Spaces:
Runtime error
Runtime error
File size: 25,057 Bytes
00bf920 e4c59f6 00bf920 e4c59f6 00bf920 e4c59f6 00bf920 |
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 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['id', 'NAME', 'host id', 'host name', 'neighbourhood group',\n",
" 'neighbourhood', 'lat', 'long', 'country', 'country code',\n",
" 'instant_bookable', 'cancellation_policy', 'room type',\n",
" 'Construction year', 'price', 'service fee', 'minimum nights',\n",
" 'number of reviews', 'last review', 'reviews per month',\n",
" 'review rate number', 'calculated host listings count',\n",
" 'availability 365', 'house_rules', 'license'],\n",
" dtype='object')"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\ardit\\AppData\\Local\\Temp\\ipykernel_25752\\2207992772.py:4: DtypeWarning: Columns (25) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" df = pd.read_csv('Airbnb_Open_Data.csv')\n"
]
}
],
"source": [
"import pandas as pd\n",
"import random\n",
"\n",
"df = pd.read_csv('Airbnb_Open_Data.csv')\n",
"df = df.drop('host_identity_verified', axis=1)\n",
"df['description'] = df['NAME']\n",
"df['price'] = df['price'].dropna().apply(lambda x : int(x[1:].strip().replace(',', '')))\n",
"df['sq. meters'] = df['price'].apply(lambda x : random.choices([25, 40, 45, 55, 60, 70], weights=[5, 5, 4, 3, 2, 1])[0])\n",
"df = df[['price', 'sq. meters', 'description', 'neighbourhood group', 'host name', 'cancellation_policy', 'house_rules']]\n",
"df = df[df['house_rules']!='#NAME?'].dropna().reset_index(drop=True)\n",
"df = df[0:10000]"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 10000/10000 [17:37<00:00, 9.45it/s]\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>price</th>\n",
" <th>sq. meters</th>\n",
" <th>description</th>\n",
" <th>neighbourhood group</th>\n",
" <th>host name</th>\n",
" <th>cancellation_policy</th>\n",
" <th>house_rules</th>\n",
" <th>text_vector_</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>966.0</td>\n",
" <td>25</td>\n",
" <td>Clean & quiet apt home by the park</td>\n",
" <td>Brooklyn</td>\n",
" <td>Madaline</td>\n",
" <td>strict</td>\n",
" <td>Clean up and treat the home the way you'd like...</td>\n",
" <td>[-0.047521110624074936, 0.03044620156288147, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>142.0</td>\n",
" <td>25</td>\n",
" <td>Skylit Midtown Castle</td>\n",
" <td>Manhattan</td>\n",
" <td>Jenna</td>\n",
" <td>moderate</td>\n",
" <td>Pet friendly but please confirm with me if the...</td>\n",
" <td>[-0.04690079391002655, 0.061329323798418045, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>620.0</td>\n",
" <td>45</td>\n",
" <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n",
" <td>Manhattan</td>\n",
" <td>Elise</td>\n",
" <td>flexible</td>\n",
" <td>I encourage you to use my kitchen, cooking and...</td>\n",
" <td>[0.00039011164335533977, 0.018310122191905975,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>204.0</td>\n",
" <td>55</td>\n",
" <td>Entire Apt: Spacious Studio/Loft by central park</td>\n",
" <td>Manhattan</td>\n",
" <td>Lyndon</td>\n",
" <td>moderate</td>\n",
" <td>Please no smoking in the house, porch or on th...</td>\n",
" <td>[-0.04602213576436043, 0.015605293214321136, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>577.0</td>\n",
" <td>25</td>\n",
" <td>Large Cozy 1 BR Apartment In Midtown East</td>\n",
" <td>Manhattan</td>\n",
" <td>Michelle</td>\n",
" <td>flexible</td>\n",
" <td>No smoking, please, and no drugs.</td>\n",
" <td>[-0.04859349876642227, -0.01263828668743372, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9995</th>\n",
" <td>745.0</td>\n",
" <td>60</td>\n",
" <td>Upper West Side 1BR next to subway/Central Park</td>\n",
" <td>Manhattan</td>\n",
" <td>Doreen</td>\n",
" <td>strict</td>\n",
" <td>Our Herbivorian House manual with detailed rul...</td>\n",
" <td>[-0.0346745029091835, -0.005859952419996262, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9996</th>\n",
" <td>1135.0</td>\n",
" <td>45</td>\n",
" <td>Modern and Bright Studio Apt in Williamsburg</td>\n",
" <td>Brooklyn</td>\n",
" <td>Shannon</td>\n",
" <td>strict</td>\n",
" <td>No smoking please!</td>\n",
" <td>[-0.016586357727646828, 0.020517650991678238, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9997</th>\n",
" <td>59.0</td>\n",
" <td>45</td>\n",
" <td>Holiday in Trendy Williamsburg Apt!</td>\n",
" <td>Brooklyn</td>\n",
" <td>Peter</td>\n",
" <td>strict</td>\n",
" <td>We suggest you use email or texting contact us...</td>\n",
" <td>[-0.05095353722572327, 0.08510775864124298, -0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9998</th>\n",
" <td>1055.0</td>\n",
" <td>25</td>\n",
" <td>Greenwich Village| Private Queen room</td>\n",
" <td>Manhattan</td>\n",
" <td>Kelly</td>\n",
" <td>flexible</td>\n",
" <td>Please treat this house as if it is your own. ...</td>\n",
" <td>[0.00017118529649451375, 0.010939894244074821,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9999</th>\n",
" <td>285.0</td>\n",
" <td>25</td>\n",
" <td>Comfortable bedroom in spacious apt</td>\n",
" <td>Brooklyn</td>\n",
" <td>Arthur</td>\n",
" <td>strict</td>\n",
" <td>Please, No smoking and no pets. We do require ...</td>\n",
" <td>[-0.01795135624706745, -0.029596544802188873, ...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10000 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" price sq. meters description \\\n",
"0 966.0 25 Clean & quiet apt home by the park \n",
"1 142.0 25 Skylit Midtown Castle \n",
"2 620.0 45 THE VILLAGE OF HARLEM....NEW YORK ! \n",
"3 204.0 55 Entire Apt: Spacious Studio/Loft by central park \n",
"4 577.0 25 Large Cozy 1 BR Apartment In Midtown East \n",
"... ... ... ... \n",
"9995 745.0 60 Upper West Side 1BR next to subway/Central Park \n",
"9996 1135.0 45 Modern and Bright Studio Apt in Williamsburg \n",
"9997 59.0 45 Holiday in Trendy Williamsburg Apt! \n",
"9998 1055.0 25 Greenwich Village| Private Queen room \n",
"9999 285.0 25 Comfortable bedroom in spacious apt \n",
"\n",
" neighbourhood group host name cancellation_policy \\\n",
"0 Brooklyn Madaline strict \n",
"1 Manhattan Jenna moderate \n",
"2 Manhattan Elise flexible \n",
"3 Manhattan Lyndon moderate \n",
"4 Manhattan Michelle flexible \n",
"... ... ... ... \n",
"9995 Manhattan Doreen strict \n",
"9996 Brooklyn Shannon strict \n",
"9997 Brooklyn Peter strict \n",
"9998 Manhattan Kelly flexible \n",
"9999 Brooklyn Arthur strict \n",
"\n",
" house_rules \\\n",
"0 Clean up and treat the home the way you'd like... \n",
"1 Pet friendly but please confirm with me if the... \n",
"2 I encourage you to use my kitchen, cooking and... \n",
"3 Please no smoking in the house, porch or on th... \n",
"4 No smoking, please, and no drugs. \n",
"... ... \n",
"9995 Our Herbivorian House manual with detailed rul... \n",
"9996 No smoking please! \n",
"9997 We suggest you use email or texting contact us... \n",
"9998 Please treat this house as if it is your own. ... \n",
"9999 Please, No smoking and no pets. We do require ... \n",
"\n",
" text_vector_ \n",
"0 [-0.047521110624074936, 0.03044620156288147, 0... \n",
"1 [-0.04690079391002655, 0.061329323798418045, 0... \n",
"2 [0.00039011164335533977, 0.018310122191905975,... \n",
"3 [-0.04602213576436043, 0.015605293214321136, 0... \n",
"4 [-0.04859349876642227, -0.01263828668743372, 0... \n",
"... ... \n",
"9995 [-0.0346745029091835, -0.005859952419996262, 0... \n",
"9996 [-0.016586357727646828, 0.020517650991678238, ... \n",
"9997 [-0.05095353722572327, 0.08510775864124298, -0... \n",
"9998 [0.00017118529649451375, 0.010939894244074821,... \n",
"9999 [-0.01795135624706745, -0.029596544802188873, ... \n",
"\n",
"[10000 rows x 8 columns]"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"from tqdm import tqdm\n",
"from sentence_transformers import SentenceTransformer\n",
"tqdm.pandas()\n",
"\n",
"model = SentenceTransformer('all-mpnet-base-v2') #all-MiniLM-L6-v2 #all-mpnet-base-v2\n",
"\n",
"#encode df version: for small dataset only\n",
"df['text_vector_'] = df['description'].progress_apply(lambda x : model.encode(x).tolist())\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_parquet('df_encoded.parquet')\n",
"df['neighbourhood group'][0:2500] = df['neighbourhood group'][0:2500].apply(lambda x : 'Manhattan')\n",
"df['neighbourhood group'][2500:5000] = df['neighbourhood group'][0:2500].apply(lambda x : 'Brooklyn')\n",
"df['neighbourhood group'][5000:7500] = df['neighbourhood group'][0:2500].apply(lambda x : 'Queens')\n",
"df['neighbourhood group'][7500:] = df['neighbourhood group'][0:2500].apply(lambda x : 'Bronx')\n",
"df['location'] = df['neighbourhood group']\n",
"df = df[['price', 'sq. meters', 'description', 'location', 'host name', 'cancellation_policy', 'house_rules', 'text_vector_']]\n",
"df = df.reset_index(drop=True)\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 145,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.neighbors import NearestNeighbors\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"from sentence_transformers import SentenceTransformer\n",
"\n",
"# df = df.read_parquet('df_encoded.parquet')\n",
"model = SentenceTransformer('all-mpnet-base-v2') #all-MiniLM-L6-v2 #all-mpnet-base-v2\n",
"\n",
"#prepare model\n",
"# nbrs = NearestNeighbors(n_neighbors=3, algorithm='ball_tree').fit(df['text_vector_'].values.tolist())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import gradio as gr\n",
"import statistics\n",
"\n",
"def closest_number(x):\n",
" closest_numbers = [10, 20, 30, 40]\n",
" closest_number = closest_numbers[0]\n",
" min_distance = abs(x - closest_number)\n",
" for number in closest_numbers[1:]:\n",
" distance = abs(x - number)\n",
" if distance < min_distance:\n",
" closest_number = number\n",
" min_distance = distance\n",
" return closest_number\n",
"\n",
"def search(df, query):\n",
" product = model.encode(query).tolist()\n",
" # product = df.iloc[0]['text_vector_'] #use one of the products as sample\n",
"\n",
" nbrs = NearestNeighbors(n_neighbors=3, algorithm='ball_tree').fit(df['text_vector_'].values.tolist())\n",
" distances, indices = nbrs.kneighbors([product]) #input the vector of the reference object\n",
"\n",
" #print out the description of every recommended product\n",
" df_search = df.iloc[list(indices)[0]].drop(['text_vector_'], axis=1) #.sort_values('avgFeedbackScore', ascending=False)\n",
"\n",
" return df_search.sort_values('price', ascending=False)\n",
"\n",
"def filter_df(df, column_name, filter_type, filter_value):\n",
" if filter_type == '==':\n",
" df_filtered = df[df[column_name]==filter_value]\n",
" elif filter_type == '>=':\n",
" df_filtered = df[df[column_name]>=filter_value]\n",
" elif filter_type == '<=':\n",
" df_filtered = df[df[column_name]<=filter_value]\n",
" return df_filtered\n",
"\n",
"history = list()\n",
"def predict(input1, input2, input3, input4):\n",
" history.append([input1, input2, input3, input4])\n",
"\n",
" print(history)\n",
" df_location = filter_df(df, 'location', '==', input3)\n",
" df_size = filter_df(df_location, 'sq. meters', '==', input2)\n",
" df_price = filter_df(df_size, 'price', '<=', input1)\n",
" df_result = search(df_price, input4)\n",
"\n",
" prediction = [\n",
" round(statistics.mean([x[0] for x in history])), #price\n",
" closest_number(statistics.mean([x[1] for x in history])), #square room\n",
" statistics.mode([x[2] for x in history]) #state\n",
" ]\n",
"\n",
" return df_result, prediction\n",
"\n",
"with gr.Blocks(theme=gr.themes.Soft(primary_hue='amber', secondary_hue='gray', neutral_hue='amber')) as demo:\n",
" gr.Markdown(\n",
" \"\"\"\n",
" # Airbnb Search Engine\n",
" \"\"\"\n",
" )\n",
" input1 = gr.Slider(100, 1200, value=700, step_size=100, label=\"Max Price\")\n",
" input2 = gr.Radio([25, 40, 45, 55, 60, 70], multiselect=False, label='square meters', value=45)\n",
" input3 = gr.Radio(['Manhattan', 'Brooklyn', 'Queens', 'Bronx'], multiselect=False, label='State', value='Queens')\n",
" input4 = gr.Textbox(label='Query', value='I want to take a break from work 😴!!!')\n",
"\n",
" btn = gr.Button(value=\"Search for a Room\")\n",
" output1 = gr.Dataframe()\n",
" output2 = gr.Textbox(label='prediction for the next search')\n",
" # btn.click(greet, inputs='text', outputs=['dataframe'])\n",
" btn.click(predict, [input1, input2, input3, input4], [output1, output2])\n",
"demo.launch(share=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"os.system('pip install openpyxl')\n",
"os.system('pip install sentence-transformers')\n",
"import pandas as pd\n",
"import gradio as gr\n",
"import statistics\n",
"from sklearn.neighbors import NearestNeighbors\n",
"from sentence_transformers import SentenceTransformer\n",
"\n",
"df = pd.read_parquet('df_encoded.parquet')\n",
"df['neighbourhood group'][0:2500] = df['neighbourhood group'][0:2500].apply(lambda x : 'Manhattan')\n",
"df['neighbourhood group'][2500:5000] = df['neighbourhood group'][0:2500].apply(lambda x : 'Brooklyn')\n",
"df['neighbourhood group'][5000:7500] = df['neighbourhood group'][0:2500].apply(lambda x : 'Queens')\n",
"df['neighbourhood group'][7500:] = df['neighbourhood group'][0:2500].apply(lambda x : 'Bronx')\n",
"df['location'] = df['neighbourhood group']\n",
"df = df[['price', 'sq. meters', 'description', 'location', 'host name', 'cancellation_policy', 'house_rules', 'text_vector_']]\n",
"df = df.reset_index(drop=True)\n",
"df\n",
"\n",
"model = SentenceTransformer('all-mpnet-base-v2') #all-MiniLM-L6-v2 #all-mpnet-base-v2\n",
"\n",
"#prepare model #we run it anew in the search function every time, after the initial filtering\n",
"# nbrs = NearestNeighbors(n_neighbors=3, algorithm='ball_tree').fit(df['text_vector_'].values.tolist())\n",
"\n",
"def closest_number(x):\n",
" closest_numbers = [25, 40, 45, 55, 60, 70]\n",
" closest_number = closest_numbers[0]\n",
" min_distance = abs(x - closest_number)\n",
" for number in closest_numbers[1:]:\n",
" distance = abs(x - number)\n",
" if distance < min_distance:\n",
" closest_number = number\n",
" min_distance = distance\n",
" return closest_number\n",
"\n",
"def search(df, query):\n",
" product = model.encode(query).tolist()\n",
" # product = df.iloc[0]['text_vector_'] #use one of the products as sample\n",
"\n",
" nbrs = NearestNeighbors(n_neighbors=3, algorithm='ball_tree').fit(df['text_vector_'].values.tolist())\n",
" distances, indices = nbrs.kneighbors([product]) #input the vector of the reference object\n",
"\n",
" #print out the description of every recommended product\n",
" df_search = df.iloc[list(indices)[0]].drop(['text_vector_'], axis=1) #.sort_values('avgFeedbackScore', ascending=False)\n",
"\n",
" return df_search.sort_values('price', ascending=False)\n",
"\n",
"def filter_df(df, column_name, filter_type, filter_value):\n",
" if filter_type == '==':\n",
" df_filtered = df[df[column_name]==filter_value]\n",
" elif filter_type == '>=':\n",
" df_filtered = df[df[column_name]>=filter_value]\n",
" elif filter_type == '<=':\n",
" df_filtered = df[df[column_name]<=filter_value]\n",
" return df_filtered"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def predict(history, input1, input2, input3, input4):\n",
" history.append([input1, input2, input3, input4])\n",
"\n",
" print(history)\n",
" df_location = filter_df(df, 'location', '==', input3)\n",
" df_size = filter_df(df_location, 'sq. meters', '==', input2)\n",
" df_price = filter_df(df_size, 'price', '<=', input1)\n",
" df_result = search(df_price, input4)\n",
"\n",
" prediction = [\n",
" round(statistics.mean([x[0] for x in history])), #price\n",
" closest_number(statistics.mean([x[1] for x in history])), #square meters\n",
" statistics.mode([x[2] for x in history]) #state\n",
" ]\n",
"\n",
" print(history)\n",
"\n",
" return df_result, prediction"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\ardit\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\gradio\\deprecation.py:43: UserWarning: You have unused kwarg parameters in Slider, please remove them: {'step_size': 100}\n",
" warnings.warn(\n",
"c:\\Users\\ardit\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\gradio\\deprecation.py:43: UserWarning: You have unused kwarg parameters in Radio, please remove them: {'multiselect': False}\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7863\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7863/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[700, 45, 'Brooklyn', 'I want to take a break from work 😴!!!']]\n",
"[[700, 45, 'Brooklyn', 'I want to take a break from work 😴!!!']]\n",
"[[700, 45, 'Brooklyn', 'I want to take a break from work 😴!!!'], [700, 45, 'Brooklyn', 'I want to take a break from work 😴!!!']]\n",
"[[700, 45, 'Brooklyn', 'I want to take a break from work 😴!!!'], [700, 45, 'Brooklyn', 'I want to take a break from work 😴!!!']]\n"
]
}
],
"source": [
"with gr.Blocks(theme=gr.themes.Soft(primary_hue='amber', secondary_hue='gray', neutral_hue='amber')) as demo:\n",
" history = gr.Variable(value=[]) #beginning\n",
" gr.Markdown(\n",
" \"\"\"\n",
" # Airbnb Search Engine\n",
" \"\"\"\n",
" )\n",
" input1 = gr.Slider(100, 1200, value=700, step_size=100, label=\"Max Price\")\n",
" input2 = gr.Radio([25, 40, 45, 55, 60, 70], multiselect=False, label='square meters', value=45)\n",
" input3 = gr.Radio(['Manhattan', 'Brooklyn', 'Queens', 'Bronx'], multiselect=False, label='State', value='Brooklyn')\n",
" input4 = gr.Textbox(label='Query', value='I want to take a break from work 😴!!!')\n",
"\n",
" btn = gr.Button(value=\"Search for a Room\")\n",
" output1 = gr.Dataframe()\n",
" output2 = gr.Textbox(label='prediction for the next search')\n",
" # btn.click(greet, inputs='text', outputs=['dataframe'])\n",
" btn.click(predict, [history, input1, input2, input3, input4], [output1, output2])\n",
"demo.launch(share=False)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
|