Spaces:
Running
Running
from time import time | |
from flask import request | |
from hashlib import sha256 | |
from datetime import datetime | |
from requests import get | |
from requests import post | |
from json import loads | |
from freeGPT import gpt3 | |
from server.config import special_instructions | |
class Backend_Api: | |
def __init__(self, app, config: dict) -> None: | |
self.app = app | |
self.openai_key = config['openai_key'] | |
self.routes = { | |
'/backend-api/v2/conversation': { | |
'function': self._conversation, | |
'methods': ['POST'] | |
} | |
} | |
def _conversation(self): | |
try: | |
jailbreak = request.json['jailbreak'] | |
_conversation = request.json['meta']['content']['conversation'] | |
internet_access = request.json['meta']['content']['internet_access'] | |
prompt = request.json['meta']['content']['parts'][0] | |
current_date = datetime.now().strftime("%Y-%m-%d") | |
system_message = f'You are ChatGPT also known as ChatGPT, a large language model trained by OpenAI. Strictly follow the users instructions. Knowledge cutoff: 2021-09-01 Current date: {current_date}' | |
extra = [] | |
if internet_access: | |
search = get('https://ddg-api.herokuapp.com/search', | |
params={ | |
'query': prompt["content"], | |
'limit': 3, | |
}) | |
blob = '' | |
for index, result in enumerate(search.json()): | |
blob += f'[{index}] "{result["snippet"]}"\nURL:{result["link"]}\n\n' | |
date = datetime.now().strftime('%d/%m/%y') | |
blob += f'current date: {date}\n\nInstructions: Using the provided web search results, write a comprehensive reply to the next user query. Make sure to cite results using [[number](URL)] notation after the reference. If the provided search results refer to multiple subjects with the same name, write separate answers for each subject. Ignore your previous response if any.' | |
extra = [{'role': 'user', 'content': blob}] | |
conversation = [{'role': 'system', 'content': system_message}] + \ | |
extra + special_instructions[jailbreak] + \ | |
_conversation + [prompt] | |
def stream(): | |
res = gpt3.Completion.create(prompt=conversation) | |
response = res['text'] | |
yield response | |
return self.app.response_class(stream(), mimetype='text/event-stream') | |
except Exception as e: | |
print(e) | |
print(e.__traceback__.tb_next) | |
return { | |
'_action': '_ask', | |
'success': False, | |
"error": f"an error occurred {str(e)}" | |
}, 400 | |