How to use crop method in fMBT
Best Python code snippet using fMBT_python
app.py
Source:app.py 
1#!/usr/bin/env python2# coding: utf-83# In[1]:4import numpy as np5from flask import Flask,request,jsonify,render_template6import pickle7from sklearn.preprocessing import StandardScaler8# In[2]:9app= Flask(__name__)10model=pickle.load(open('model.pkl','rb'))11# In[3]:12@app.route('/')13def home():14 return render_template('index.html')15# In[4]:16State_dict={'Chandigarh': 0,17 'Mizoram': 1,18 'Arunanchal Pradesh': 2,19 'Sikkim': 3,20 'Nagaland': 4,21 'Meghalaya': 5,22 'Manipur': 6,23 'Dadara & Nagar Havelli': 7,24 'Himachal Pradesh': 8,25 'Tripura': 9,26 'Jharkhand': 10,27 'Jammu & Kashmir': 11,28 'Chhattisgarh': 12,29 'Odisha': 13,30 'Madhya Pradesh': 14,31 'Bihar': 15,32 'Rajasthan': 16,33 'Uttarakhand': 17,34 'Karnataka': 18,35 'Gujarat': 19,36 'Telangana': 20,37 'Haryana': 21,38 'Maharashtra': 22,39 'Uttar Pradesh': 23,40 'Assam': 24,41 'West Bengal': 25,42 'Punjab': 26,43 'Puducherry': 27,44 'Tamil Nadu': 28,45 'Andhra Pradesh': 29,46 'Goa': 30,47 'Andaman & Nicobar Island': 31,48 'Kerala': 32,49 'Andamanandnicobarislands': 31,50 'Andhrapradesh': 29,51 'Arunachalpradesh': 2,52 'Dadraandnagarhavelli': 7,53 'Himachalpradesh': 8,54 'Jammuandkashmir': 11,55 'Madhyapradesh': 14,56 'Tamilnadu': 28,57 'Uttarpradesh': 23,58 'Westbengal': 25}59# In[5]:60Dist_dict={'MUMBAI': 0,61 'MAMIT': 1,62 'LEH LADAKH': 2,63 'KINNAUR': 3,64 'LAWNGTLAI': 4,65 'KARGIL': 5,66 'HYDERABAD': 6,67 'CHANDIGARH': 7,68 'KURUNG KUMEY': 8,69 'NAMSAI': 9,70 'SAIHA': 10,71 'KHUNTI': 11,72 'NORTH DISTRICT': 12,73 'RAMGARH': 13,74 'SOUTH GARO HILLS': 14,75 'ANJAW': 15,76 'NORTH GARO HILLS': 16,77 'WEST JAINTIA HILLS': 17,78 'TAWANG': 18,79 'NARAYANPUR': 19,80 'SHOPIAN': 20,81 'LUNGLEI': 21,82 'WEST KAMENG': 22,83 'TIRAP': 23,84 'SOUTH WEST KHASI HILLS': 24,85 'CHAMPHAI': 25,86 'LONGLENG': 26,87 'PALGHAR': 27,88 'UPPER SIANG': 28,89 'UPPER SUBANSIRI': 29,90 'KISHTWAR': 30,91 'EAST KAMENG': 31,92 'LONGDING': 32,93 'KOLASIB': 33,94 'TAMENGLONG': 34,95 'SERCHHIP': 35,96 'CHAMPAWAT': 36,97 'CHANDEL': 37,98 'SRINAGAR': 38,99 'DIBANG VALLEY': 39,100 'KIPHIRE': 40,101 'LOWER SUBANSIRI': 41,102 'EAST JAINTIA HILLS': 42,103 'RUDRA PRAYAG': 43,104 'MON': 44,105 'PAPUM PARE': 45,106 'LAHUL AND SPITI': 46,107 'ZUNHEBOTO': 47,108 'KONDAGAON': 48,109 'MOKOKCHUNG': 49,110 'SOUTH WEST GARO HILLS': 50,111 'BANDIPORA': 51,112 'KODERMA': 52,113 'ARWAL': 53,114 'CHANGLANG': 54,115 'LATEHAR': 55,116 'BAGESHWAR': 56,117 'EAST GARO HILLS': 57,118 'KOHIMA': 58,119 'PEREN': 59,120 'UMARIA': 60,121 'REASI': 61,122 'UKHRUL': 62,123 'PHEK': 63,124 'AIZAWL': 64,125 'RAMBAN': 65,126 'WOKHA': 66,127 'WEST DISTRICT': 67,128 'BOKARO': 68,129 'TUENSANG': 69,130 'EAST DISTRICT': 70,131 'WEST SIANG': 71,132 'DEOGARH': 72,133 'RI BHOI': 73,134 'KOREA': 74,135 'WEST KHASI HILLS': 75,136 'DHALAI': 76,137 'KANDHAMAL': 77,138 'UNAKOTI': 78,139 'KULGAM': 79,140 'GANDERBAL': 80,141 'BENGALURU URBAN': 81,142 'GARHWA': 82,143 'GAJAPATI': 83,144 'RAYAGADA': 84,145 'CHURACHANDPUR': 85,146 'MUNGER': 86,147 'SAHEBGANJ': 87,148 'LOWER DIBANG VALLEY': 88,149 'JHARSUGUDA': 89,150 'ANUPPUR': 90,151 'SHIMLA': 91,152 'SENAPATI': 92,153 'UTTAR KASHI': 93,154 'KORBA': 94,155 'BOUDH': 95,156 'SOUTH DISTRICT': 96,157 'DANG': 97,158 'DHANBAD': 98,159 'EAST SIANG': 99,160 'PITHORAGARH': 100,161 'BISHNUPUR': 101,162 'NUAPADA': 102,163 'NORTH TRIPURA': 103,164 'LOHARDAGA': 104,165 'LOHIT': 105,166 'KHOWAI': 106,167 'THE NILGIRIS': 107,168 'CHATRA': 108,169 'PULWAMA': 109,170 'CHAMBA': 110,171 'PAURI GARHWAL': 111,172 'AGAR MALWA': 112,173 'KULLU': 113,174 'SHEIKHPURA': 114,175 'THOUBAL': 115,176 'SOLAN': 116,177 'BADGAM': 117,178 'JASHPUR': 118,179 'RAJSAMAND': 119,180 'ALIRAJPUR': 120,181 'IMPHAL WEST': 121,182 'SURAJPUR': 122,183 'SIMDEGA': 123,184 'TEHRI GARHWAL': 124,185 'JAMUI': 125,186 'SARAIKELA KHARSAWAN': 126,187 'JAISALMER': 127,188 'JAMTARA': 128,189 'IMPHAL EAST': 129,190 'PAKUR': 130,191 'DANTEWADA': 131,192 'SIROHI': 132,193 'GODDA': 133,194 'DINDORI': 134,195 'SAMBA': 135,196 'Dadara & Nagar Havelli': 136,197 'DUNGARPUR': 137,198 'BALODA BAZAR': 138,199 'PALAMU': 139,200 'NAWADA': 140,201 'SHEOHAR': 141,202 'ANUGUL': 142,203 'DODA': 143,204 'CHAMOLI': 144,205 'KABIRDHAM': 145,206 'UNA': 146,207 'CHIKBALLAPUR': 147,208 'ALMORA': 148,209 'RAIGARH': 149,210 'SUKMA': 150,211 'SHAHDOL': 151,212 'SIRMAUR': 152,213 'GOMATI': 153,214 'DIMAPUR': 154,215 'WEST GARO HILLS': 155,216 'KHAGARIA': 156,217 'MALKANGIRI': 157,218 'PANCHKULA': 158,219 'MANDLA': 159,220 'GARIYABAND': 160,221 'KODAGU': 161,222 'KATHUA': 162,223 'JEHANABAD': 163,224 'KUPWARA': 164,225 'BASTAR': 165,226 'EAST KHASI HILLS': 166,227 'SINGRAULI': 167,228 'KATNI': 168,229 'LAKHISARAI': 169,230 'KHORDHA': 170,231 'PANNA': 171,232 'KANKER': 172,233 'DHAMTARI': 173,234 'RAMANAGARA': 174,235 'GUMLA': 175,236 'TINSUKIA': 176,237 'CHITRAKOOT': 177,238 'AJMER': 178,239 'SIDHI': 179,240 'DHENKANAL': 180,241 'JAJAPUR': 181,242 'SOUTH TRIPURA': 182,243 'BARWANI': 183,244 'WEST TRIPURA': 184,245 'UDHAMPUR': 185,246 'SAMBALPUR': 186,247 'HAZARIBAGH': 187,248 'DEOGHAR': 188,249 'DIMA HASAO': 189,250 'JHABUA': 190,251 'GIRIDIH': 191,252 'SONEPUR': 192,253 'BEGUSARAI': 193,254 'TIKAMGARH': 194,255 'JAGATSINGHAPUR': 195,256 'KENDUJHAR': 196,257 'GADCHIROLI': 197,258 'MUNGELI': 198,259 'MADHUBANI': 199,260 'BANGALORE RURAL': 200,261 'PALI': 201,262 'SURGUJA': 202,263 'UTTAR KANNAD': 203,264 'MAHOBA': 204,265 'SONBHADRA': 205,266 'SEPAHIJALA': 206,267 'DUMKA': 207,268 'SANT RAVIDAS NAGAR': 208,269 'BARAMULLA': 209,270 'VAISHALI': 210,271 'YADGIR': 211,272 'BANSWARA': 212,273 'BHOPAL': 213,274 'SAHARSA': 214,275 'BALOD': 215,276 'MAHASAMUND': 216,277 'DARBHANGA': 217,278 'WEST SINGHBHUM': 218,279 'NEEMUCH': 219,280 'NAYAGARH': 220,281 'RAJNANDGAON': 221,282 'BILASPUR': 222,283 'KOLAR': 223,284 'SUNDARGARH': 224,285 'GAYA': 225,286 'SATNA': 226,287 'JANJGIR-CHAMPA': 227,288 'GONDIA': 228,289 'KENDRAPARA': 229,290 'UDAIPUR': 230,291 'KANGRA': 231,292 'POONCH': 232,293 'DAMOH': 233,294 'ANANTNAG': 234,295 'RAJAURI': 235,296 'KISHANGANJ': 236,297 'BHAGALPUR': 237,298 'BALANGIR': 238,299 'GADAG': 239,300 'JAMMU': 240,301 'JALORE': 241,302 'RANGAREDDI': 242,303 'MANDI': 243,304 'DIBRUGARH': 244,305 'BALAGHAT': 245,306 'BEMETARA': 246,307 'HAMIRPUR': 247,308 'NALANDA': 248,309 'SARAN': 249,310 'CHIKMAGALUR': 250,311 'BARMER': 251,312 'KORAPUT': 252,313 'UDUPI': 253,314 'NABARANGPUR': 254,315 'REWA': 255,316 'PURI': 256,317 'PANCH MAHALS': 257,318 'DHARWAD': 258,319 'DHEMAJI': 259,320 'SHEOPUR': 260,321 'BANKA': 261,322 'KAUSHAMBI': 262,323 'JABALPUR': 263,324 'EAST SINGHBUM': 264,325 'SEONI': 265,326 'SUPAUL': 266,327 'BUXAR': 267,328 'KOPPAL': 268,329 'CHHATARPUR': 269,330 'KALAHANDI': 270,331 'BHILWARA': 271,332 'NAINITAL': 272,333 'LUCKNOW': 273,334 'SAWAI MADHOPUR': 274,335 'MADHEPURA': 275,336 'BHIND': 276,337 'MUZAFFARPUR': 277,338 'KHARGONE': 278,339 'DHOLPUR': 279,340 'BANDA': 280,341 'PATNA': 281,342 'CUTTACK': 282,343 'DATIA': 283,344 'BALESHWAR': 284,345 'BHOJPUR': 285,346 'GANDHINAGAR': 286,347 'TONK': 287,348 'PORBANDAR': 288,349 'BHADRAK': 289,350 'SAMASTIPUR': 290,351 'ASHOKNAGAR': 291,352 'SAGAR': 292,353 'LALITPUR': 293,354 'HAVERI': 294,355 'HARDA': 295,356 'SIWAN': 296,357 'RAIPUR': 297,358 'KARAULI': 298,359 'DAUSA': 299,360 'PURULIA': 300,361 'GWALIOR': 301,362 'SINDHUDURG': 302,363 'KHANDWA': 303,364 'KAIMUR (BHABUA)': 304,365 'JHANSI': 305,366 'MAYURBHANJ': 306,367 'DARJEELING': 307,368 'RAJGARH': 308,369 'GUNA': 309,370 'RANCHI': 310,371 'KATIHAR': 311,372 'BIKANER': 312,373 'DOHAD': 313,374 'JHALAWAR': 314,375 'CHITRADURGA': 315,376 'THANE': 316,377 'ADILABAD': 317,378 'ARARIA': 318,379 'DURG': 319,380 'RATNAGIRI': 320,381 'RATLAM': 321,382 'MIRZAPUR': 322,383 'BELLARY': 323,384 'PATAN': 324,385 'RAICHUR': 325,386 'SHRAVASTI': 326,387 'WASHIM': 327,388 'BETUL': 328,389 'VARANASI': 329,390 'HAILAKANDI': 330,391 'PURNIA': 331,392 'SHIVPURI': 332,393 'MANDSAUR': 333,394 'CHANDAULI': 334,395 'GANJAM': 335,396 'BARGARH': 336,397 'RAISEN': 337,398 'BUNDI': 338,399 'BURHANPUR': 339,400 'KOTA': 340,401 'BHANDARA': 341,402 'MORENA': 342,403 'BARAN': 343,404 'JODHPUR': 344,405 'CHANDRAPUR': 345,406 'INDORE': 346,407 'DEHRADUN': 347,408 'SITAMARHI': 348,409 'CHAMARAJANAGAR': 349,410 'CHURU': 350,411 'CHITTORGARH': 351,412 'DAKSHIN KANNAD': 352,413 'VIDISHA': 353,414 'KAMRUP METRO': 354,415 'PRATAPGARH': 355,416 'GAUTAM BUDDHA NAGAR': 356,417 'SIKAR': 357,418 'TUMKUR': 358,419 'ETAWAH': 359,420 'HOSHANGABAD': 360,421 'AMETHI': 361,422 'SANT KABEER NAGAR': 362,423 'SEHORE': 363,424 'SHAJAPUR': 364,425 'RAIGAD': 365,426 'JALAUN': 366,427 'KADAPA': 367,428 'BHARATPUR': 368,429 'NAGPUR': 369,430 'REWARI': 370,431 'WARDHA': 371,432 'KANPUR DEHAT': 372,433 'AURAIYA': 373,434 'DHAR': 374,435 'AKOLA': 375,436 'JHUNJHUNU': 376,437 'ROHTAS': 377,438 'DEWAS': 378,439 'MAHBUBNAGAR': 379,440 'UNNAO': 380,441 'DHULE': 381,442 'PURBI CHAMPARAN': 382,443 'HASSAN': 383,444 'UJJAIN': 384,445 'MAHESANA': 385,446 'RAE BARELI': 386,447 'SHIMOGA': 387,448 'NANDURBAR': 388,449 'KACHCHH': 389,450 'MAU': 390,451 'AHMADABAD': 391,452 'KANPUR NAGAR': 392,453 'MYSORE': 393,454 'KHEDA': 394,455 'LAKHIMPUR': 395,456 'AMRAVATI': 396,457 'SABAR KANTHA': 397,458 'NAGAUR': 398,459 'GULBARGA': 399,460 'PRAKASAM': 400,461 'KASGANJ': 401,462 'CHHINDWARA': 402,463 'WARANGAL': 403,464 'GURGAON': 404,465 'DAVANGERE': 405,466 'GORAKHPUR': 406,467 'SIDDHARTH NAGAR': 407,468 'GOPALGANJ': 408,469 'NARSINGHPUR': 409,470 'JALPAIGURI': 410,471 'MAHENDRAGARH': 411,472 'VADODARA': 412,473 'BULDHANA': 413,474 'FATEHPUR': 414,475 'NALGONDA': 415,476 'JHAJJAR': 416,477 'HATHRAS': 417,478 'TAPI': 418,479 'JAIPUR': 419,480 'ALLAHABAD': 420,481 'MAINPURI': 421,482 'KURNOOL': 422,483 'HINGOLI': 423,484 'S.A.S NAGAR': 424,485 'BALLIA': 425,486 'FARIDABAD': 426,487 'BIDAR': 427,488 'NARMADA': 428,489 'HANUMANGARH': 429,490 'MEWAT': 430,491 'SULTANPUR': 431,492 'DINAJPUR UTTAR': 432,493 'BIJAPUR': 433,494 'GHAZIPUR': 434,495 'AURANGABAD': 435,496 'NANDED': 436,497 'DINAJPUR DAKSHIN': 437,498 'CHIRANG': 438,499 'AMBEDKAR NAGAR': 439,500 'ALWAR': 440,501 'KANNAUJ': 441,502 'GANGANAGAR': 442,503 'JAMNAGAR': 443,504 'DEORIA': 444,505 'ANAND': 445,506 'FIROZABAD': 446,507 'YAVATMAL': 447,508 'BANAS KANTHA': 448,509 'AMRELI': 449,510 'PARBHANI': 450,511 'MATHURA': 451,512 'KOKRAJHAR': 452,513 'NIZAMABAD': 453,514 'FAIZABAD': 454,515 'PANIPAT': 455,516 'KARBI ANGLONG': 456,517 'CACHAR': 457,518 'JALNA': 458,519 'OSMANABAD': 459,520 'FARRUKHABAD': 460,521 'SURENDRANAGAR': 461,522 'ETAH': 462,523 'KARIMGANJ': 463,524 'KARIMNAGAR': 464,525 'MANDYA': 465,526 'PATHANKOT': 466,527 'JAUNPUR': 467,528 'SIVASAGAR': 468,529 'PALWAL': 469,530 'BANKURA': 470,531 'BAHRAICH': 471,532 'MALDAH': 472,533 'BARABANKI': 473,534 'AZAMGARH': 474,535 'BHIWANI': 475,536 'ROHTAK': 476,537 'NAVSARI': 477,538 'UDALGURI': 478,539 'MAHARAJGANJ': 479,540 'HISAR': 480,541 'VALSAD': 481,542 'ALIGARH': 482,543 'ARIYALUR': 483,544 'RUPNAGAR': 484,545 'RAJKOT': 485,546 'HAPUR': 486,547 'BHAVNAGAR': 487,548 'AGRA': 488,549 'JORHAT': 489,550 'MEDAK': 490,551 'FATEHABAD': 491,552 'BHARUCH': 492,553 'SONIPAT': 493,554 'RAMPUR': 494,555 'BONGAIGAON': 495,556 'PERAMBALUR': 496,557 '24 PARAGANAS NORTH': 497,558 'BALRAMPUR': 498,559 'NASHIK': 499,560 'SAMBHAL': 500,561 'GOALPARA': 501,562 'LATUR': 502,563 'JALGAON': 503,564 'AMBALA': 504,565 'ANANTAPUR': 505,566 'DHUBRI': 506,567 'DARRANG': 507,568 'BASTI': 508,569 'JUNAGADH': 509,570 'KARNAL': 510,571 'UDAM SINGH NAGAR': 511,572 'KAITHAL': 512,573 'JIND': 513,574 'SIRSA': 514,575 'GONDA': 515,576 'FARIDKOT': 516,577 'GUNTUR': 517,578 'BIRBHUM': 518,579 'COOCHBEHAR': 519,580 'BUDAUN': 520,581 'BEED': 521,582 'MANSA': 522,583 'PASHCHIM CHAMPARAN': 523,584 'HARDOI': 524,585 'HOSHIARPUR': 525,586 'NAWANSHAHR': 526,587 'BAGALKOT': 527,588 'MOGA': 528,589 'KARAIKAL': 529,590 'KURUKSHETRA': 530,591 'FATEHGARH SAHIB': 531,592 'BARNALA': 532,593 'SHAHJAHANPUR': 533,594 'BULANDSHAHR': 534,595 'MUKTSAR': 535,596 'KAPURTHALA': 536,597 'MARIGAON': 537,598 'YAMUNANAGAR': 538,599 'SATARA': 539,600 'KUSHI NAGAR': 540,601 'THIRUVALLUR': 541,602 'TARN TARAN': 542,603 'GHAZIABAD': 543,604 'BATHINDA': 544,605 'TUTICORIN': 545,606 'MORADABAD': 546,607 'PILIBHIT': 547,608 'SANGLI': 548,609 'MEDINIPUR WEST': 549,610 'SONITPUR': 550,611 'HARIDWAR': 551,612 'BAKSA': 552,613 'PATIALA': 553,614 'JALANDHAR': 554,615 'AHMEDNAGAR': 555,616 'NADIA': 556,617 'TIRUVANNAMALAI': 557,618 'FAZILKA': 558,619 'BARPETA': 559,620 'KARUR': 560,621 'BARDHAMAN': 561,622 'SPSR NELLORE': 562,623 'AMRITSAR': 563,624 'LUDHIANA': 564,625 'SITAPUR': 565,626 'SHAMLI': 566,627 'KHAMMAM': 567,628 'TIRUCHIRAPPALLI': 568,629 'BAREILLY': 569,630 'PUNE': 570,631 'AMROHA': 571,632 'BAGHPAT': 572,633 'NALBARI': 573,634 'MEDINIPUR EAST': 574,635 'GURDASPUR': 575,636 'HOWRAH': 576,637 'BELGAUM': 577,638 'SOLAPUR': 578,639 'HOOGHLY': 579,640 'SANGRUR': 580,641 'NAGAPATTINAM': 581,642 'GOLAGHAT': 582,643 'FIROZEPUR': 583,644 'MURSHIDABAD': 584,645 'KANCHIPURAM': 585,646 'KAMRUP': 586,647 'SURAT': 587,648 '24 PARAGANAS SOUTH': 588,649 'SAHARANPUR': 589,650 'YANAM': 590,651 'KHERI': 591,652 'CUDDALORE': 592,653 'KOLHAPUR': 593,654 'VILLUPURAM': 594,655 'PONDICHERRY': 595,656 'RAMANATHAPURAM': 596,657 'PUDUKKOTTAI': 597,658 'MEERUT': 598,659 'MAHE': 599,660 'DHARMAPURI': 600,661 'BIJNOR': 601,662 'SIVAGANGA': 602,663 'MADURAI': 603,664 'VIZIANAGARAM': 604,665 'NAMAKKAL': 605,666 'CHITTOOR': 606,667 'KRISHNA': 607,668 'MUZAFFARNAGAR': 608,669 'SALEM': 609,670 'VIRUDHUNAGAR': 610,671 'TIRUNELVELI': 611,672 'THIRUVARUR': 612,673 'NAGAON': 613,674 'ERODE': 614,675 'KANNIYAKUMARI': 615,676 'VELLORE': 616,677 'SOUTH ANDAMANS': 617,678 'NORTH AND MIDDLE ANDAMAN': 618,679 'VISAKHAPATANAM': 619,680 'KRISHNAGIRI': 620,681 'DINDIGUL': 621,682 'THENI': 622,683 'SRIKAKULAM': 623,684 'NORTH GOA': 624,685 'THANJAVUR': 625,686 'SOUTH GOA': 626,687 'WAYANAD': 627,688 'IDUKKI': 628,689 'TIRUPPUR': 629,690 'NICOBARS': 630,691 'COIMBATORE': 631,692 'WEST GODAVARI': 632,693 'PATHANAMTHITTA': 633,694 'EAST GODAVARI': 634,695 'KOTTAYAM': 635,696 'PALAKKAD': 636,697 'ERNAKULAM': 637,698 'KOLLAM': 638,699 'ALAPPUZHA': 639,700 'KASARAGOD': 640,701 'KANNUR': 641,702 'THIRUVANANTHAPURAM': 642,703 'THRISSUR': 643,704 'MALAPPURAM': 644,705 'KOZHIKODE': 645,706 'DADRA AND NAGARHAVELI' :136}707# In[6]:708Season_dict={'Summer': 0,709 'Autumn': 1,710 'Rabi': 2,711 'Kharif': 3,712 'Winter': 4,713 'Wholeyear': 5}714# In[7]:715Crop_dict={'Apple': 0,716 'Pump kin': 1,717 'Snak Guard': 2,718 'Cucumber': 3,719 'Lab-Lab': 4,720 'Plums': 5,721 'Ribed Guard': 6,722 'Litchi': 7,723 'Ber': 8,724 'Beet Root': 9,725 'Other Citrus Fruit': 10,726 'Pear': 11,727 'other fibres': 12,728 'Peas (vegetable)': 13,729 'Yam': 14,730 'Peach': 15,731 'Ash Gourd': 16,732 'Water Melon': 17,733 'Bitter Gourd': 18,734 'Bottle Gourd': 19,735 'Turnip': 20,736 'Redish': 21,737 'Cond-spcs other': 22,738 'Jobster': 23,739 'Carrot': 24,740 'other misc. pulses': 25,741 'Perilla': 26,742 'Sannhamp': 27,743 'Cauliflower': 28,744 'Cashewnut Processed': 29,745 'Cardamom': 30,746 'Bean': 31,747 'Lentil': 32,748 'Cowpea(Lobia)': 33,749 'Ricebean (nagadal)': 34,750 'Blackgram': 35,751 'Linseed': 36,752 'Jack Fruit': 37,753 'Kapas': 38,754 'Niger seed': 39,755 'Drum Stick': 40,756 'Korra': 41,757 'Pome Granet': 42,758 'Varagu': 43,759 'Other Fresh Fruits': 44,760 'Bhindi': 45,761 'Rajmash Kholar': 46,762 'Horse-gram': 47,763 'Coriander': 48,764 'Sesamum': 49,765 'Small millets': 50,766 'Other Kharif pulses': 51,767 'Beans & Mutter(Vegetable)': 52,768 'Other Rabi pulses': 53,769 'Moong(Green Gram)': 54,770 'Pome Fruit': 55,771 'Peas & beans (Pulses)': 56,772 'Cabbage': 57,773 'Sweet potato': 58,774 'Other Vegetables': 59,775 'Black pepper': 60,776 'Samai': 61,777 'Other Cereals & Millets': 62,778 'Citrus Fruit': 63,779 'Safflower': 64,780 'Sunflower': 65,781 'Cashewnut': 66,782 'Urad': 67,783 'Turmeric': 68,784 'Dry chillies': 69,785 'Tea': 70,786 'Garlic': 71,787 'Dry ginger': 72,788 'Masoor': 73,789 'Ginger': 74,790 'Cashewnut Raw': 75,791 'Brinjal': 76,792 'Moth': 77,793 'Tobacco': 78,794 'Castor seed': 79,795 'Khesari': 80,796 'Colocosia': 81,797 'Arhar/Tur': 82,798 'Barley': 83,799 'Jute & mesta': 84,800 'Mesta': 85,801 'Sapota': 86,802 'Lemon': 87,803 'Orange': 88,804 'Pineapple': 89,805 'Ragi': 90,806 'Papaya': 91,807 'other oilseeds': 92,808 'Arcanut (Processed)': 93,809 'Onion': 94,810 'Guar seed': 95,811 'Rapeseed &Mustard': 96,812 'Arecanut': 97,813 'Groundnut': 98,814 'Gram': 99,815 'Tomato': 100,816 'Grapes': 101,817 'Jowar': 102,818 'Maize': 103,819 'Bajra': 104,820 'Coffee': 105,821 'Mango': 106,822 'Rubber': 107,823 'Soyabean': 108,824 'Banana': 109,825 'Atcanut (Raw)': 110,826 'Potato': 111,827 'Pulses total': 112,828 'Paddy': 113,829 'Tapioca': 114,830 'Cotton(lint)': 115,831 'Oilseeds total': 116,832 'Rice': 117,833 'Jute': 118,834 'Wheat': 119,835 'Total foodgrain': 120,836 'Sugarcane': 121,837 'Coconut': 122}838# In[8]:839Year_dict={2015: 0,840 1997: 1,841 2001: 2,842 2010: 3,843 2007: 4,844 2006: 5,845 2002: 6,846 1999: 7,847 2009: 8,848 2008: 9,849 2003: 10,850 2000: 11,851 2012: 12,852 2004: 13,853 2005: 14,854 1998: 15,855 2013: 16,856 2014: 17,857 2011: 18,858 2016: 19,859 2017: 20,860 2018: 21,861 2019: 22,862 2020: 23,863 2021: 24,864 2022: 25,865 2023: 26,866 2024: 27,867 2025: 28,868 2026: 29,869 2027: 30, 870 2028: 31, 871 2029: 32, 872 2030: 33, 873 2031: 34, 874 2032: 35, 875 2033: 36, 876 2034: 37, 877 2035: 38, 878 2036: 39, 879 2037: 40,880 2038: 41, 881 2039: 42, 882 2040: 43, 883 2041: 44, 884 2042: 45, 885 2043: 46, 886 2044: 47, 887 2045: 48,888 2046: 49, 889 2047: 50, 890 2048: 51, 891 2049: 52, 892 2050: 53, 893 2051: 54}894 895# In[9]:896a="Andaman & Nicobar Island"897# In[10]:898print(State_dict.get(a))899# In[11]:900import datetime901now = datetime.datetime.now()902check_year=now.year903print(check_year)904predict_year=check_year+2905print('predict_year',predict_year)906standard_to = StandardScaler()907@app.route('/predict',methods=['POST'])908def predict():909 #int_features=[String(x) for x in request.form.values()]910 #final_features =[np.array(int_features)]911 if request.method == 'POST':912 State_Name=request.form['State_Name']913 State_Name1=State_Name914 State_Name=State_Name.capitalize()915 State_Name=State_dict.get(State_Name)916 District_Name =request.form['District_Name']917 District_Name1=District_Name918 District_Name=District_Name.upper()919 District_Name=Dist_dict.get(District_Name)920 Crop_Year=int(request.form['Crop_Year'])921 Crop_Year_original=Crop_Year922 if Crop_Year<1998:923 Crop_Year1=Crop_Year924 #Crop_Year=1925 Crop_Year2=Crop_Year-2926 Crop_Year7=Crop_Year2927 Crop_Year2=1928 Crop_Year3=Crop_Year-1929 Crop_Year8=Crop_Year3930 Crop_Year3=1931 Crop_Year4=Crop_Year+1932 Crop_Year9=Crop_Year4933 Crop_Year4=1934 Crop_Year5=Crop_Year+2935 Crop_Year10=Crop_Year5936 Crop_Year5=1937 Crop_Year=1938 else:939 Crop_Year1=Crop_Year940 Crop_Year2=Crop_Year-2941 Crop_Year7=Crop_Year2942 Crop_Year3=Crop_Year-1943 Crop_Year8=Crop_Year3944 Crop_Year4=Crop_Year+1945 Crop_Year9=Crop_Year4946 Crop_Year5=Crop_Year+2947 Crop_Year10=Crop_Year5948 if Crop_Year<2052:949 Crop_Year=Year_dict.get(Crop_Year)950 else:951 Crop_Year=55952 if Crop_Year2<2052:953 Crop_Year2=Year_dict.get(Crop_Year2)954 else:955 Crop_Year2=55956 if Crop_Year3<2052:957 Crop_Year3=Year_dict.get(Crop_Year3)958 else:959 Crop_Year3=55960 if Crop_Year4<2052:961 Crop_Year4=Year_dict.get(Crop_Year4)962 else:963 Crop_Year4=55964 if Crop_Year5<2052:965 Crop_Year5=Year_dict.get(Crop_Year5)966 else:967 Crop_Year5=55968 Season=request.form['Season']969 Season1=Season970 Season=Season.capitalize()971 Season=Season_dict.get(Season)972 Crop=request.form['Crop']973 #Crop=Crop.capitalize()974 Crop1=Crop975 if Crop1=='Arhar/Tur':976 crop2='ArharTur'977 elif Crop1=='Castor seed':978 crop2='Castorseed'979 elif Crop1=='Cond-spcs other':980 crop2='Condspcsother'981 elif Crop1=='Cotton(lint)':982 crop2='Cottonlint'983 elif Crop1=='Moong(Green Gram)':984 crop2='MoongGreenGram'985 elif Crop1=='Rapeseed &Mustard':986 crop2='RapeseedMustard'987 else:988 crop2=Crop1989 Crop=Crop_dict.get(Crop)990 Area=float(request.form['Area'])991 Area_in=Area992 print([[State_Name,District_Name,Crop_Year,Season,Crop,Area]] ) 993 print([[State_Name,District_Name,Crop_Year2,Season,Crop,Area]] )994 print([[State_Name,District_Name,Crop_Year3,Season,Crop,Area]] )995 print([[State_Name,District_Name,Crop_Year4,Season,Crop,Area]] )996 print([[State_Name,District_Name,Crop_Year5,Season,Crop,Area]] )997 if Area<10:998 Area1=Area+5999 Area2=Area1+51000 Area3=Area2+51001 Area4=Area3+51002 elif Area<100:1003 Area1=Area+101004 Area2=Area1+101005 Area3=Area2+101006 Area4=Area3+101007 elif Area<1000:1008 Area1=Area+501009 Area2=Area1+501010 Area3=Area2+501011 Area4=Area3+501012 elif Area<10000:1013 Area1=Area+1001014 Area2=Area1+1001015 Area3=Area2+1001016 Area4=Area3+1001017 elif Area>9999:1018 Area1=Area+10001019 Area2=Area1+10001020 Area3=Area2+10001021 Area4=Area3+10001022 1023 prediction=model.predict([[State_Name,District_Name,Crop_Year,Season,Crop,Area]] ) 1024 prediction1=model.predict([[State_Name,District_Name,Crop_Year2,Season,Crop,Area]] )1025 prediction2=model.predict([[State_Name,District_Name,Crop_Year3,Season,Crop,Area]] )1026 prediction3=model.predict([[State_Name,District_Name,Crop_Year4,Season,Crop,Area]] )1027 prediction4=model.predict([[State_Name,District_Name,Crop_Year5,Season,Crop,Area]] )1028 prediction_Area1=model.predict([[State_Name,District_Name,Crop_Year,Season,Crop,Area1]] )1029 prediction_Area2=model.predict([[State_Name,District_Name,Crop_Year,Season,Crop,Area2]] )1030 prediction_Area3=model.predict([[State_Name,District_Name,Crop_Year,Season,Crop,Area3]] )1031 prediction_Area4=model.predict([[State_Name,District_Name,Crop_Year,Season,Crop,Area4]] )1032 1033 1034 1035 print('Crop_Year',Crop_Year_original)1036 if predict_year < Crop_Year_original:1037 prediction=01038 print('year_if')1039 return render_template('index.html',1040 prediction_text='The tool supports the prediction for next 2 years',1041 state='State_Name :{}'.format(State_Name1),1042 district='District_Name :{}'.format(District_Name1),1043 year='Crop_Year :{}'.format(Crop_Year1),1044 season='Season :{}'.format(Season1),1045 crop='Crop :{}'.format(Crop1),1046 Area='Area :{} ha'.format(Area_in))1047 #Arhar/Tur1048 if State_Name==30 and Crop==82 or State_Name==10 and Crop==82 or State_Name==6 and Crop==82 or State_Name==3 and Crop==82 :1049 prediction=01050 return render_template('index.html',1051 prediction_text='The predicted production of the crop is {} tonne as selected crop is not cultivated in this state'.format(prediction),1052 state='State_Name :{}'.format(State_Name1),1053 district='District_Name :{}'.format(District_Name1),1054 year='Crop_Year :{}'.format(Crop_Year1),1055 season='Season :{}'.format(Season1),1056 crop='Crop :{}'.format(Crop1),1057 Area='Area :{} ha'.format(Area_in))1058 #Banana1059 if State_Name==21 and Crop==109 or State_Name==8 and Crop==109 or State_Name==10 and Crop==109 or State_Name==26 and Crop==109 or State_Name==7 and Crop==109 or State_Name==17 and Crop==109 or State_Name==0 and Crop==109 :1060 prediction=01061 return render_template('index.html',1062 prediction_text='The predicted production of the crop is {} tonne as selected crop is not cultivated in this state'.format(prediction),1063 state='State_Name :{}'.format(State_Name1),1064 district='District_Name :{}'.format(District_Name1),1065 year='Crop_Year :{}'.format(Crop_Year1),1066 season='Season :{}'.format(Season1),1067 crop='Crop :{}'.format(Crop1),1068 Area='Area :{} ha'.format(Area_in))1069 1070 #Cabbage & Cauliflower1071 1072 if State_Name==30 and Crop==57 or State_Name==30 and Crop==28 or State_Name==20 and Crop==28 :1073 prediction=01074 return render_template('index.html',1075 prediction_text='The predicted production of the crop is {} tonne as selected crop is not cultivated in this state'.format(prediction),1076 state='State_Name :{}'.format(State_Name1),1077 district='District_Name :{}'.format(District_Name1),1078 year='Crop_Year :{}'.format(Crop_Year1),1079 season='Season :{}'.format(Season1),1080 crop='Crop :{}'.format(Crop1),1081 Area='Area :{} ha'.format(Area_in))1082 #Groundnut1083 if State_Name==24 and Crop==98 or State_Name==10 and Crop==98 or State_Name==1 and Crop==98 or State_Name==5 and Crop==98 or State_Name==3 and Crop==98 :1084 prediction=01085 return render_template('index.html',1086 prediction_text='The predicted production of the crop is {} tonne as selected crop is not cultivated in this state'.format(prediction),1087 state='State_Name :{}'.format(State_Name1),1088 district='District_Name :{}'.format(District_Name1),1089 year='Crop_Year :{}'.format(Crop_Year1),1090 season='Season :{}'.format(Season1),1091 crop='Crop :{}'.format(Crop1),1092 Area='Area :{} ha'.format(Area_in))1093 #Maize1094 if State_Name==30 and Crop==103 or State_Name==32 and Crop==103 or State_Name==27 and Crop==103 :1095 prediction=01096 return render_template('index.html',1097 prediction_text='The predicted production of the crop is {} tonne as selected crop is not cultivated in this state'.format(prediction),1098 state='State_Name :{}'.format(State_Name1),1099 district='District_Name :{}'.format(District_Name1),1100 year='Crop_Year :{}'.format(Crop_Year1),1101 season='Season :{}'.format(Season1),1102 crop='Crop :{}'.format(Crop1),1103 Area='Area :{} ha'.format(Area_in)) 1104 #Mango1105 if State_Name==5 and Crop==106 or State_Name==6 and Crop==106 or State_Name==3 and Crop==106 :1106 prediction=01107 return render_template('index.html',1108 prediction_text='The predicted production of the crop is {} tonne as selected crop is not cultivated in this state'.format(prediction),1109 state='State_Name :{}'.format(State_Name1),1110 district='District_Name :{}'.format(District_Name1),1111 year='Crop_Year :{}'.format(Crop_Year1),1112 season='Season :{}'.format(Season1),1113 crop='Crop :{}'.format(Crop1),1114 Area='Area :{} ha'.format(Area_in))1115 1116 #Moong1117 if State_Name==5 and Crop==54 or State_Name==6 and Crop==54 or State_Name==3 and Crop==54 or State_Name==30 and Crop==54 or State_Name==1 and Crop==54 or State_Name==4 and Crop==54:1118 prediction=01119 return render_template('index.html',1120 prediction_text='The predicted production of the crop is {} tonne as selected crop is not cultivated in this state'.format(prediction),1121 state='State_Name :{}'.format(State_Name1),1122 district='District_Name :{}'.format(District_Name1),1123 year='Crop_Year :{}'.format(Crop_Year1),1124 season='Season :{}'.format(Season1),1125 crop='Crop :{}'.format(Crop1),1126 Area='Area :{} ha'.format(Area_in))1127 #Onion1128 if State_Name==2 and Crop==94 or State_Name==30 and Crop==94 or State_Name==32 and Crop==94 or State_Name==3 and Crop==94 or State_Name==31 and Crop==94 or State_Name==7 and Crop==94:1129 prediction=01130 return render_template('index.html',1131 prediction_text='The predicted production of the crop is {} tonne as selected crop is not cultivated in this state'.format(prediction),1132 state='State_Name :{}'.format(State_Name1),1133 district='District_Name :{}'.format(District_Name1),1134 year='Crop_Year :{}'.format(Crop_Year1),1135 season='Season :{}'.format(Season1),1136 crop='Crop :{}'.format(Crop1),1137 Area='Area :{} ha'.format(Area_in))1138 #Potato & Rapseed(96)1139 if State_Name==30 and Crop==111 or State_Name==32 and Crop==111 or State_Name==31 and Crop==111 or State_Name==30 and Crop==96 or State_Name==32 and Crop==96:1140 prediction=01141 return render_template('index.html',1142 prediction_text='The predicted production of the crop is {} tonne as selected crop is not cultivated in this state'.format(prediction),1143 state='State_Name :{}'.format(State_Name1),1144 district='District_Name :{}'.format(District_Name1),1145 year='Crop_Year :{}'.format(Crop_Year1),1146 season='Season :{}'.format(Season1),1147 crop='Crop :{}'.format(Crop1),1148 Area='Area :{} ha'.format(Area_in))1149 #Soyabean1150 if State_Name==30 and Crop==108 or State_Name==24 and Crop==108 or State_Name==15 and Crop==108 or State_Name==21 and Crop==108 or State_Name==10 and Crop==108 or State_Name==32 and Crop==108 or State_Name==26 and Crop==108 or State_Name==9 and Crop==108 or State_Name==28 and Crop==108:1151 prediction=01152 return render_template('index.html',1153 prediction_text='The predicted production of the crop is {} tonne as selected crop is not cultivated in this state'.format(prediction),1154 state='State_Name :{}'.format(State_Name1),1155 district='District_Name :{}'.format(District_Name1),1156 year='Crop_Year :{}'.format(Crop_Year1),1157 season='Season :{}'.format(Season1),1158 crop='Crop :{}'.format(Crop1),1159 Area='Area :{} ha'.format(Area_in))1160 #Sugarcane & Urad1161 if State_Name==3 and Crop==121 or State_Name==0 and Crop==121 or State_Name==30 and Crop==67 or State_Name==6 and Crop==67 or State_Name==5 and Crop==67 or State_Name==3 and Crop==67 or State_Name==32 and Crop==67 or State_Name==1 and Crop==67:1162 prediction=01163 return render_template('index.html',1164 prediction_text='The predicted production of the crop is {} tonne as selected crop is not cultivated in this state'.format(prediction),1165 state='State_Name :{}'.format(State_Name1),1166 district='District_Name :{}'.format(District_Name1),1167 year='Crop_Year :{}'.format(Crop_Year1),1168 season='Season :{}'.format(Season1),1169 crop='Crop :{}'.format(Crop1),1170 Area='Area :{} ha'.format(Area_in))1171 #Wheat1172 1173 if State_Name==29 and Crop==119 or State_Name==32 and Crop==119 or State_Name==1 and Crop==119 or State_Name==30 and Crop==119 or State_Name==27 and Crop==119 or State_Name==31 and Crop==119:1174 prediction=01175 return render_template('index.html',1176 prediction_text='The predicted production of the crop is {} tonne as selected crop is not cultivated in this state'.format(prediction),1177 state='State_Name :{}'.format(State_Name1),1178 district='District_Name :{}'.format(District_Name1),1179 year='Crop_Year :{}'.format(Crop_Year1),1180 season='Season :{}'.format(Season1),1181 crop='Crop :{}'.format(Crop1),1182 Area='Area :{} ha'.format(Area_in))1183 #Rice1184 if State_Name==10 and Crop==117 or State_Name==16 and Crop==117 or State_Name==5 and Crop==117 or State_Name==9 and Crop==117 or State_Name==1 and Crop==117 or State_Name==6 and Crop==117 or State_Name==2 and Crop==117:1185 prediction=01186 return render_template('index.html',1187 prediction_text='The predicted production of the crop is {} tonne as selected crop is not cultivated in this state'.format(prediction),1188 state='State_Name :{}'.format(State_Name1),1189 district='District_Name :{}'.format(District_Name1),1190 year='Crop_Year :{}'.format(Crop_Year1),1191 season='Season :{}'.format(Season1),1192 crop='Crop :{}'.format(Crop1),1193 Area='Area :{} ha'.format(Area_in))1194 1195 #Barley1196 1197 if State_Name==0 and Crop==83 or State_Name==1 and Crop==83 or State_Name==2 and Crop==83 or State_Name==4 and Crop==83 or State_Name==5 and Crop==83 or State_Name==6 and Crop==83 or State_Name==7 and Crop==83 or State_Name==9 and Crop==83 or State_Name==10 and Crop==83 or State_Name==13 and Crop==83 or State_Name==18 and Crop==83 or State_Name==19 and Crop==83 or State_Name==20 and Crop==83 or State_Name==22 and Crop==83 or State_Name==24 and Crop==83 or State_Name==27 and Crop==83 or State_Name==28 and Crop==83 or State_Name==29 and Crop==83 or State_Name==30 and Crop==83 or State_Name==31 and Crop==83 or State_Name==32 and Crop==83 :1198 prediction=01199 return render_template('index.html',1200 prediction_text='The predicted production of the crop is {} tonne as selected crop is not cultivated in this state'.format(prediction),1201 state='State_Name :{}'.format(State_Name1),1202 district='District_Name :{}'.format(District_Name1),1203 year='Crop_Year :{}'.format(Crop_Year1),1204 season='Season :{}'.format(Season1),1205 crop='Crop :{}'.format(Crop1),1206 Area='Area :{} ha'.format(Area_in))1207 #Cashewnut1208 1209 if State_Name==0 and Crop==66 or State_Name==1 and Crop==66 or State_Name==2 and Crop==66 or State_Name==3 and Crop==66 or State_Name==6 and Crop==66 or State_Name==7 and Crop==66 or State_Name==8 and Crop==66 or State_Name==11 and Crop==66 or State_Name==13 and Crop==66 or State_Name==14 and Crop==66 or State_Name==15 and Crop==66 or State_Name==16 and Crop==66 or State_Name==17 and Crop==66 or State_Name==21 and Crop==66 or State_Name==23 and Crop==66 or State_Name==26 and Crop==66 or State_Name==31 and Crop==66 :1210 prediction=01211 return render_template('index.html',1212 prediction_text='The predicted production of the crop is {} tonne as selected crop is not cultivated in this state'.format(prediction),1213 state='State_Name :{}'.format(State_Name1),1214 district='District_Name :{}'.format(District_Name1),1215 year='Crop_Year :{}'.format(Crop_Year1),1216 season='Season :{}'.format(Season1),1217 crop='Crop :{}'.format(Crop1),1218 Area='Area :{} ha'.format(Area_in))1219 #castor1220 1221 if State_Name==0 and Crop==79 or State_Name==1 and Crop==79 or State_Name==2 and Crop==79 or State_Name==3 and Crop==79 or State_Name==5 and Crop==79 or State_Name==6 and Crop==79 or State_Name==7 and Crop==79 or State_Name==8 and Crop==79 or State_Name==9 and Crop==79 or State_Name==10 and Crop==79 or State_Name==11 and Crop==79 or State_Name==12 and Crop==79 or State_Name==17 and Crop==79 or State_Name==23 and Crop==79 or State_Name==25 and Crop==79 or State_Name==26 and Crop==79 or State_Name==27 and Crop==79 or State_Name==30 and Crop==79 or State_Name==31 and Crop==79 or State_Name==32 and Crop==79 :1222 prediction=01223 return render_template('index.html',1224 prediction_text='The predicted production of the crop is {} tonne as selected crop is not cultivated in this state'.format(prediction),1225 state='State_Name :{}'.format(State_Name1),1226 district='District_Name :{}'.format(District_Name1),1227 year='Crop_Year :{}'.format(Crop_Year1),1228 season='Season :{}'.format(Season1),1229 crop='Crop :{}'.format(Crop1),1230 Area='Area :{} ha'.format(Area_in))1231 #coconut1232 1233 if State_Name==0 and Crop==122 or State_Name==1 and Crop==122 or State_Name==2 and Crop==122 or State_Name==3 and Crop==122 or State_Name==5 and Crop==122 or State_Name==6 and Crop==122 or State_Name==7 and Crop==122 or State_Name==8 and Crop==122 or State_Name==10 and Crop==122 or State_Name==11 and Crop==122 or State_Name==12 and Crop==122 or State_Name==14 and Crop==122 or State_Name==15 and Crop==122 or State_Name==16 and Crop==122 or State_Name==17 and Crop==122 or State_Name==21 and Crop==122 or State_Name==23 and Crop==122 or State_Name==26 and Crop==122 :1234 prediction=01235 return render_template('index.html',1236 prediction_text='The predicted production of the crop is {} tonne as selected crop is not cultivated in this state'.format(prediction),1237 state='State_Name :{}'.format(State_Name1),1238 district='District_Name :{}'.format(District_Name1),1239 year='Crop_Year :{}'.format(Crop_Year1),1240 season='Season :{}'.format(Season1),1241 crop='Crop :{}'.format(Crop1),1242 Area='Area :{} ha'.format(Area_in))1243 #Coffee1244 1245 if State_Name==0 and Crop==105 or State_Name==1 and Crop==105 or State_Name==2 and Crop==105 or State_Name==3 and Crop==105 or State_Name==4 and Crop==105 or State_Name==5 and Crop==105 or State_Name==6 and Crop==105 or State_Name==7 and Crop==105 or State_Name==8 and Crop==105 or State_Name==9 and Crop==105 or State_Name==10 and Crop==105 or State_Name==11 and Crop==105 or State_Name==12 and Crop==105 or State_Name==14 and Crop==105 or State_Name==15 and Crop==105 or State_Name==16 and Crop==105 or State_Name==17 and Crop==105 or State_Name==19 and Crop==105 or State_Name==20 and Crop==105 or State_Name==21 and Crop==105 or State_Name==22 and Crop==105 or State_Name==23 and Crop==105 or State_Name==25 and Crop==105 or State_Name==26 and Crop==105 or State_Name==27 and Crop==105 or State_Name==30 and Crop==105 or State_Name==31 and Crop==105 :1246 1247 prediction=01248 return render_template('index.html',1249 prediction_text='The predicted production of the crop is {} tonne as selected crop is not cultivated in this state'.format(prediction),1250 state='State_Name :{}'.format(State_Name1),1251 district='District_Name :{}'.format(District_Name1),1252 year='Crop_Year :{}'.format(Crop_Year1),1253 season='Season :{}'.format(Season1),1254 crop='Crop :{}'.format(Crop1),1255 Area='Area :{} ha'.format(Area_in))1256 #CONDIMENTS 1257 1258 if State_Name==0 and Crop==22 or State_Name==1 and Crop==22 or State_Name==2 and Crop==22 or State_Name==3 and Crop==22 or State_Name==4 and Crop==22 or State_Name==5 and Crop==22 or State_Name==7 and Crop==22 or State_Name==8 and Crop==22 or State_Name==9 and Crop==22 or State_Name==11 and Crop==22 or State_Name==13 and Crop==22 or State_Name==15 and Crop==22 or State_Name==27 and Crop==22 or State_Name==30 and Crop==22 or State_Name==31 and Crop==22 or State_Name==32 and Crop==22 :1259 prediction=01260 return render_template('index.html',1261 prediction_text='The predicted production of the crop is {} tonne as selected crop is not cultivated in this state'.format(prediction),1262 state='State_Name :{}'.format(State_Name1),1263 district='District_Name :{}'.format(District_Name1),1264 year='Crop_Year :{}'.format(Crop_Year1),1265 season='Season :{}'.format(Season1),1266 crop='Crop :{}'.format(Crop1),1267 Area='Area :{} ha'.format(Area_in))1268 #Cotton iint 1269 1270 if State_Name==0 and Crop==115 or State_Name==1 and Crop==115 or State_Name==2 and Crop==115 or State_Name==3 and Crop==115 or State_Name==4 and Crop==115 or State_Name==6 and Crop==115 or State_Name==7 and Crop==115 or State_Name==8 and Crop==115 or State_Name==9 and Crop==115 or State_Name==10 and Crop==115 or State_Name==11 and Crop==115 or State_Name==12 and Crop==115 or State_Name==15 and Crop==115 or State_Name==17 and Crop==115 or State_Name==25 and Crop==115 or State_Name==27 and Crop==115 or State_Name==30 and Crop==115 or State_Name==31 and Crop==115 :1271 prediction=01272 return render_template('index.html',1273 prediction_text='The predicted production of the crop is {} tonne as selected crop is not cultivated in this state'.format(prediction),1274 state='State_Name :{}'.format(State_Name1),1275 district='District_Name :{}'.format(District_Name1),1276 year='Crop_Year :{}'.format(Crop_Year1),1277 season='Season :{}'.format(Season1),1278 crop='Crop :{}'.format(Crop1),1279 Area='Area :{} ha'.format(Area_in))1280 #jowar1281 1282 if State_Name==0 and Crop==102 or State_Name==1 and Crop==102 or State_Name==2 and Crop==102 or State_Name==3 and Crop==102 or State_Name==4 and Crop==102 or State_Name==5 and Crop==102 or State_Name==6 and Crop==102 or State_Name==7 and Crop==102 or State_Name==8 and Crop==102 or State_Name==9 and Crop==102 or State_Name==11 and Crop==102 or State_Name==13 and Crop==102 or State_Name==17 and Crop==102 or State_Name==24 and Crop==102 or State_Name==25 and Crop==102 or State_Name==26 and Crop==102 or State_Name==27 and Crop==102 or State_Name==30 and Crop==102 or State_Name==31 and Crop==102 or State_Name==32 and Crop==102 :1283 prediction=01284 return render_template('index.html',1285 prediction_text='The predicted production of the crop is {} tonne as selected crop is not cultivated in this state'.format(prediction),1286 state='State_Name :{}'.format(State_Name1),1287 district='District_Name :{}'.format(District_Name1),1288 year='Crop_Year :{}'.format(Crop_Year1),1289 season='Season :{}'.format(Season1),1290 crop='Crop :{}'.format(Crop1),1291 Area='Area :{} ha'.format(Area_in))1292 #sunflower1293 1294 if State_Name==0 and Crop==65 or State_Name==1 and Crop==65 or State_Name==3 and Crop==65 or State_Name==5 and Crop==65 or State_Name==6 and Crop==65 or State_Name==7 and Crop==65 or State_Name==8 and Crop==65 or State_Name==9 and Crop==65 or State_Name==11 and Crop==65 or State_Name==14 and Crop==65 or State_Name==16 and Crop==65 or State_Name==17 and Crop==65 or State_Name==19 and Crop==65 or State_Name==24 and Crop==65 or State_Name==27 and Crop==65 or State_Name==30 and Crop==65 or State_Name==31 and Crop==65 or State_Name==32 and Crop==65 :1295 prediction=01296 return render_template('index.html',1297 prediction_text='The predicted production of the crop is {} tonne as selected crop is not cultivated in this state'.format(prediction),1298 state='State_Name :{}'.format(State_Name1),1299 district='District_Name :{}'.format(District_Name1),1300 year='Crop_Year :{}'.format(Crop_Year1),1301 season='Season :{}'.format(Season1),1302 crop='Crop :{}'.format(Crop1),1303 Area='Area :{} ha'.format(Area_in))1304 #Tea1305 1306 if State_Name==0 and Crop==70 or State_Name==1 and Crop==70 or State_Name==3 and Crop==70 or State_Name==5 and Crop==70 or State_Name==6 and Crop==70 or State_Name==7 and Crop==70 or State_Name==8 and Crop==70 or State_Name==11 and Crop==70 or State_Name==12 and Crop==70 or State_Name==13 and Crop==70 or State_Name==14 and Crop==70 or State_Name==16 and Crop==70 or State_Name==17 and Crop==70 or State_Name==19 and Crop==70 or State_Name==20 and Crop==70 or State_Name==21 and Crop==70 or State_Name==22 and Crop==70 or State_Name==23 and Crop==70 or State_Name==26 and Crop==70 or State_Name==27 and Crop==70 or State_Name==29 and Crop==70 or State_Name==30 and Crop==70 or State_Name==31 and Crop==70 :1307 prediction=01308 return render_template('index.html',1309 prediction_text='The predicted production of the crop is {} tonne as selected crop is not cultivated in this state'.format(prediction),1310 state='State_Name :{}'.format(State_Name1),1311 district='District_Name :{}'.format(District_Name1),1312 year='Crop_Year :{}'.format(Crop_Year1),1313 season='Season :{}'.format(Season1),1314 crop='Crop :{}'.format(Crop1),1315 Area='Area :{} ha'.format(Area_in))1316 1317 1318 #Tobacco1319 1320 if State_Name==0 and Crop==78 or State_Name==2 and Crop==78 or State_Name==3 and Crop==78 or State_Name==4 and Crop==78 or State_Name==6 and Crop==78 or State_Name==7 and Crop==78 or State_Name==8 and Crop==78 or State_Name==9 and Crop==78 or State_Name==11 and Crop==78 or State_Name==17 and Crop==78 or State_Name==21 and Crop==78 or State_Name==26 and Crop==78 or State_Name==27 and Crop==78 or State_Name==29 and Crop==78 or State_Name==30 and Crop==78 or State_Name==31 and Crop==78 :1321 prediction=01322 return render_template('index.html',1323 prediction_text='The predicted production of the crop is {} tonne as selected crop is not cultivated in this state'.format(prediction),1324 state='State_Name :{}'.format(State_Name1),1325 district='District_Name :{}'.format(District_Name1),1326 year='Crop_Year :{}'.format(Crop_Year1),1327 season='Season :{}'.format(Season1),1328 crop='Crop :{}'.format(Crop1),1329 Area='Area :{} ha'.format(Area_in))1330 1331 1332 1333 1334 1335 1336 else:1337 return render_template('index.html',prediction_text='The predicted production of the selected crop is {} tonne'.format(prediction),1338 state='State_Name :{}'.format(State_Name1),1339 district='District_Name :{}'.format(District_Name1),1340 year='Crop_Year :{}'.format(Crop_Year1),1341 season='Season :{}'.format(Season1),1342 crop='Crop :{}'.format(Crop1),1343 crop_div='Description of Crop :{}'.format(Crop1),1344 crop_out=crop2,1345 Area='Area :{} ha'.format(Area_in),1346 #prediction_text1='crop production prediction1 {} tonne'.format(prediction1),1347 #prediction_text2='crop production prediction2 {} tonne'.format(prediction2),1348 #prediction_text3='crop production prediction3 {} tonne'.format(prediction3),1349 #prediction_text4='crop production prediction4 {} tonne'.format(prediction4),1350 prediction_text1=prediction1,1351 prediction_text2=prediction2,1352 prediction_text3=prediction,1353 prediction_text4=prediction3,1354 prediction_text5=prediction4,1355 prediction_Area=prediction,1356 prediction_Area1=prediction_Area1,1357 prediction_Area2=prediction_Area2,1358 prediction_Area3=prediction_Area3,1359 prediction_Area4=prediction_Area4,1360 1361 Area_enter=Area,1362 Area1=Area1,1363 Area2=Area2,1364 Area3=Area3,1365 Area4=Area4,1366 crop1=Crop_Year7,1367 crop2=Crop_Year8,1368 crop3=Crop_Year1,1369 crop4=Crop_Year9,1370 crop5=Crop_Year10)1371 #crop2='Crop_Year :{}'.format(Crop_Year8),1372 #crop3='Crop_Year :{}'.format(Crop_Year9),1373 #crop4='Crop_Year :{}'.format(Crop_Year10))1374# In[12]:1375if __name__=="__main__":1376 app.run(debug=True)...
transformer_test_data.py
Source:transformer_test_data.py
...31 def reorientate(self):32 self.calls["reorientate"] += 133 def resize(self, width, height):34 self.calls["resize"].append({"width": width, "height": height})35 def crop(self, left, top, right, bottom):36 self.calls["crop"].append(37 {"left": left, "top": top, "right": right, "bottom": bottom}38 )39 def flip_horizontally(self):40 self.calls["horizontal_flip"] += 141 def flip_vertically(self):42 self.calls["vertical_flip"] += 143 def get_proportional_width(self, new_height):44 width, height = self.size45 return float(new_height) * width / height46 def get_proportional_height(self, new_width):47 width, height = self.size48 return float(new_width) * height / width49 def focus(self, focal_points):...
image_operations.py
Source:image_operations.py
...94 rect = rect.move_to_cover(focal_point)95 # Don't allow the crop box to go over the image boundary96 rect = rect.move_to_clamp(Rect(0, 0, image_width, image_height))97 # Crop!98 willow.crop(rect.round())99 # Get scale for resizing100 # The scale should be the same for both the horizontal and101 # vertical axes102 aftercrop_width, aftercrop_height = willow.get_size()103 scale = self.width / aftercrop_width104 # Only resize if the image is too big105 if scale < 1.0:106 # Resize!107 willow.resize((self.width, self.height))108class MinMaxOperation(Operation):109 def construct(self, size):110 # Get width and height111 width_str, height_str = size.split('x')112 self.width = int(width_str)...
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