{
    "info": {
        "author": "Michal Szczepanski",
        "author_email": "michal@vane.pl",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 3"
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        "description": "prosecco\n====\n\n[![GitHub](https://img.shields.io/github/license/vane/prosecco)](https://github.com/vane/prosecco/blob/master/LICENSE)\n[![pypi](https://img.shields.io/pypi/v/prosecco)](https://pypi.org/project/prosecco/)\n[![GitHub commits since tagged version](https://img.shields.io/github/commits-since/vane/prosecco/0.0.7)](https://github.com/vane/prosecco/compare/0.0.7...master)\n[![GitHub last commit](https://img.shields.io/github/last-commit/vane/prosecco)](https://github.com/vane/prosecco)\n\n\n## Description\n\nNLP engine with text extraction capabilities that can be easily extended to desired needs.\n\nCan be used to build chat bots, question answer machines (see [example/qa.py](https://github.com/vane/prosecco/blob/master/example/qa.py)), text converters.\n\nExtract words or even whole sentences in ordered manner.  \nGet position of found text.  \nUse ```Condition``` class and mark data using regex or string comparasion.  \nExtend each part of it in easy manner. ( see [example/custom_condition_class.py](https://github.com/vane/prosecco/blob/master/example/custom_condition_class.py)).\n\n## Install\n```bash\npip install prosecco\n```\n## Usage\n\n### Basic\n[example/basic.py](https://github.com/vane/prosecco/blob/master/example/basic.py)\n```python\nfrom prosecco import Prosecco, Condition, EnglishWordNormalizer\n\n# Read wikipedia https://en.wikipedia.org/wiki/Superhero\nwith open(\"superhero.txt\") as f:\n    text = f.read()\n\n# 1. Create conditions with hero names\nconditions = [\n    Condition(lemma_type=\"hero|dc\", compare=[\"batman\", \"superman\", \"wonder woman\"], lower=True),\n    Condition(lemma_type=\"hero|marvel\", normalizer=EnglishWordNormalizer(),\n              compare=[\"spiderman\", \"iron man\", \"black panther\"], lower=True)\n]\n# 2. Create prosecco\np = Prosecco(conditions=conditions)\n# 3. Let's drink and print output\np.drink(text, progress=True)\nlemmas  = set(p.get_lemmas(type=\"hero\"))\nprint(\" \".join(map(str, lemmas)))\n```\n\n### Output\n```Batman[hero|dc][start:1089] Wonder Woman[hero|dc][start:2100] Iron Man[hero|marvel][start:2184] Superman[hero|dc][start:2070] Spider-Man[hero|marvel][start:2080] Black Panther[hero|marvel][start:17690]```\n\n### Advanced\n[example/advanced.py](https://github.com/vane/prosecco/blob/master/example/advanced.py)\n```python\nfrom prosecco import *\n\ntext = \"\"\"Chrz\u0105szcz brzmi w trzcinie w Szczebrzeszynie.\nZ\u0105b zupa z\u0119bowa, d\u0105b zupa d\u0119bowa.\nGdzie Rzym, gdzie Krym. W Pacanowie kozy kuj\u0105.\nTak, je\u015bli mam szcz\u0119\u015bliwy by\u0107, to w Gda\u0144sku musz\u0119 \u017cy\u0107! \n\"\"\"\n\n# 1. Create condition with city names\ncities = [\"szczebrzeszyn\", \"pacanow\", \"gdansk\", \"rzym\", \"krym\"]\nanimals = [\"koz\", \"chrzaszcz\"]\n# 2. Normalizer to remove polish specific charset\nn = CharsetNormalizer(Charset.PL_EN)\n# 3. Stemmer to remove suffix\ns = SuffixStemmer(language=\"pl\")\n# 4. Conditions for city and animal\ncity_condition = Condition(lemma_type=\"city\", compare=cities, normalizer=n, stemmer=s, lower=True)\nanimal_condition = Condition(lemma_type=\"animal\", compare=animals, normalizer=n, stemmer=s, lower=True)\nconditions = [city_condition, animal_condition]\n# 5. Create tokenizer for polish charset\ntokenizer = LanguageTokenizer(Charset.PL)\n# 6. Get list of tokens\ntokens = tokenizer.tokenize(text)\n# 7. Create visitor with conditions provided in step 1\nvisitor = Visitor(conditions=conditions)\n# 8. Parse tokens based on visitor conditions\nlexer = Lexer(tokens=tokens, visitor=visitor)\n# 9. Get list of lemmas\nlemmas = lexer.lex()\n# 10. filter found cities and print output\nfound = filter(lambda l: l.type == \"city\", lemmas)\nprint(\" \".join(map(str, found)))\n# 11. filter found anumals and print output\nfound = list(filter(lambda l: l.type == \"animal\", lemmas))\nprint(\" \".join(map(str, found)))\n# 12. print exact words from text\nfor l in list(found):\n    print(text[l.start:l.start+len(l.sentence)])\n```   \n\n### Output\n```bash\nSzczebrzeszynie[city][start:29] Rzym[city][start:86] Krym[city][start:98] Pacanowie[city][start:106] Gda\u0144sku[city][start:163]\nChrz\u0105szcz[animal][start:0] kozy[animal][start:116]\nChrz\u0105szcz\nkozy\n```\n\n### QA ( question answer machine )\n[example/qa.py](https://github.com/vane/prosecco/blob/master/example/qa.py)\n```python\nfrom datetime import datetime\nfrom prosecco import Prosecco, Condition, EnglishWordNormalizer, SuffixStemmer\n\n\nmessages = \"\"\"Whats the time ?\nHow long boil egg?\n100 miles to km\n30,3 celsius to farenheit\"\"\"\n\n# create conditions\nquestion = (\"what\", \"whats\", \"how\")\nmeasure = (\"celsius\", \"farenheit\", \"mile\", \"km\", \"kilometer\", \"time\", \"long\")\ncooking = (\"boil\",\"cook\", \"fry\")\nfood = (\"egg\",)\nconditions = [\n    Condition(lemma_type=\"question\", compare=question, normalizer=EnglishWordNormalizer(), lower=True),\n    Condition(lemma_type=\"measure\", compare=measure,\n              normalizer=EnglishWordNormalizer(),\n              stemmer=SuffixStemmer(language=\"en\"),\n              lower=True),\n    Condition(lemma_type=\"cooking\", compare=cooking, normalizer=EnglishWordNormalizer(), lower=True),\n    Condition(lemma_type=\"food\", compare=food,\n              normalizer=EnglishWordNormalizer(),\n              stemmer=SuffixStemmer(language=\"en\"),\n              lower=True),\n    Condition(lemma_type=\"number\", compare=r\"\\d+([\\.\\,]\\d+)?\", regex=True, until_character=\" \"),\n]\n\ndef printer(data):\n    print(\"Robot : \", data)\n\n# time condition\ndef resolve_time(p):\n    printer(datetime.now())\n\n# cooking condition\ndef resolve_cooking(p):\n    if check_condition(p.get_lemmas(\"cooking|food\"), [\"boil\", \"egg\"]):\n        printer(\"\"\"\nHard for 9-15 minutes.\nSoft for 6-8 minutes.\"\"\")\n        return True\n\ndef resolve_measure(p):\n    measures = p.get_lemmas(\"measure\")\n    fr = measures[0]\n    to = measures[1]\n    numbers = p.get_lemmas(\"number\")\n    if len(numbers) == 0:\n        printer(\"No number for conversion provided\")\n        return True\n    value = float(numbers[0].sentence.replace(\",\", \".\"))\n    if fr.condition == \"mile\" and to.condition == \"km\":\n        printer(value / 0.62137119)\n        return True\n    elif fr.condition == \"km\" and to.condition == \"mile\":\n        printer(value * 0.62137119)\n        return True\n    elif fr.condition == \"celsius\" and to.condition == \"farenheit\":\n        print(9/5 * value + 32)\n        return True\n    elif fr.condition == \"farenheit\" and to.condition == \"celsius\":\n        print((value - 32) * 5/9)\n        return True\n    return False\n\ndef check_condition(lemmas, conditions):\n    for l in lemmas:\n        for c in conditions:\n            if l.condition == c:\n                conditions.remove(c)\n    return len(conditions) == 0\n\ndef resolve(p, m):\n    if len(p.get_lemmas(\"question\")) > 0:\n        if check_condition(p.get_lemmas(\"measure\"), [\"time\"]):\n            resolve_time(p)\n            return True\n        elif len(p.get_lemmas(\"cooking\")) > 0:\n            return resolve_cooking(p)\n    elif len(p.get_lemmas(\"measure\")) > 0:\n        return resolve_measure(p)\n    return False\n\nfor m in messages.split('\\n'):\n    print(\"Question : \", m)\n    p = Prosecco(conditions=conditions)\n    p.drink(m)\n    if not resolve(p, m):\n        print(\"Unsupported resolver : \", p.lemmas)\n```\n\n### Output\n\n```bash\nQuestion :  Whats the time ?\nRobot :  2019-08-13 20:38:06.948720\nQuestion :  How long boil egg?\nRobot :  \nHard for 9-15 minutes.\nSoft for 6-8 minutes.\nQuestion :  100 miles to km\nRobot :  160.93440057946685\nQuestion :  30,3 celsius to farenheit\n86.53999999999999\n```\n\n\n",
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