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21
LICENSE
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21
LICENSE
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MIT License
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Copyright (c) 2024 The AI Brain Project
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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79
README.md
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README.md
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# ai-brain
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updates for the ai brain
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# The AI Brain Project
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The AI Brain Project is a Python program that demonstrates how to use OpenAI's GPT-3 language model to upload your brain to the cloud. The program asks the user for a name and generates random actions every 5 seconds, along with a random sentence using the GPT-3 model based on certain context. The program also allows the user to load custom context from a file. Think of it as [HLE](https://en.wikipedia.org/wiki/UltraHLE) for [brain emulation/mind uploading](https://en.wikipedia.org/wiki/Mind_uploading).
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## Installation
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To install the program, follow these steps:
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Clone the repository:
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```
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git clone https://github.com/yourusername/ai-brain-project.git
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```
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Install the required Python packages:
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```
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pip install -r requirements.txt
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```
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Set up your OpenAI API key and name using the OpenAI Secret Manager. See the section on "Using the OpenAI Secret Manager" for more information.
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Run the program:
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```
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python main.py
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```
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## Usage
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When you run the program, it will ask you for a name. Enter your name and press Enter. The program will then start generating actions and GPT-3 sentences every 5 seconds.
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You can also load custom context from a file by placing a file named custom-context.txt in the same directory as main.py. The program will automatically load the context from the file if it exists.
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Using the OpenAI Secret Manager
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To keep your OpenAI API key and name secure, you can use the OpenAI Secret Manager to store them securely. To do this, follow these steps:
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Create an account on the OpenAI website if you haven't already done so.
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Log in to the OpenAI website and go to the "API keys" page.
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Click "Generate new API key" to create a new API key.
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Install the openai_secret_manager Python package:
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```
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pip install openai_secret_manager
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```
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Import the openai_secret_manager module in your Python code:
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```
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import openai_secret_manager
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```
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Use the openai_secret_manager.get_secret function to retrieve your API key and name (already in the code):
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```
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secrets = openai_secret_manager.get_secret("my_app_name")
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openai.api_key = secrets["api_key"]
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name = secrets["name"]
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```
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In the OpenAI Secret Manager, create a new secret with the name my_app_name and your API key and name as the values. You can also add other keys if needed.
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Creating a new secret in the OpenAI Secret Manager
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Save the secret in the OpenAI Secret Manager.
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## License
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The AI Brain Project is licensed under the MIT License. See the LICENSE file for more information.
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## Possible future impementations (for people who would like to contrib!)
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* An semi-decentralized API for the AI using GUN-DB?
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* A ROBLOX Metaverse based on the API?
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4
install-requirements-debian.sh
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install-requirements-debian.sh
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curl https://ollama.ai/install.sh | sh
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pip3 install -r requirements.txt
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ollama pull sparksammy/samai
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ollama pull llama2
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273
main.py
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main.py
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import asyncio
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import random
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import os
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import subprocess
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import datetime
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import sys
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import requests
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import subprocess
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from fastapi import FastAPI
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from typing import Union
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app = FastAPI()
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subprocess.run(["ollama", "serve"]) #make sure that our ollama server is started
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from langchain.callbacks.manager import CallbackManager
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from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
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from langchain_community.llms import Ollama
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# replace model with the downloaded model you want to use
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llm = Ollama(
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model="sparksammy/samai",
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callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),
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)
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completeContext = ""
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# Define URLs for requirements.txt and main.py
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#REQS_URL = 'https://raw.githubusercontent.com/The-AI-Brain/ai-brain/main/requirements.txt'
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#MAIN_URL = 'https://raw.githubusercontent.com/The-AI-Brain/ai-brain/main/main.py'
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# Define paths for local requirements.txt and main.py files
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REQS_PATH = 'requirements.txt'
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MAIN_PATH = 'main.py'
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List = {}
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# Check for updates
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def check_updates():
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# Download remote requirements.txt file
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remote_reqs = requests.get(REQS_URL).text
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# Compare local and remote requirements.txt files
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with open(REQS_PATH, 'r') as f:
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local_reqs = f.read()
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if local_reqs != remote_reqs:
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# Install updated requirements
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subprocess.run(['pip', 'install', '-r', REQS_PATH])
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# Download updated main.py file
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remote_main = requests.get(MAIN_URL).text
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# Write updated main.py file
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with open(MAIN_PATH, 'w') as f:
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f.write(remote_main)
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# Restart the script
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os.execv(sys.argv[0], sys.argv)
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emotions = [
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"happy", "sad", "angry", "surprised", "disgusted", "fearful",
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"excited", "nostalgic", "hopeful", "anxious", "relaxed", "curious",
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"confused", "amused", "bored", "ecstatic", "exhausted", "grateful",
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"guilty", "embarrassed", "envious", "proud", "ashamed", "content",
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"depressed", "fascinated", "frustrated", "inspired", "irritated",
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"jealous", "lonely", "melancholic", "optimistic", "overwhelmed",
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"peaceful", "playful", "reflective", "remorseful", "restless",
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"satisfied", "sympathetic", "tense", "terrified", "triumphant",
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"uncomfortable", "vulnerable", "wistful", "yearning", "zealous"
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]
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# Array of human actions
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actions = [
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"walked the dog",
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"cooked dinner",
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"read a book",
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"went swimming",
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"played soccer",
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"listened to music",
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"watched a movie",
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"painted a picture",
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"wrote a story",
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"rode a bike",
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"danced in the rain",
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"visited a museum",
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"went on a road trip",
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"went to a concert",
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"built a sandcastle",
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"went to the beach",
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"played video games",
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"climbed a mountain",
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"played with a pet",
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"went for a run",
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"did yoga",
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"went camping",
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"visited a new city",
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"went to a party",
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"took a nap",
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"had a picnic",
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"played a musical instrument",
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"tried a new food",
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"went on a hike",
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"took a bath",
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"visited a friend",
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"went to a theme park",
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"went to a zoo",
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"went to a sporting event",
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"went to a play",
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"went to a comedy show",
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"went to a ballet",
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"went to a musical",
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"went to a poetry reading",
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"went to a book club meeting",
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"went to a cooking class",
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"went to a painting class",
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"went to a wine tasting",
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"went to a beer festival",
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"went to a farmers' market",
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"went to a flea market",
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"went shopping",
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"went to a garage sale",
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"went to a thrift store",
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"volunteered at a charity",
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"went to a political rally",
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"went to a religious service",
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"attended a wedding",
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"attended a funeral",
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"graduated from school",
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"started a new job",
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"retired from a job",
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"got married",
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"got divorced",
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"had a baby",
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"raised a child",
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"adopted a pet",
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"moved to a new city",
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"bought a house",
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"rented an apartment",
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"remodeled a home",
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"gardened",
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"landscaped a yard",
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"went on a cruise",
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"went on a safari",
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"went on a skiing trip",
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"went on a snowboarding trip",
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"went on a fishing trip",
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"went on a hunting trip",
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"went on a scuba diving trip",
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"went on a surfing trip",
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"went on a kayaking trip",
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"went on a canoeing trip",
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"went on a rafting trip",
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"went on a hot air balloon ride",
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"went on a helicopter ride",
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"went on a plane ride",
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"went on a train ride",
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"went on a road trip",
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"went skydiving",
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"went bungee jumping",
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"went zip lining",
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"went rock climbing",
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"went to a spa",
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"got a massage",
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"got a facial",
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"got a manicure",
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"got a pedicure",
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"went to a chiropractor",
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"went to a physical therapist",
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"went to a dentist",
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"went to a doctor",
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"got surgery",
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"recovered from an illness",
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"overcame an addiction",
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"learned a new skill",
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"learned a new language",
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"took a class",
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"ate",
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"played the piano",
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"went for a walk"
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]
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# Array of places for the actions
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places = [
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"in the park",
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"at home",
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"in the library",
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"on the beach",
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"in the movie theater",
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"at the doctor's office",
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"at school",
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"at the spa",
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"at the airport",
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"at the gym",
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"in a cafe",
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"in a museum",
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"in a grocery store",
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"in a restaurant",
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"at a concert",
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"at a stadium",
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"in a hospital",
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"in a church",
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"in a mosque",
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"in a temple",
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"in a theater",
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"in a nightclub",
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"in a casino",
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"at a zoo",
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"at a theme park",
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"at a water park",
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"in a shopping mall",
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"in a department store",
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"at a gas station",
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"in a parking lot",
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"in a hotel",
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"in a motel",
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"in a hostel",
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"in a campground",
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"in a forest",
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"on a mountain",
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"in a desert",
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"in a valley",
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"by a river",
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"by a lake",
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"at sea",
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"in the ocean",
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"in a cave",
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"at a train station",
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"at a bus station",
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"at a subway station",
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"at a ferry terminal",
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"at a harbor",
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"in a space station",
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"in a laboratory"
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]
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# Asynchronous function to print actions
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def createContext():
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action = random.choice(actions)
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place = random.choice(places)
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completeContext = f"you just did \"{action}\" and did it \"{place}\""
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return complete
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@app.get("/context")
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async def get_emote():
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return await createContext()
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@app.get("/chatin/{chatText}")
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async def get_chatin(chatText: str):
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return await main(chatText)
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# Main function to run the program
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def main(chatin):
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#check_updates()
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chatin = "Guest:" + chatin
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# Get response from ollama
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message = llm(f"(Additional context for reply: {completeContext}), reply to this: {chatin}")
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# Print ollama response and what it said
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print(f"You: {chatin}")
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print(f"{name}: {message}")
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return f"You: {chatin}\n${name}: ${message}"
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# Run the cli function
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def cli():
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while True:
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toChat = input("brain@localhost:~$ ")
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main(f"{toChat}")
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cli()
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2
requirements.txt
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2
requirements.txt
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langchain
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fastapi
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