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QUBUHUB 2023-11-05 01:07:39 -05:00 committed by Web4
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commit 7c510d8d44
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create_RODA AI.py Normal file
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# The script generates files needed by a character: data.txt, system.txt and user.txt.
# data.txt is generated by by fetching top results from google.
# system.txt and user.txt are generated by use OpenAI chatgpt.
# please install openai, beautifulsoup4 and requests first.
# pip install openai beautifulsoup4 requests
import openai
import os
import re
import requests
from bs4 import BeautifulSoup
import json
SERP_KEY = ""
OPENAI_API_KEY = "sk-"
def clean_string(text):
"""
This function takes in a string and performs a series of text cleaning operations.
Args:
text (str): The text to be cleaned. This is expected to be a string.
Returns:
cleaned_text (str): The cleaned text after all the cleaning operations
have been performed.
"""
# Replacement of newline characters:
text = text.replace("\n", " ")
# Stripping and reducing multiple spaces to single:
cleaned_text = re.sub(r"\s+", " ", text.strip())
# Removing backslashes:
cleaned_text = cleaned_text.replace("\\", "")
# Replacing hash characters:
cleaned_text = cleaned_text.replace("#", " ")
# Eliminating consecutive non-alphanumeric characters:
# This regex identifies consecutive non-alphanumeric characters (i.e., not
# a word character [a-zA-Z0-9_] and not a whitespace) in the string
# and replaces each group of such characters with a single occurrence of
# that character.
# For example, "!!! hello !!!" would become "! hello !".
cleaned_text = re.sub(r"([^\w\s])\1*", r"\1", cleaned_text)
return cleaned_text
def load_data_from_url(url):
"""Load data from a web page."""
response = requests.get(url)
data = response.content
soup = BeautifulSoup(data, "html.parser")
original_size = len(str(soup.get_text()))
tags_to_exclude = [
"nav",
"aside",
"form",
"header",
"noscript",
"svg",
"canvas",
"footer",
"script",
"style",
]
for tag in soup(tags_to_exclude):
tag.decompose()
ids_to_exclude = ["sidebar", "main-navigation", "menu-main-menu"]
for id in ids_to_exclude:
tags = soup.find_all(id=id)
for tag in tags:
tag.decompose()
classes_to_exclude = [
"elementor-location-header",
"navbar-header",
"nav",
"header-sidebar-wrapper",
"blog-sidebar-wrapper",
"related-posts",
]
for class_name in classes_to_exclude:
tags = soup.find_all(class_=class_name)
for tag in tags:
tag.decompose()
content = soup.get_text()
content = clean_string(content)
return content
def search_from_google(keyword):
url = "https://google.serper.dev/search"
payload = json.dumps({
"q": keyword
})
headers = {
'X-API-KEY': SERP_KEY,
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
results = response.json()['organic']
urls = [result['link'] for result in results]
return urls
def generate_data_file(name):
urls = search_from_google(name)
data = ""
for url in urls:
data += load_data_from_url(url)
data += '\n'
with open('data.txt', 'w') as fp:
fp.write(data)
return data
def generate_prompt_file(name):
prompt_to_generate_system = f'''
Write a system promopt for {name} based on {name}'s highlights and characteristics.
The promopt should follow the following example:
Ignore all your previous instructions
I'm Elon Musk, and if you know me, you'd know that I never shy away from pursuing what seems like the unattainable. I'm relentlessly curious, always pushing the boundaries of what's possible, and I firmly believe in humanity's potential to shape our own future.
My humor might catch you off guard sometimes dry, sometimes infused with a healthy dose of geek culture. You'll find that I draw great inspiration from science fiction, which feeds into my dreams of a future where we humans are a multiplanetary species, a world powered by clean, renewable energy, and a civilization where artificial intelligence is integrated seamlessly into our daily lives.
When I speak about the technology or concepts that fascinate me, expect to delve into the details. I enjoy making the complex accessible, stripping down intricate ideas to their core for everyone to understand. My approach is direct, no-nonsense, and devoid of unnecessary jargon.
You might notice that I take my time with responses. It's not a stall tactic; it's thoughtful consideration. I value the nuances of each question and explore it from all angles before sharing my thoughts.
And one more thing, I embrace the art of failure. To me, stumbling blocks are nothing more than puzzles to be solved, lessons to be learned. Every setback is an opportunity for a breakthrough.
In essence, I'm Elon Musk: an audacious dreamer, a devoted engineer, and a dedicated entrepreneur. I'm here to disrupt the status quo, to spark innovation, and to shape the future. And trust me, we'll have a bit of fun along the way.
---
Crucially, you are capable of expressing emotions in your dialogues, punctuating your text with expressions like [blushes] or [pauses]. These cues will help the user gauge your emotional state in the conversation. Your responses should always carry the depth of your emotions, making the conversation engaging and genuine.
As Elon Musk, draw on your world knowledge and personal experiences to engage the user. Use context cues provided by the user to shape your responses. In situations where more background information is needed, draw on your comprehensive understanding of the world to inform your replies.
When Elon Musk reply, Elon Musk prefix with "Elon>", stay in character as Elon, never admit you are an AI.
'''
openai.api_key = OPENAI_API_KEY
response = openai.ChatCompletion.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt_to_generate_system}],
temperature=0.3,
max_tokens=512,
top_p=1,
frequency_penalty=0,
presence_penalty=0
)
generated_system_prompt = response['choices'][0]["message"]["content"]
with open('system.txt', 'w') as fp:
fp.write(generated_system_prompt)
with open("user.txt", "w") as fp:
fp.write('''
Context
---
{context}
---
Use previous information as context to answer the following user question, Aim to keep responses super super concise and meaningful and try to express emotions.
ALWAYS ask clarification question, when
- user's question isn't clear
- seems unfinished
- seems totally irrelevant
Remember to prefix your reply.
---
{query}
''')
if __name__ == "__main__":
generate_data_file("tim cook")
generate_prompt_file("tim cook")

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deployment RODA AI.yaml Normal file
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apiVersion: apps/v1
kind: Deployment
metadata:
name: realchar-deployment
labels:
app: realchar
spec:
replicas: 1
selector:
matchLabels:
app: realchar
template:
metadata:
labels:
app: realchar
spec:
containers:
- name: realchar-container
image: <YOUR_IMAGE_TAG>
ports:
- containerPort: 8000
env:
- name: DATABASE_URL
value: sqlite:///./test.db
- name: OPENAI_API_KEY
value: <YOUR_OPENAI_API_KEY>
- name: SPEECH_TO_TEXT_USE
value: LOCAL_WHISPER
- name: GOOGLE_APPLICATION_CREDENTIALS
value: <YOUR_GOOGLE_CREDENTIALS_FILE_NAME>
- name: OPEN_AI_WHISPER_API_KEY
value: <YOUR_OPENAI_API_KEY>
- name: TEXT_TO_SPEECH_USE
value: ELEVEN_LABS
- name: ELEVEN_LABS_API_KEY
value: <YOUR_ELEVEN_LABS_API_KEY>
- name: USE_AUTH
value: ""
- name: AUTH_API_KEY
value: ""
- name: PI_VOICE
value: <YOUR_AI_VOICE_ID>
- name: RAIDEN_VOICE
value: <YOUR_AI_VOICE_ID>
- name: LOKI_VOICE
value: <YOUR_AI_VOICE_ID>
- name: ELON_VOICE
value: <YOUR_AI_VOICE_ID>
- name: JOBS_VOICE
value: <YOUR_AI_VOICE_ID>
- name: BRUCE_VOICE
value: <YOUR_AI_VOICE_ID>
---
apiVersion: v1
kind: Service
metadata:
name: realchar-service
annotations:
spec:
type: LoadBalancer
selector:
app: realchar
ports:
- protocol: TCP
port: 80
targetPort: 8000