mirror of https://github.com/xtekky/gpt4free
Improve providers
Heiner Lohaus
8 months ago
parent
b268771baa
commit
e4caf1d0ca
@ -1,101 +1,158 @@
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from __future__ import annotations
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import json
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import random
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import requests
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import time
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from ...typing import Any, CreateResult, Messages
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from ...typing import CreateResult, Messages
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from ..base_provider import BaseProvider
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from ..helper import format_prompt
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from ..helper import WebDriver, format_prompt, get_browser
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models = {
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"theb-ai": "TheB.AI",
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"theb-ai-free": "TheB.AI Free",
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"gpt-3.5-turbo": "GPT-3.5 Turbo (New)",
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"gpt-3.5-turbo-16k": "GPT-3.5-16K",
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"gpt-4-turbo": "GPT-4 Turbo",
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"gpt-4": "GPT-4",
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"gpt-4-32k": "GPT-4 32K",
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"claude-2": "Claude 2",
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"claude-instant-1": "Claude Instant 1.2",
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"palm-2": "PaLM 2",
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"palm-2-32k": "PaLM 2 32K",
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"palm-2-codey": "Codey",
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"palm-2-codey-32k": "Codey 32K",
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"vicuna-13b-v1.5": "Vicuna v1.5 13B",
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"llama-2-7b-chat": "Llama 2 7B",
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"llama-2-13b-chat": "Llama 2 13B",
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"llama-2-70b-chat": "Llama 2 70B",
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"code-llama-7b": "Code Llama 7B",
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"code-llama-13b": "Code Llama 13B",
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"code-llama-34b": "Code Llama 34B",
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"qwen-7b-chat": "Qwen 7B"
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}
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class Theb(BaseProvider):
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url = "https://theb.ai"
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working = True
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supports_stream = True
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supports_gpt_35_turbo = True
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needs_auth = True
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url = "https://beta.theb.ai"
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working = True
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supports_gpt_35_turbo = True
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supports_gpt_4 = True
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supports_stream = True
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@staticmethod
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@classmethod
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def create_completion(
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cls,
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model: str,
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messages: Messages,
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stream: bool,
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proxy: str = None,
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browser: WebDriver = None,
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headless: bool = True,
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**kwargs
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) -> CreateResult:
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auth = kwargs.get("auth", {
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"bearer_token":"free",
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"org_id":"theb",
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})
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bearer_token = auth["bearer_token"]
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org_id = auth["org_id"]
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if model in models:
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model = models[model]
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prompt = format_prompt(messages)
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driver = browser if browser else get_browser(None, headless, proxy)
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headers = {
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'authority': 'beta.theb.ai',
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'accept': 'text/event-stream',
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'accept-language': 'id-ID,id;q=0.9,en-US;q=0.8,en;q=0.7',
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'authorization': f'Bearer {bearer_token}',
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'content-type': 'application/json',
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'origin': 'https://beta.theb.ai',
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'referer': 'https://beta.theb.ai/home',
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'sec-ch-ua': '"Chromium";v="116", "Not)A;Brand";v="24", "Google Chrome";v="116"',
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'sec-ch-ua-mobile': '?0',
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'sec-ch-ua-platform': '"Windows"',
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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': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36',
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'x-ai-model': 'ee8d4f29cb7047f78cbe84313ed6ace8',
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}
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from selenium.webdriver.common.by import By
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from selenium.webdriver.support.ui import WebDriverWait
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from selenium.webdriver.support import expected_conditions as EC
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from selenium.webdriver.common.keys import Keys
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req_rand = random.randint(100000000, 9999999999)
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try:
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driver.get(f"{cls.url}/home")
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wait = WebDriverWait(driver, 10 if headless else 240)
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wait.until(EC.visibility_of_element_located((By.TAG_NAME, "body")))
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time.sleep(0.1)
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try:
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driver.find_element(By.CSS_SELECTOR, ".driver-overlay").click()
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driver.find_element(By.CSS_SELECTOR, ".driver-overlay").click()
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except:
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pass
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if model:
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# Load model panel
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wait.until(EC.visibility_of_element_located((By.CSS_SELECTOR, "#SelectModel svg")))
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time.sleep(0.1)
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driver.find_element(By.CSS_SELECTOR, "#SelectModel svg").click()
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try:
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driver.find_element(By.CSS_SELECTOR, ".driver-overlay").click()
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driver.find_element(By.CSS_SELECTOR, ".driver-overlay").click()
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except:
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pass
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# Select model
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selector = f"div.flex-col div.items-center span[title='{model}']"
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wait.until(EC.visibility_of_element_located((By.CSS_SELECTOR, selector)))
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span = driver.find_element(By.CSS_SELECTOR, selector)
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container = span.find_element(By.XPATH, "//div/../..")
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button = container.find_element(By.CSS_SELECTOR, "button.btn-blue.btn-small.border")
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button.click()
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json_data: dict[str, Any] = {
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"text" : format_prompt(messages),
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"category" : "04f58f64a4aa4191a957b47290fee864",
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"model" : "ee8d4f29cb7047f78cbe84313ed6ace8",
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"model_params": {
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"system_prompt" : "You are ChatGPT, a large language model trained by OpenAI, based on the GPT-3.5 architecture.\nKnowledge cutoff: 2021-09\nCurrent date: {{YYYY-MM-DD}}",
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"temperature" : kwargs.get("temperature", 1),
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"top_p" : kwargs.get("top_p", 1),
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"frequency_penalty" : kwargs.get("frequency_penalty", 0),
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"presence_penalty" : kwargs.get("presence_penalty", 0),
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"long_term_memory" : "auto"
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}
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# Register fetch hook
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script = """
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window._fetch = window.fetch;
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window.fetch = (url, options) => {
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// Call parent fetch method
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const result = window._fetch(url, options);
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if (!url.startsWith("/api/conversation")) {
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return result;
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}
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// Load response reader
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result.then((response) => {
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if (!response.body.locked) {
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window._reader = response.body.getReader();
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}
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});
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// Return dummy response
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return new Promise((resolve, reject) => {
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resolve(new Response(new ReadableStream()))
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});
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}
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window._last_message = "";
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"""
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driver.execute_script(script)
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response = requests.post(
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f"https://beta.theb.ai/api/conversation?org_id={org_id}&req_rand={req_rand}",
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headers=headers,
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json=json_data,
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stream=True,
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proxies={"https": proxy}
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)
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response.raise_for_status()
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content = ""
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next_content = ""
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for chunk in response.iter_lines():
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if b"content" in chunk:
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next_content = content
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data = json.loads(chunk.decode().split("data: ")[1])
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content = data["content"]
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yield content.replace(next_content, "")
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# Submit prompt
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wait.until(EC.visibility_of_element_located((By.ID, "textareaAutosize")))
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driver.find_element(By.ID, "textareaAutosize").send_keys(prompt)
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driver.find_element(By.ID, "textareaAutosize").send_keys(Keys.ENTER)
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@classmethod
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@property
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def params(cls):
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params = [
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("model", "str"),
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("messages", "list[dict[str, str]]"),
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("auth", "list[dict[str, str]]"),
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("stream", "bool"),
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("temperature", "float"),
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("presence_penalty", "int"),
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("frequency_penalty", "int"),
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("top_p", "int")
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]
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param = ", ".join([": ".join(p) for p in params])
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return f"g4f.provider.{cls.__name__} supports: ({param})"
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# Read response with reader
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script = """
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if(window._reader) {
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chunk = await window._reader.read();
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if (chunk['done']) {
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return null;
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}
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text = (new TextDecoder()).decode(chunk['value']);
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message = '';
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text.split('\\r\\n').forEach((line, index) => {
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if (line.startsWith('data: ')) {
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try {
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line = JSON.parse(line.substring('data: '.length));
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message = line["args"]["content"];
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} catch(e) { }
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}
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});
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if (message) {
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try {
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return message.substring(window._last_message.length);
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} finally {
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window._last_message = message;
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}
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}
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}
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return '';
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"""
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while True:
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chunk = driver.execute_script(script)
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if chunk:
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yield chunk
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elif chunk != "":
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break
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else:
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time.sleep(0.1)
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finally:
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if not browser:
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driver.close()
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time.sleep(0.1)
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driver.quit()
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@ -0,0 +1,77 @@
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from __future__ import annotations
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import requests
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from ...typing import Any, CreateResult, Messages
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from ..base_provider import BaseProvider
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models = {
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"theb-ai": "TheB.AI",
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"gpt-3.5-turbo": "GPT-3.5",
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"gpt-3.5-turbo-16k": "GPT-3.5-16K",
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"gpt-4-turbo": "GPT-4 Turbo",
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"gpt-4": "GPT-4",
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"gpt-4-32k": "GPT-4 32K",
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"claude-2": "Claude 2",
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"claude-1": "Claude",
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"claude-1-100k": "Claude 100K",
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"claude-instant-1": "Claude Instant",
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"claude-instant-1-100k": "Claude Instant 100K",
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"palm-2": "PaLM 2",
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"palm-2-codey": "Codey",
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"vicuna-13b-v1.5": "Vicuna v1.5 13B",
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"llama-2-7b-chat": "Llama 2 7B",
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"llama-2-13b-chat": "Llama 2 13B",
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"llama-2-70b-chat": "Llama 2 70B",
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"code-llama-7b": "Code Llama 7B",
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"code-llama-13b": "Code Llama 13B",
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"code-llama-34b": "Code Llama 34B",
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"qwen-7b-chat": "Qwen 7B"
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}
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class ThebApi(BaseProvider):
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url = "https://theb.ai"
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working = True
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needs_auth = True
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@staticmethod
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def create_completion(
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model: str,
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messages: Messages,
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stream: bool,
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auth: str,
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proxy: str = None,
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**kwargs
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) -> CreateResult:
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if model and model not in models:
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raise ValueError(f"Model are not supported: {model}")
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headers = {
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'accept': 'application/json',
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'authorization': f'Bearer {auth}',
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'content-type': 'application/json',
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}
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# response = requests.get("https://api.baizhi.ai/v1/models", headers=headers).json()["data"]
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# models = dict([(m["id"], m["name"]) for m in response])
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# print(json.dumps(models, indent=4))
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data: dict[str, Any] = {
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"model": model if model else "gpt-3.5-turbo",
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"messages": messages,
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"stream": False,
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"model_params": {
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"system_prompt": kwargs.get("system_message", "You are ChatGPT, a large language model trained by OpenAI, based on the GPT-3.5 architecture."),
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"temperature": 1,
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"top_p": 1,
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**kwargs
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}
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}
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response = requests.post(
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"https://api.theb.ai/v1/chat/completions",
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headers=headers,
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json=data,
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proxies={"https": proxy}
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)
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try:
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response.raise_for_status()
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yield response.json()["choices"][0]["message"]["content"]
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except:
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raise RuntimeError(f"Response: {next(response.iter_lines()).decode()}")
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@ -1,6 +1,7 @@
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from .Bard import Bard
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from .Raycast import Raycast
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from .Theb import Theb
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from .ThebApi import ThebApi
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from .HuggingChat import HuggingChat
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from .OpenaiChat import OpenaiChat
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from .OpenAssistant import OpenAssistant
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