|
| 1638 |
+
fn=refresh_all,
|
| 1639 |
+
inputs=[],
|
| 1640 |
+
outputs=[dataset_preview, notes_preview, row_selector, dataset_download, admin_status_html],
|
| 1641 |
+
)
|
| 1642 |
+
|
| 1643 |
+
|
| 1644 |
+
def main() -> None:
|
| 1645 |
+
demo.queue(default_concurrency_limit=8).launch(
|
| 1646 |
+
server_name="0.0.0.0",
|
| 1647 |
+
server_port=int(os.getenv("PORT", "7860")),
|
| 1648 |
+
)
|
| 1649 |
+
|
| 1650 |
+
|
| 1651 |
+
if __name__ == "__main__":
|
| 1652 |
+
main()
|
requirements (4).txt
ADDED
|
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
gradio>=6.0,<7
|
| 2 |
+
pandas>=2.2,<3
|
| 3 |
+
openpyxl>=3.1,<4
|
| 4 |
+
scikit-learn>=1.5,<2
|
| 5 |
+
anthropic>=0.49,<1
|
| 6 |
+
numpy>=1.26,<3
|