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from urllib.parse import quote
from time import time
from datetime import datetime
from queue import Queue, Empty
from threading import Thread
from re import findall
from curl_cffi.requests import post
class PhindResponse:
class Completion:
class Choices:
def __init__(self, choice: dict) -> None:
self.text = choice['text']
self.content = self.text.encode()
self.index = choice['index']
self.logprobs = choice['logprobs']
self.finish_reason = choice['finish_reason']
def __repr__(self) -> str:
return f'''<__main__.APIResponse.Completion.Choices(\n text = {self.text.encode()},\n index = {self.index},\n logprobs = {self.logprobs},\n finish_reason = {self.finish_reason})object at 0x1337>'''
def __init__(self, choices: dict) -> None:
self.choices = [self.Choices(choice) for choice in choices]
class Usage:
def __init__(self, usage_dict: dict) -> None:
self.prompt_tokens = usage_dict['prompt_tokens']
self.completion_tokens = usage_dict['completion_tokens']
self.total_tokens = usage_dict['total_tokens']
def __repr__(self):
return f'''<__main__.APIResponse.Usage(\n prompt_tokens = {self.prompt_tokens},\n completion_tokens = {self.completion_tokens},\n total_tokens = {self.total_tokens})object at 0x1337>'''
def __init__(self, response_dict: dict) -> None:
self.response_dict = response_dict
self.id = response_dict['id']
self.object = response_dict['object']
self.created = response_dict['created']
self.model = response_dict['model']
self.completion = self.Completion(response_dict['choices'])
self.usage = self.Usage(response_dict['usage'])
def json(self) -> dict:
return self.response_dict
class Search:
def create(prompt: str, actualSearch: bool = True, language: str = 'en') -> dict: # None = no search
if not actualSearch:
return {
'_type': 'SearchResponse',
'queryContext': {
'originalQuery': prompt
},
'webPages': {
'webSearchUrl': f'https://www.bing.com/search?q={quote(prompt)}',
'totalEstimatedMatches': 0,
'value': []
},
'rankingResponse': {
'mainline': {
'items': []
}
}
}
headers = {
'authority' : 'www.phind.com',
'origin' : 'https://www.phind.com',
'referer' : 'https://www.phind.com/search',
'user-agent' : 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110.0.0.0 Safari/537.36',
}
return post('https://www.phind.com/api/bing/search', headers = headers, json = {
'q': prompt,
'userRankList': {},
'browserLanguage': language}).json()['rawBingResults']
class Completion:
def create(
model = 'gpt-4',
prompt: str = '',
results: dict = None,
creative: bool = False,
detailed: bool = False,
codeContext: str = '',
language: str = 'en') -> PhindResponse:
if results is None:
results = Search.create(prompt, actualSearch = True)
if len(codeContext) > 2999:
raise ValueError('codeContext must be less than 3000 characters')
models = {
'gpt-4' : 'expert',
'gpt-3.5-turbo' : 'intermediate',
'gpt-3.5': 'intermediate',
}
json_data = {
'question' : prompt,
'bingResults' : results, #response.json()['rawBingResults'],
'codeContext' : codeContext,
'options': {
'skill' : models[model],
'date' : datetime.now().strftime("%d/%m/%Y"),
'language': language,
'detailed': detailed,
'creative': creative
}
}
headers = {
'authority' : 'www.phind.com',
'origin' : 'https://www.phind.com',
'referer' : f'https://www.phind.com/search?q={quote(prompt)}&c=&source=searchbox&init=true',
'user-agent' : 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110.0.0.0 Safari/537.36',
}
completion = ''
response = post('https://www.phind.com/api/infer/answer', headers = headers, json = json_data, timeout=99999)
for line in response.text.split('\r\n\r\n'):
completion += (line.replace('data: ', ''))
return PhindResponse({
'id' : f'cmpl-1337-{int(time())}',
'object' : 'text_completion',
'created': int(time()),
'model' : models[model],
'choices': [{
'text' : completion,
'index' : 0,
'logprobs' : None,
'finish_reason' : 'stop'
}],
'usage': {
'prompt_tokens' : len(prompt),
'completion_tokens' : len(completion),
'total_tokens' : len(prompt) + len(completion)
}
})
class StreamingCompletion:
message_queue = Queue()
stream_completed = False
def request(model, prompt, results, creative, detailed, codeContext, language) -> None:
models = {
'gpt-4' : 'expert',
'gpt-3.5-turbo' : 'intermediate',
'gpt-3.5': 'intermediate',
}
json_data = {
'question' : prompt,
'bingResults' : results,
'codeContext' : codeContext,
'options': {
'skill' : models[model],
'date' : datetime.now().strftime("%d/%m/%Y"),
'language': language,
'detailed': detailed,
'creative': creative
}
}
stream_req = post('https://www.phind.com/api/infer/answer', json=json_data, timeout=99999,
content_callback = StreamingCompletion.handle_stream_response,
headers = {
'authority' : 'www.phind.com',
'origin' : 'https://www.phind.com',
'referer' : f'https://www.phind.com/search?q={quote(prompt)}&c=&source=searchbox&init=true',
'user-agent' : 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110.0.0.0 Safari/537.36',
})
StreamingCompletion.stream_completed = True
@staticmethod
def create(
model : str = 'gpt-4',
prompt : str = '',
results : dict = None,
creative : bool = False,
detailed : bool = False,
codeContext : str = '',
language : str = 'en'):
if results is None:
results = Search.create(prompt, actualSearch = True)
if len(codeContext) > 2999:
raise ValueError('codeContext must be less than 3000 characters')
Thread(target = StreamingCompletion.request, args = [
model, prompt, results, creative, detailed, codeContext, language]).start()
while StreamingCompletion.stream_completed != True or not StreamingCompletion.message_queue.empty():
try:
message = StreamingCompletion.message_queue.get(timeout=0)
for token in findall(r'(?<=data: )(.+?)(?=\r\n\r\n)', message.decode()):
yield PhindResponse({
'id' : f'cmpl-1337-{int(time())}',
'object' : 'text_completion',
'created': int(time()),
'model' : model,
'choices': [{
'text' : token,
'index' : 0,
'logprobs' : None,
'finish_reason' : 'stop'
}],
'usage': {
'prompt_tokens' : len(prompt),
'completion_tokens' : len(token),
'total_tokens' : len(prompt) + len(token)
}
})
except Empty:
pass
@staticmethod
def handle_stream_response(response):
StreamingCompletion.message_queue.put(response)
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