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<main>
<article id="content">
<header>
<h1 class="title">Module <code>ktrain.text.textextractor</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">from ..imports import *
from . import textutils as TU
class TextExtractor:
"""
```
Text Extractor: a wrapper to textract package
```
"""
def __init__(self):
try:
import textract
except ImportError:
raise Exception('TextExtractor requires textract: pip install textract')
self.process = textract.process
def extract(self, filename=None, text=None,return_format='document', lang=None):
"""
```
Extracts text from document given file path to document.
filename(str): path to file, Mutually-exclusive with text.
text(str): string to tokenize. Mutually-exclusive with filename.
The extract method can also simply accept a string and return lists of sentences or paragraphs.
return_format(str): One of {'document', 'paragraphs', 'sentences'}
'document': returns text of document
'paragraphs': returns a list of paragraphs from document
'sentences': returns a list of sentences from document
lang(str): language code. If None, lang will be detected from extracted text
```
"""
if filename is None and text is None:
raise ValueError('Either the filename parameter or the text parameter must be supplied')
if filename is not None and text is not None:
raise ValueError('The filename and text parameters are mutually-exclusive.')
if return_format not in ['document', 'paragraphs', 'sentences']:
raise ValueError('return_format must be one of {"document", "paragraphs", "sentences"}')
if filename is not None:
mtype = TU.get_mimetype(filename)
try:
if mtype and mtype.split('/')[0] == 'text':
with open(filename, 'r') as f:
text = f.read()
text = str.encode(text)
else:
text = self.process(filename)
except Exception as e:
if verbose:
print('ERROR on %s:\n%s' % (filename, e))
try:
text = text.decode(errors='ignore')
except:
pass
if return_format == 'sentences':
return TU.sent_tokenize(text, lang=lang)
elif return_format == 'paragraphs':
return TU.paragraph_tokenize(text, join_sentences=True, lang=lang)
else:
return text</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="ktrain.text.textextractor.TextExtractor"><code class="flex name class">
<span>class <span class="ident">TextExtractor</span></span>
</code></dt>
<dd>
<div class="desc"><pre><code>Text Extractor: a wrapper to textract package
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class TextExtractor:
"""
```
Text Extractor: a wrapper to textract package
```
"""
def __init__(self):
try:
import textract
except ImportError:
raise Exception('TextExtractor requires textract: pip install textract')
self.process = textract.process
def extract(self, filename=None, text=None,return_format='document', lang=None):
"""
```
Extracts text from document given file path to document.
filename(str): path to file, Mutually-exclusive with text.
text(str): string to tokenize. Mutually-exclusive with filename.
The extract method can also simply accept a string and return lists of sentences or paragraphs.
return_format(str): One of {'document', 'paragraphs', 'sentences'}
'document': returns text of document
'paragraphs': returns a list of paragraphs from document
'sentences': returns a list of sentences from document
lang(str): language code. If None, lang will be detected from extracted text
```
"""
if filename is None and text is None:
raise ValueError('Either the filename parameter or the text parameter must be supplied')
if filename is not None and text is not None:
raise ValueError('The filename and text parameters are mutually-exclusive.')
if return_format not in ['document', 'paragraphs', 'sentences']:
raise ValueError('return_format must be one of {"document", "paragraphs", "sentences"}')
if filename is not None:
mtype = TU.get_mimetype(filename)
try:
if mtype and mtype.split('/')[0] == 'text':
with open(filename, 'r') as f:
text = f.read()
text = str.encode(text)
else:
text = self.process(filename)
except Exception as e:
if verbose:
print('ERROR on %s:\n%s' % (filename, e))
try:
text = text.decode(errors='ignore')
except:
pass
if return_format == 'sentences':
return TU.sent_tokenize(text, lang=lang)
elif return_format == 'paragraphs':
return TU.paragraph_tokenize(text, join_sentences=True, lang=lang)
else:
return text</code></pre>
</details>
<h3>Methods</h3>
<dl>
<dt id="ktrain.text.textextractor.TextExtractor.extract"><code class="name flex">
<span>def <span class="ident">extract</span></span>(<span>self, filename=None, text=None, return_format='document', lang=None)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Extracts text from document given file path to document.
filename(str): path to file, Mutually-exclusive with text.
text(str): string to tokenize. Mutually-exclusive with filename.
The extract method can also simply accept a string and return lists of sentences or paragraphs.
return_format(str): One of {'document', 'paragraphs', 'sentences'}
'document': returns text of document
'paragraphs': returns a list of paragraphs from document
'sentences': returns a list of sentences from document
lang(str): language code. If None, lang will be detected from extracted text
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def extract(self, filename=None, text=None,return_format='document', lang=None):
"""
```
Extracts text from document given file path to document.
filename(str): path to file, Mutually-exclusive with text.
text(str): string to tokenize. Mutually-exclusive with filename.
The extract method can also simply accept a string and return lists of sentences or paragraphs.
return_format(str): One of {'document', 'paragraphs', 'sentences'}
'document': returns text of document
'paragraphs': returns a list of paragraphs from document
'sentences': returns a list of sentences from document
lang(str): language code. If None, lang will be detected from extracted text
```
"""
if filename is None and text is None:
raise ValueError('Either the filename parameter or the text parameter must be supplied')
if filename is not None and text is not None:
raise ValueError('The filename and text parameters are mutually-exclusive.')
if return_format not in ['document', 'paragraphs', 'sentences']:
raise ValueError('return_format must be one of {"document", "paragraphs", "sentences"}')
if filename is not None:
mtype = TU.get_mimetype(filename)
try:
if mtype and mtype.split('/')[0] == 'text':
with open(filename, 'r') as f:
text = f.read()
text = str.encode(text)
else:
text = self.process(filename)
except Exception as e:
if verbose:
print('ERROR on %s:\n%s' % (filename, e))
try:
text = text.decode(errors='ignore')
except:
pass
if return_format == 'sentences':
return TU.sent_tokenize(text, lang=lang)
elif return_format == 'paragraphs':
return TU.paragraph_tokenize(text, join_sentences=True, lang=lang)
else:
return text</code></pre>
</details>
</dd>
</dl>
</dd>
</dl>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="ktrain.text" href="index.html">ktrain.text</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="ktrain.text.textextractor.TextExtractor" href="#ktrain.text.textextractor.TextExtractor">TextExtractor</a></code></h4>
<ul class="">
<li><code><a title="ktrain.text.textextractor.TextExtractor.extract" href="#ktrain.text.textextractor.TextExtractor.extract">extract</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
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