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)