kopia lustrzana https://github.com/thinkst/zippy
78 wiersze
2.4 KiB
Python
78 wiersze
2.4 KiB
Python
#!/usr/bin/env python3
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import requests, re, os
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from typing import Optional, Dict, Tuple
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API_KEY = os.getenv('GPTZERO_APIKEY')
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API_URL = 'https://api.gptzero.me/v2/predict/text'
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def make_req(text : str) -> Optional[Dict]:
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headers = {
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'X-Api-Key': API_KEY
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}
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data = {
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'document': text,
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}
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res = requests.post(API_URL, headers=headers, json=data)
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if res.status_code != 200:
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print(res.text)
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return [None]
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return res.json().get('documents', [None])[0]
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def classify_text(s : str) -> Optional[Tuple[str, float]]:
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res = make_req(s)
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if res is None:
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print("Unable to classify!")
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return None
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else:
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#print(res)
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if res.get('average_generated_prob') > 0.5:
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return ('AI', res.get('completely_generated_prob'))
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else:
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return ('Human', 1 - res.get('completely_generated_prob'))
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def run_on_file_chunked(filename : str, chunk_size : int = 1025) -> Optional[Tuple[str, float]]:
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'''
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Given a filename (and an optional chunk size) returns the score for the contents of that file.
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This function chunks the file into at most chunk_size parts to score separately, then returns an average. This prevents a very large input
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overwhelming the model.
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'''
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with open(filename, 'r') as fp:
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contents = fp.read()
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return run_on_text_chunked(contents, chunk_size)
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def run_on_text_chunked(contents : str, chunk_size : int = 1025) -> Optional[Tuple[str, float]]:
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'''
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Given a text (and an optional chunk size) returns the score for the contents of that string.
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This function chunks the string into at most chunk_size parts to score separately, then returns an average. This prevents a very large input
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overwhelming the model.
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'''
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# Remove extra spaces and duplicate newlines.
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contents = re.sub(' +', ' ', contents)
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contents = re.sub('\t', '', contents)
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contents = re.sub('\n+', '\n', contents)
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contents = re.sub('\n ', '\n', contents)
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start = 0
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end = 0
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chunks = []
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while start + chunk_size < len(contents) and end != -1:
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end = contents.rfind(' ', start, start + chunk_size + 1)
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chunks.append(contents[start:end])
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start = end + 1
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chunks.append(contents[start:])
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scores = []
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for c in chunks:
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scores.append(classify_text(c))
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ssum : float = 0.0
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for s in scores:
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if s[0] == 'AI':
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ssum -= s[1]
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else:
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ssum += s[1]
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sa : float = ssum / len(scores)
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if sa < 0:
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return ('AI', abs(sa))
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else:
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return ('Human', abs(sa)) |