LSWenda/llms/gpt4free/phind/__init__.py

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

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',
            'origin'       : 'https://www.phind.com',
            'referer'      : 'https://www.phind.com/search',
            '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',
        }
        
        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',
            'origin'       : 'https://www.phind.com',
            'referer'      : f'https://www.phind.com/search?q={quote(prompt)}&c=&source=searchbox&init=true',
            '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',
        }
        
        completion = ''
        response   = post('https://www.phind.com/api/infer/answer', headers = headers, json = json_data, timeout=99999)
        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
            }
        }
        
        stream_req = post('https://www.phind.com/api/infer/answer', json=json_data, timeout=99999,
            content_callback = StreamingCompletion.handle_stream_response,
            headers = {
                'authority'    : 'www.phind.com',
                'origin'       : 'https://www.phind.com',
                'referer'      : f'https://www.phind.com/search?q={quote(prompt)}&c=&source=searchbox&init=true',
                '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',
        })

        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)