path: root/phind/__init__.py

from datetime import datetime
from queue import Queue, Empty
from threading import Thread
from time import time
from urllib.parse import quote

from curl_cffi.requests import post

cf_clearance = ''
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'


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 = list(map(self.Choices, 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 user_agent == '':
            raise ValueError('user_agent must be set, refer to documentation')
        if cf_clearance == '':
            raise ValueError('cf_clearance must be set, refer to documentation')

        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': user_agent
        }

        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 user_agent == '':
            raise ValueError('user_agent must be set, refer to documentation')

        if cf_clearance == '':
            raise ValueError('cf_clearance must be set, refer to documentation')

        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': user_agent
        }

        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
            }
        }

        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': user_agent
        }

        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 user_agent == '':
            raise ValueError('user_agent must be set, refer to documentation')
        if cf_clearance == '':
            raise ValueError('cf_clearance must be set, refer to documentation')

        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)