drop garbage
This commit is contained in:
if
parent
3e0219b82b
commit
1ff262d106
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@ -65,10 +65,10 @@ repos:
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- id: python-use-type-annotations
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- id: text-unicode-replacement-char
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- repo: https://github.com/pre-commit/mirrors-mypy
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rev: v1.2.0
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hooks:
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- id: mypy
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additional_dependencies: [ types-PyYAML==6.0.10, types_requests==2.28.8, types-ujson==5.6.0 ]
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args: [ --ignore-missing-imports, --warn-no-return, --warn-redundant-casts, --disallow-incomplete-defs ]
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exclude: ^setup.py
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# - repo: https://github.com/pre-commit/mirrors-mypy
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# rev: v1.2.0
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# hooks:
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# - id: mypy
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# additional_dependencies: [ types-PyYAML==6.0.10, types_requests==2.28.8, types-ujson==5.6.0 ]
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# args: [ --ignore-missing-imports, --warn-no-return, --warn-redundant-casts, --disallow-incomplete-defs ]
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# exclude: ^setup.py
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@ -26,8 +26,6 @@ AI_MODEL_OPTIONS: list[str] = [
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"gpt-3.5-turbo",
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"gpt-4",
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"gpt-4-32k",
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"bard",
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"phind-gpt-4",
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]
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st.set_page_config(page_title=PAGE_TITLE, page_icon=PAGE_ICON)
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@ -1,107 +0,0 @@
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"""
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Reverse engineering of Google Bard from https://github.com/discordtehe/Bard
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"""
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import json
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import random
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import re
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import string
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import requests
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import streamlit as st
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US_AG = "Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.114 Safari/537.36"
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class BardChat:
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"""
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A class to interact with Google Bard.
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Parameters
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session_id: str
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The __Secure-1PSID cookie.
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"""
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__slots__ = [
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"headers",
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"_reqid",
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"SNlM0e",
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"conversation_id",
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"response_id",
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"choice_id",
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"session",
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]
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def __init__(self, session_id):
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headers = {
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"Host": "bard.google.com",
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"X-Same-Domain": "1",
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"User-Agent": US_AG,
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"Content-Type": "application/x-www-form-urlencoded;charset=UTF-8",
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"Origin": "https://bard.google.com",
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"Referer": "https://bard.google.com/",
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}
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self._reqid = int("".join(random.choices(string.digits, k=4)))
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self.conversation_id = ""
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self.response_id = ""
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self.choice_id = ""
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self.session = requests.Session()
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self.session.headers = headers
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self.session.cookies.set("__Secure-1PSID", session_id)
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self.SNlM0e = self.__get_snlm0e()
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def __get_snlm0e(self):
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resp = self.session.get(url="https://bard.google.com/", timeout=10)
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# Find "SNlM0e":"<ID>"
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if resp.status_code != 200:
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st.error("Could not get Google Bard")
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SNlM0e = re.search(r"SNlM0e\":\"(.*?)\"", resp.text).group(1)
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return SNlM0e
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def ask(self, message: str) -> dict:
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"""
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Send a message to Google Bard and return the response.
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:param message: The message to send to Google Bard.
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:return: A dict containing the response from Google Bard.
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"""
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# url params
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params = {
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"bl": "boq_assistant-bard-web-server_20230326.21_p0",
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"_reqid": str(self._reqid),
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"rt": "c",
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}
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# message arr -> data["f.req"]. Message is double json stringified
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message_struct = [
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[message],
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None,
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[self.conversation_id, self.response_id, self.choice_id],
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]
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data = {
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"f.req": json.dumps([None, json.dumps(message_struct)]),
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"at": self.SNlM0e,
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}
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# do the request!
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resp = self.session.post(
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"https://bard.google.com/_/BardChatUi/data/assistant.lamda.BardFrontendService/StreamGenerate",
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params=params,
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data=data,
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timeout=120,
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)
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chat_data = json.loads(resp.content.splitlines()[3])[0][2]
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if not chat_data:
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return {"content": f"Google Bard encountered an error: {resp.content}."}
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json_chat_data = json.loads(chat_data)
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results = {
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"content": json_chat_data[0][0],
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"conversation_id": json_chat_data[1][0],
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"response_id": json_chat_data[1][1],
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"factualityQueries": json_chat_data[3],
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"textQuery": json_chat_data[2][0] if json_chat_data[2] is not None else "",
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"choices": [{"id": i[0], "content": i[1]} for i in json_chat_data[4]],
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}
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self.conversation_id = results["conversation_id"]
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self.response_id = results["response_id"]
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self.choice_id = results["choices"][0]["id"]
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self._reqid += 100000
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return results
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@ -1,285 +0,0 @@
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from datetime import datetime
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from queue import Empty, Queue
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from threading import Thread
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from time import time
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from urllib.parse import quote
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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" # noqa: E501
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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>""" # noqa: E501
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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>""" # noqa: E501
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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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@staticmethod
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def create(prompt: str, actual_search: bool = True, language: str = "en") -> dict: # None = no search
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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 actual_search:
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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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@staticmethod
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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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code_context: 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, actual_search=True)
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if len(code_context) > 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, # response.json()['rawBingResults'],
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"codeContext": code_context,
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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,
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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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@staticmethod
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def request(model, prompt, results, creative, detailed, code_context, 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": code_context,
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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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post("https://www.phind.com/api/infer/answer",
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headers=headers, json=json_data, timeout=99999, impersonate="chrome110",
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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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code_context: str = "",
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language: str = "en"):
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|
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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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|
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if results is None:
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results = Search.create(prompt, actual_search=True)
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|
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if len(code_context) > 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, code_context, language]).start()
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while StreamingCompletion.stream_completed is not 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,
|
||||
"logprobs": None,
|
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"finish_reason": "stop"
|
||||
}],
|
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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)
|
||||
}
|
||||
})
|
||||
|
||||
except Empty:
|
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pass
|
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|
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@staticmethod
|
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def handle_stream_response(response):
|
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StreamingCompletion.message_queue.put(response)
|
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|
|
@ -2,11 +2,8 @@ from random import randrange
|
|||
|
||||
import streamlit as st
|
||||
from openai.error import InvalidRequestError, OpenAIError
|
||||
from requests.exceptions import TooManyRedirects
|
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from streamlit_chat import message
|
||||
|
||||
from .agi import phind
|
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from .agi.bard import BardChat
|
||||
from .agi.chat_gpt import create_gpt_completion
|
||||
from .stt import show_voice_input
|
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from .tts import show_audio_player
|
||||
|
|
@ -100,33 +97,6 @@ def show_gpt_conversation() -> None:
|
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st.error(err)
|
||||
|
||||
|
||||
def show_bard_conversation() -> None:
|
||||
try:
|
||||
bard = BardChat(st.secrets.api_credentials.bard_session)
|
||||
ai_content = bard.ask(st.session_state.user_text)
|
||||
st.warning(ai_content.get("content"))
|
||||
except (TooManyRedirects, AttributeError) as err:
|
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st.error(err)
|
||||
|
||||
|
||||
def phind_get_answer(question: str):
|
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phind.cf_clearance = st.secrets.api_credentials.phind_cf_clearance
|
||||
phind.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" # noqa: E501
|
||||
|
||||
try:
|
||||
result = phind.Completion.create(
|
||||
model="gpt-4",
|
||||
prompt=question,
|
||||
results=phind.Search.create(question, actual_search=True),
|
||||
creative=False,
|
||||
detailed=False,
|
||||
code_context=""
|
||||
)
|
||||
st.markdown(result.completion.choices[0].text)
|
||||
except Exception as e:
|
||||
st.error(e)
|
||||
|
||||
|
||||
def show_conversation() -> None:
|
||||
if st.session_state.messages:
|
||||
st.session_state.messages.append({"role": "user", "content": st.session_state.user_text})
|
||||
|
|
@ -136,9 +106,4 @@ def show_conversation() -> None:
|
|||
{"role": "system", "content": ai_role},
|
||||
{"role": "user", "content": st.session_state.user_text},
|
||||
]
|
||||
if st.session_state.model == "bard":
|
||||
show_bard_conversation()
|
||||
elif st.session_state.model == "phind-gpt-4":
|
||||
phind_get_answer(st.session_state.user_text)
|
||||
else:
|
||||
show_gpt_conversation()
|
||||
show_gpt_conversation()
|
||||
|
|
|
|||
|
|
@ -1,10 +1,9 @@
|
|||
pip>=23.1
|
||||
streamlit>=1.21.0
|
||||
pip>=23.1.2
|
||||
streamlit>=1.22.0
|
||||
streamlit-chat>=0.0.2.2
|
||||
streamlit_option_menu>=0.3.2
|
||||
openai>=0.27.4
|
||||
openai>=0.27.5
|
||||
gtts>=2.3.1
|
||||
bokeh==2.4.2
|
||||
streamlit-bokeh-events>=0.1.2
|
||||
watchdog>=3.0.0
|
||||
curl_cffi>=0.5.5
|
||||
|
|
|
|||
Loading…
Reference in New Issue