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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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cf_clearance = '' |
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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' |
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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: |
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if user_agent == '': |
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raise ValueError('user_agent must be set, refer to documentation') |
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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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'accept': '*/*', |
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'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', |
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'cookie': f'cf_clearance={cf_clearance}', |
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'origin': 'https://www.phind.com', |
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'referer': 'https://www.phind.com/search?q=hi&c=&source=searchbox&init=true', |
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'sec-ch-ua': '"Chromium";v="112", "Google Chrome";v="112", "Not:A-Brand";v="99"', |
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'sec-ch-ua-mobile': '?0', |
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'sec-ch-ua-platform': '"macOS"', |
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'sec-fetch-dest': 'empty', |
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'sec-fetch-mode': 'cors', |
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'sec-fetch-site': 'same-origin', |
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'user-agent': user_agent |
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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 user_agent == '': |
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raise ValueError('user_agent must be set, refer to documentation') |
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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, |
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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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'accept': '*/*', |
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'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', |
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'content-type': 'application/json', |
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'cookie': f'cf_clearance={cf_clearance}', |
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'origin': 'https://www.phind.com', |
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'referer': 'https://www.phind.com/search?q=hi&c=&source=searchbox&init=true', |
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'sec-ch-ua': '"Chromium";v="112", "Google Chrome";v="112", "Not:A-Brand";v="99"', |
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'sec-ch-ua-mobile': '?0', |
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'sec-ch-ua-platform': '"macOS"', |
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'sec-fetch-dest': 'empty', |
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'sec-fetch-mode': 'cors', |
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'sec-fetch-site': 'same-origin', |
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'user-agent': user_agent |
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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, impersonate='chrome110') |
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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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headers = { |
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'authority': 'www.phind.com', |
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'accept': '*/*', |
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'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', |
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'content-type': 'application/json', |
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'cookie': f'cf_clearance={cf_clearance}', |
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'origin': 'https://www.phind.com', |
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'referer': 'https://www.phind.com/search?q=hi&c=&source=searchbox&init=true', |
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'sec-ch-ua': '"Chromium";v="112", "Google Chrome";v="112", "Not:A-Brand";v="99"', |
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'sec-ch-ua-mobile': '?0', |
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'sec-ch-ua-platform': '"macOS"', |
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'sec-fetch-dest': 'empty', |
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'sec-fetch-mode': 'cors', |
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'sec-fetch-site': 'same-origin', |
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'user-agent': user_agent |
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} |
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response = post('https://www.phind.com/api/infer/answer', |
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headers = headers, json = json_data, timeout=99999, impersonate='chrome110', content_callback=StreamingCompletion.handle_stream_response) |
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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 user_agent == '': |
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raise ValueError('user_agent must be set, refer to documentation') |
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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) |