gpt4free/phind/__init__.py
2023-04-22 13:37:18 +01:00

276 lines
11 KiB
Python

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
cf_clearance = ''
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',
'accept': '*/*',
'accept-language': 'en,fr-FR;q=0.9,fr;q=0.8,es-ES;q=0.7,es;q=0.6,en-US;q=0.5,am;q=0.4,de;q=0.3',
'cookie': f'cf_clearance={cf_clearance}',
'origin': 'https://www.phind.com',
'referer': 'https://www.phind.com/search?q=hi&c=&source=searchbox&init=true',
'sec-ch-ua': '"Chromium";v="112", "Google Chrome";v="112", "Not:A-Brand";v="99"',
'sec-ch-ua-mobile': '?0',
'sec-ch-ua-platform': '"macOS"',
'sec-fetch-dest': 'empty',
'sec-fetch-mode': 'cors',
'sec-fetch-site': 'same-origin',
'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 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',
'accept': '*/*',
'accept-language': 'en,fr-FR;q=0.9,fr;q=0.8,es-ES;q=0.7,es;q=0.6,en-US;q=0.5,am;q=0.4,de;q=0.3',
'content-type': 'application/json',
'cookie': f'cf_clearance={cf_clearance}',
'origin': 'https://www.phind.com',
'referer': 'https://www.phind.com/search?q=hi&c=&source=searchbox&init=true',
'sec-ch-ua': '"Chromium";v="112", "Google Chrome";v="112", "Not:A-Brand";v="99"',
'sec-ch-ua-mobile': '?0',
'sec-ch-ua-platform': '"macOS"',
'sec-fetch-dest': 'empty',
'sec-fetch-mode': 'cors',
'sec-fetch-site': 'same-origin',
'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36',
}
completion = ''
response = post('https://www.phind.com/api/infer/answer', headers = headers, json = json_data, timeout=99999, impersonate='chrome110')
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
}
}
print(cf_clearance)
headers = {
'authority': 'www.phind.com',
'accept': '*/*',
'accept-language': 'en,fr-FR;q=0.9,fr;q=0.8,es-ES;q=0.7,es;q=0.6,en-US;q=0.5,am;q=0.4,de;q=0.3',
'content-type': 'application/json',
'cookie': f'cf_clearance={cf_clearance}',
'origin': 'https://www.phind.com',
'referer': 'https://www.phind.com/search?q=hi&c=&source=searchbox&init=true',
'sec-ch-ua': '"Chromium";v="112", "Google Chrome";v="112", "Not:A-Brand";v="99"',
'sec-ch-ua-mobile': '?0',
'sec-ch-ua-platform': '"macOS"',
'sec-fetch-dest': 'empty',
'sec-fetch-mode': 'cors',
'sec-fetch-site': 'same-origin',
'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36',
}
response = post('https://www.phind.com/api/infer/answer',
headers = headers, json = json_data, timeout=99999, impersonate='chrome110', content_callback=StreamingCompletion.handle_stream_response)
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:
chunk = StreamingCompletion.message_queue.get(timeout=0)
if chunk == b'data: \r\ndata: \r\ndata: \r\n\r\n':
chunk = b'data: \n\n\r\n\r\n'
chunk = chunk.decode()
chunk = chunk.replace('data: \r\n\r\ndata: ', 'data: \n')
chunk = chunk.replace('\r\ndata: \r\ndata: \r\n\r\n', '\n\n\r\n\r\n')
chunk = chunk.replace('data: ', '').replace('\r\n\r\n', '')
yield PhindResponse({
'id' : f'cmpl-1337-{int(time())}',
'object' : 'text_completion',
'created': int(time()),
'model' : model,
'choices': [{
'text' : chunk,
'index' : 0,
'logprobs' : None,
'finish_reason' : 'stop'
}],
'usage': {
'prompt_tokens' : len(prompt),
'completion_tokens' : len(chunk),
'total_tokens' : len(prompt) + len(chunk)
}
})
except Empty:
pass
@staticmethod
def handle_stream_response(response):
StreamingCompletion.message_queue.put(response)