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### Example: `forefront` (use like openai pypi package) <a name="example-forefront"></a> | ||
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```python | ||
from gpt4free import forefront | ||
# create an account | ||
token = forefront.Account.create(logging=False) | ||
print(token) | ||
# get a response | ||
for response in forefront.StreamingCompletion.create(token=token, | ||
prompt='hello world', model='gpt-4'): | ||
print(response.completion.choices[0].text, end='') | ||
print("") | ||
``` |
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from json import loads | ||
from re import findall | ||
from time import time, sleep | ||
from typing import Generator, Optional | ||
from uuid import uuid4 | ||
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from fake_useragent import UserAgent | ||
from requests import post | ||
from pymailtm import MailTm, Message | ||
from tls_client import Session | ||
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from .typing import ForeFrontResponse | ||
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class Account: | ||
@staticmethod | ||
def create(proxy: Optional[str] = None, logging: bool = False): | ||
proxies = {'http': 'http://' + proxy, 'https': 'http://' + proxy} if proxy else False | ||
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start = time() | ||
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mail_client = MailTm().get_account() | ||
mail_address = mail_client.address | ||
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client = Session(client_identifier='chrome110') | ||
client.proxies = proxies | ||
client.headers = { | ||
'origin': 'https://accounts.forefront.ai', | ||
'user-agent': UserAgent().random, | ||
} | ||
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response = client.post( | ||
'https://clerk.forefront.ai/v1/client/sign_ups?_clerk_js_version=4.38.4', | ||
data={'email_address': mail_address}, | ||
) | ||
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try: | ||
trace_token = response.json()['response']['id'] | ||
if logging: | ||
print(trace_token) | ||
except KeyError: | ||
return 'Failed to create account!' | ||
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response = client.post( | ||
f'https://clerk.forefront.ai/v1/client/sign_ups/{trace_token}/prepare_verification?_clerk_js_version=4.38.4', | ||
data={ | ||
'strategy': 'email_link', | ||
'redirect_url': 'https://accounts.forefront.ai/sign-up/verify' | ||
}, | ||
) | ||
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if logging: | ||
print(response.text) | ||
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if 'sign_up_attempt' not in response.text: | ||
return 'Failed to create account!' | ||
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while True: | ||
sleep(1) | ||
new_message: Message = mail_client.wait_for_message() | ||
if logging: | ||
print(new_message.data['id']) | ||
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verification_url = findall(r'https:\/\/clerk\.forefront\.ai\/v1\/verify\?token=\w.+', new_message.text)[0] | ||
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if verification_url: | ||
break | ||
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if logging: | ||
print(verification_url) | ||
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response = client.get(verification_url) | ||
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response = client.get('https://clerk.forefront.ai/v1/client?_clerk_js_version=4.38.4') | ||
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token = response.json()['response']['sessions'][0]['last_active_token']['jwt'] | ||
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with open('accounts.txt', 'a') as f: | ||
f.write(f'{mail_address}:{token}\n') | ||
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if logging: | ||
print(time() - start) | ||
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return token | ||
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class StreamingCompletion: | ||
@staticmethod | ||
def create( | ||
token=None, | ||
chat_id=None, | ||
prompt='', | ||
action_type='new', | ||
default_persona='607e41fe-95be-497e-8e97-010a59b2e2c0', # default | ||
model='gpt-4', | ||
proxy=None | ||
) -> Generator[ForeFrontResponse, None, None]: | ||
if not token: | ||
raise Exception('Token is required!') | ||
if not chat_id: | ||
chat_id = str(uuid4()) | ||
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proxies = { 'http': 'http://' + proxy, 'https': 'http://' + proxy } if proxy else None | ||
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headers = { | ||
'authority': 'chat-server.tenant-forefront-default.knative.chi.coreweave.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', | ||
'authorization': 'Bearer ' + token, | ||
'cache-control': 'no-cache', | ||
'content-type': 'application/json', | ||
'origin': 'https://chat.forefront.ai', | ||
'pragma': 'no-cache', | ||
'referer': 'https://chat.forefront.ai/', | ||
'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': 'cross-site', | ||
'user-agent': UserAgent().random, | ||
} | ||
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json_data = { | ||
'text': prompt, | ||
'action': action_type, | ||
'parentId': chat_id, | ||
'workspaceId': chat_id, | ||
'messagePersona': default_persona, | ||
'model': model, | ||
} | ||
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for chunk in post( | ||
'https://chat-server.tenant-forefront-default.knative.chi.coreweave.com/chat', | ||
headers=headers, | ||
proxies=proxies, | ||
json=json_data, | ||
stream=True, | ||
).iter_lines(): | ||
if b'finish_reason":null' in chunk: | ||
data = loads(chunk.decode('utf-8').split('data: ')[1]) | ||
token = data['choices'][0]['delta'].get('content') | ||
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if token is not None: | ||
yield ForeFrontResponse( | ||
**{ | ||
'id': chat_id, | ||
'object': 'text_completion', | ||
'created': int(time()), | ||
'text': token, | ||
'model': model, | ||
'choices': [{'text': token, 'index': 0, 'logprobs': None, 'finish_reason': 'stop'}], | ||
'usage': { | ||
'prompt_tokens': len(prompt), | ||
'completion_tokens': len(token), | ||
'total_tokens': len(prompt) + len(token), | ||
}, | ||
} | ||
) | ||
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class Completion: | ||
@staticmethod | ||
def create( | ||
token=None, | ||
chat_id=None, | ||
prompt='', | ||
action_type='new', | ||
default_persona='607e41fe-95be-497e-8e97-010a59b2e2c0', # default | ||
model='gpt-4', | ||
proxy=None | ||
) -> ForeFrontResponse: | ||
text = '' | ||
final_response = None | ||
for response in StreamingCompletion.create( | ||
token=token, | ||
chat_id=chat_id, | ||
prompt=prompt, | ||
action_type=action_type, | ||
default_persona=default_persona, | ||
model=model, | ||
proxy=proxy | ||
): | ||
if response: | ||
final_response = response | ||
text += response.text | ||
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if final_response: | ||
final_response.text = text | ||
else: | ||
raise Exception('Unable to get the response, Please try again') | ||
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return final_response | ||
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from typing import Any, List | ||
from pydantic import BaseModel | ||
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class Choice(BaseModel): | ||
text: str | ||
index: int | ||
logprobs: Any | ||
finish_reason: str | ||
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class Usage(BaseModel): | ||
prompt_tokens: int | ||
completion_tokens: int | ||
total_tokens: int | ||
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class ForeFrontResponse(BaseModel): | ||
id: str | ||
object: str | ||
created: int | ||
model: str | ||
choices: List[Choice] | ||
usage: Usage | ||
text: str |