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