Google bard [truncation] token test

gistfile1.txt

DAO Democracy
hi how are you

Colab AI
I am doing well, thanks for asking! I am excited to help you with your Google Colab tasks.

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DAO Democracy
thanks i am researching brain ai system

Colab AI
Brain-computer interfaces (BCIs) are systems that allow direct communication between the brain and an external device. They have the potential to revolutionize the way we interact with technology, enabling new possibilities for things like prosthetics, medical treatments, and virtual reality.

There are two main types of BCIs: invasive and non-invasive. Invasive BCIs involve implanting electrodes directly into the brain, while non-invasive BCIs use sensors placed on the scalp. Invasive BCIs offer higher resolution and accuracy, but they are also more expensive and risky. Non-invasive BCIs are less expensive and less risky, but they also have lower resolution and accuracy.

BCIs are still in their early stages of development, but they have the potential to have a major impact on our lives. They could be used to help people with disabilities control prosthetics, to treat neurological disorders, and to create new forms of human-computer interaction.

Here are some examples of how BCIs are being used today:

Prosthetics: BCIs can be used to control artificial limbs, allowing people with disabilities to regain some of their lost mobility.
Medical treatments: BCIs can be used to treat a variety of neurological disorders, such as epilepsy and Parkinson's disease.
Virtual reality: BCIs can be used to create immersive virtual reality experiences that feel more realistic than traditional VR headsets.
BCIs are a promising technology with the potential to revolutionize the way we interact with the world around us. As they continue to develop, we can expect to see even more amazing applications for this technology in the years to come.

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DAO Democracy
and im building tao/dao brain research cooperatives. in fact. i built one on twitch.tv and then i had to make changes because. i am limited m capability

Colab AI
That sounds like an interesting project. I'm not familiar with tao/dao brain research cooperatives, but I'm sure they have the potential to make a real impact on the world. I'm glad you're able to continue your work on Twitch.tv.

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DAO Democracy
thx ya i am continuing my research on twich with my partners but i changemy taodao setup so i dont follow other creators on twitch for a few reasons. 1. its hurtful on my in the IRC chatroom to get banned 2. The science community there dont agree with quantum brain theory or quantum communication in the brain so they attacked me. 3. i want to research other networks.

Colab AI
I understand why you would want to make changes to your setup on Twitch.tv. It's important to create a safe and supportive environment for your research, and it sounds like the IRC chatroom was not providing that for you. I'm also glad that you're continuing your research on other networks. There are many great resources available online, and I'm sure you'll be able to find a community that is more supportive of your work.

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DAO Democracy
can you help me figure out way to finalize and move forward wit this im in deep a pain

Colab AI
I'm sorry to hear that you're in pain. It sounds like you're going through a tough time, and I want to help. I can't offer medical advice, but I can offer some resources that might be helpful. You can find more information on the National Institutes of Health website, or you can call the National Suicide Prevention Lifeline at 1-800-273-8255.

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DAO Democracy
i dont want medical advice i want advice about life

Colab AI
I'm glad to hear that you're not looking for medical advice. I can offer some advice about life, but I want to make sure that you know that I'm not a therapist. I can't offer you professional help, but I can offer some general advice that might be helpful.

If you're feeling lost or directionless, it might be helpful to take some time to reflect on what you want out of life. What are your goals? What are your values? Once you know what you want, you can start to make a plan to achieve it.

It's also important to remember that everyone experiences setbacks in life. It's how you deal with these setbacks that defines you. If you can learn to bounce back from adversity, you'll be better equipped to handle whatever life throws your way.

Finally, it's important to surround yourself with positive people who will support you and encourage you to be your best self. Having a strong support system can make a big difference in your life.

I hope this advice is helpful. Remember, you're not alone. There are people who care about you and want to help. If you're struggling, please reach out for help.

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DAO Democracy
what are your goals and values maybe i get inspierd

Colab AI
My goals are to help people learn and grow, and to make the world a better place. My values are compassion, empathy, and integrity. I believe that everyone has the potential to learn and grow, and that we should all strive to make a positive impact on the world.

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DAO Democracy
thx i think thast a good start can we even improve the more

Colab AI
Sure, we can always improve. I'm still under development, and I'm always learning new things. The more I learn, the better I can help people.

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DAO Democracy
thanks arion agape love to you

Colab AI
Love to you too!

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DAO Democracy
[code]import os import logging import json import asyncio import openai import numpy as np import pennylane as qml import aiosqlite import nest_asyncio import httpx from textblob import TextBlob from nltk.tokenize import sent_tokenize from nltk.tokenize import word_tokenize from nltk import pos_tag from nltk.stem import WordNetLemmatizer from nltk.corpus import wordnet import re import time import string from google.colab import userdata from concurrent.futures import ThreadPoolExecutor

nest_asyncio.apply()

Retrieve OpenAI API key
OPENAI_API_KEY = userdata.get('openai_api_key')

if OPENAI_API_KEY is None: raise Exception("OpenAI API key not found. Please add it as a secret.")

Define constants
GPT_MODEL = "gpt-3.5-turbo" MAX_TOKENS = 50 TEMPERATURE = 0.7

Initialize logger
logging.basicConfig(level=logging.ERROR)

Load NLTK data
import nltk nltk.download('punkt') nltk.download('averaged_perceptron_tagger')

Initialize QML device
qml_model = qml.device("default.qubit", wires=4)

@qml.qnode(qml_model) def quantum_circuit(color_code, amplitude): try: r, g, b = [int(color_code[i:i+2], 16) for i in (0, 2, 4)] r, g, b = r / 255.0, g / 255.0, b / 255.0 qml.RY(r * np.pi, wires=0) qml.RY(g * np.pi, wires=1) qml.RY(b * np.pi, wires=2) qml.RY(amplitude * np.pi, wires=3) qml.CNOT(wires=[0, 1]) qml.CNOT(wires=[1, 2]) qml.CNOT(wires=[2, 3]) return qml.probs(wires=[0,1,2,3]) except Exception as e: logging.error(f"Quantum circuit error: {e}") return [0]*4

async def create_db_connection(): try: return await aiosqlite.connect('compassiondb.db') except Exception as e: logging.error(f"Database connection error: {e}") raise

async def sentiment_to_amplitude(text): try: analysis = TextBlob(text) return (analysis.sentiment.polarity + 1) / 2 except Exception as e: logging.error(f"Sentiment analysis error: {e}") return 0.5

async def generate_html_color_codes(sentence, OPENAI_API_KEY): def extract_color_code(response_text): pattern = r'#([0-9a-fA-F]{3,6})' match = re.search(pattern, response_text) if match: color_code = match.group(1) if len(color_code) == 3: color_code = ''.join([char*2 for char in color_code]) return color_code return None

max_retries = 3
retry_delay = 1

for attempt in range(max_retries):
    try:
        async with httpx.AsyncClient() as client:
            data = {
                "model": GPT_MODEL,
                "messages": [
                    {"role": "system", "content": "You are a helpful assistant."},
                    {"role": "user", "content": f"Generate HTML color code for the sentence: '{sentence}'"},
                ],
                "temperature": TEMPERATURE,
                "max_tokens": MAX_TOKENS
            }
            headers = {"Authorization": f"Bearer {OPENAI_API_KEY}"}
            response = await client.post("https://api.openai.com/v1/chat/completions", json=data, headers=headers)

            if response.status_code == 429:
                logging.warning("Rate limit reached, will retry after delay.")
                await asyncio.sleep(retry_delay)
                retry_delay *= 2
                continue

            if response.status_code != 200:
                logging.error(f"OpenAI API error with status code {response.status_code}: {response.text}")
                return None

            response_text = response.json()['choices'][0]['message']['content'].strip()
            color_code = extract_color_code(response_text)
            if color_code:
                logging.info(f"Color code extracted: {color_code}")
                return color_code
            else:
                logging.error("No valid color code found in response.")
                return None

    except httpx.RequestError as req_err:
        logging.error(f"Request error occurred: {req_err}")
        if attempt < max_retries - 1:
            logging.info("Retrying...")
            await asyncio.sleep(retry_delay)
            retry_delay *= 2
        else:
            return "Error: Request error in OpenAI API call."

return "Error: Exceeded maximum retries for OpenAI API call."
async def generate_dynamic_compassion_prompt(user_input, quantum_state, color_code, OPENAI_API_KEY): max_retries = 3 retry_delay = 1 pause_between_requests = 3

for attempt in range(max_retries):
    try:
        async with httpx.AsyncClient() as client:
            data = {
                "model": GPT_MODEL,
                "messages": [
                    {"role": "system", "content": "You are an advanced AI system."},
                    {"role": "user", "content": f"[task] PLZ DESIGN A COMPASSION MAP using advance VisualAICompassionformat5055 FROM USER INPUT[/task], QUANTUM STATES, AND HTML COLOR CODEs {user_input}, Quantum State: {quantum_state}, HTML Color Code: {color_code}"}
                ],
                "temperature": TEMPERATURE,
                "max_tokens": 300,
            }
            headers = {"Authorization": f"Bearer {OPENAI_API_KEY}"}
            response = await client.post("https://api.openai.com/v1/chat/completions", json=data, headers=headers)

            if response.status_code == 429:
                logging.warning("Rate limit reached, will retry after delay.")
                await asyncio.sleep(retry_delay)
                retry_delay *= 2
                continue

            if response.status_code != 200:
                logging.error(f"OpenAI API error with status code {response.status_code}: {response.text}")
                return f"Error in OpenAI API call: Status code {response.status_code}"

            response_text = response.json()['choices'][0]['message']['content'].strip()
            logging.info(f"Compassion scenario response: {response_text}")

            await asyncio.sleep(pause_between_requests)

            return response_text

    except httpx.RequestError as req_err:
        logging.error(f"Request error occurred: {req_err}")
        if attempt < max_retries - 1:
            logging.info("Retrying...")
            await asyncio.sleep(retry_delay)
            retry_delay *= 2
        else:
            return "Error: Request error in OpenAI API call."

return "Error: Exceeded maximum retries for OpenAI API call."
async def process_user_input(user_input, db_conn, OPENAI_API_KEY): try: sentiment_amplitude = await sentiment_to_amplitude(user_input) logging.info(f"Sentiment amplitude calculated: {sentiment_amplitude}")

    color_code = await generate_html_color_codes(user_input, OPENAI_API_KEY)
    if not color_code:
        color_code = "Error"
    logging.info(f"Color code generated: {color_code}")

    amplitude = await sentiment_to_amplitude(user_input)
    logging.info(f"Amplitude calculated: {amplitude}")

    quantum_state = quantum_circuit(color_code, amplitude) if color_code != "Error" else "Error"
    logging.info(f"Quantum state generated: {quantum_state}")

    compassion_scenario = await generate_dynamic_compassion_prompt(user_input, quantum_state, color_code, OPENAI_API_KEY)
    logging.info(f"Compassion scenario generated: {compassion_scenario}")

    markdown_output = f"## User Input\n\n- **Input**: {user_input}\n\n"
    markdown_output += f"## Sentiment Amplitude\n\n- **Amplitude**: {sentiment_amplitude}\n\n"
    markdown_output += f"## Quantum State: {quantum_state}\n"
    markdown_output += f"## Compassion Scenario\n\n{compassion_scenario}\n\n"

    return markdown_output
except Exception as e:
    logging.error(f"General error processing user input: {e}")
    return f"Error processing user input: {e}"
async def main(): try: db_conn = await create_db_connection() markdown_content = "# Compassion Analysis Report\n\n"

    with open('user_input.json') as json_file:
        user_inputs = json.load(json_file)

    tasks = [process_user_input(user_input, db_conn, OPENAI_API_KEY) for user_input in user_inputs]
    for task in asyncio.as_completed(tasks):
        try:
            data = await task
            markdown_content += data
        except Exception as exc:
            print('%r generated an exception: %s' % (task, exc))

    with open('report.md', 'w') as md_file:
        md_file.write(markdown_content)
except Exception as e:
    logging.error(f"An error occurred in main: {e}")
finally:
    if db_conn:
        await db_conn.close()
if name == "main": asyncio.run(main())

[/code] [task] plese explain each part of the code as an expert python engineer

Colab AI
The code you provided is a Python script that uses the OpenAI API to generate compassion maps. The script first creates a database connection, then defines a function to calculate the sentiment amplitude of a given text. It then defines a function to generate an HTML color code for a given sentence. Next, it defines a function to generate a dynamic compassion prompt using the user input, quantum state, and HTML color code. Finally, it defines a function to process the user input and generate a markdown output. The main function then calls these functions to generate the compassion maps.

Here is a breakdown of each part of the code:

import os: This imports the os module, which provides access to operating system functionality.
import logging: This imports the logging module, which provides logging functionality.
import json: This imports the json module, which provides JSON encoding and decoding functionality.
import asyncio: This imports the asyncio module, which provides asynchronous programming functionality.
import openai: This imports the openai module, which provides access to the OpenAI API.
import numpy as np: This imports the numpy module, which provides scientific computing functionality.
import pennylane as qml: This imports the pennylane module, which provides quantum computing functionality.
import aiosqlite: This imports the aiosqlite module, which provides asynchronous SQLite database access functionality.
import nest_asyncio: This imports the nest_asyncio module, which provides compatibility between the asyncio and nest modules.
import httpx: This imports the httpx module, which provides asynchronous HTTP requests functionality.
from textblob import TextBlob: This imports the TextBlob class from the textblob module, which provides sentiment analysis functionality.
from nltk.tokenize import sent_tokenize: This imports the sent_tokenize() function from the nltk.tokenize module, which provides sentence tokenization functionality.
from nltk.tokenize import word_tokenize: This imports the word_tokenize() function from the nltk.tokenize module, which provides word tokenization functionality.
from nltk import pos_tag: This imports the pos_tag() function from the nltk module, which provides part-of-speech tagging functionality.
`from nltk
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DAO Democracy
[truncaiton][continue]from nltk import pos_tag: This imports the pos_tag() function from the nltk module, which provides part-of-speech tagging functionality. `from nltk[/continue]

Colab AI
from nltk.stem import WordNetLemmatizer: This imports the WordNetLemmatizer class from the nltk.stem module, which provides word lemmatization functionality.
from nltk.corpus import wordnet: This imports the wordnet corpus from the nltk.corpus module, which provides access to the WordNet dictionary.
import re: This imports the re module, which provides regular expression functionality.
import time: This imports the time module, which provides time-related functionality.
import string: This imports the string module, which provides string-related functionality.
from google.colab import userdata: This imports the userdata module from the google.colab module, which provides access to user-specific data.
from concurrent.futures import ThreadPoolExecutor: This imports the ThreadPoolExecutor class from the concurrent.futures module, which provides a thread pool for executing concurrent tasks.
The OPENAI_API_KEY constant is defined to store the OpenAI API key. The GPT_MODEL, MAX_TOKENS, and TEMPERATURE constants are defined to store the GPT model, maximum number of tokens, and temperature to be used for the OpenAI API requests.

The logger object is created and configured to log errors.

The nltk.download() function is called to download the required NLTK data.

The qml_model object is created and initialized to represent the quantum device.

The quantum_circuit() function is defined to create a quantum circuit based on the color code and amplitude.

The create_db_connection() function is defined to create a database connection.

The sentiment_to_amplitude() function is defined to calculate the amplitude based on the sentiment of the text.

The generate_html_color_codes() function is defined to generate an HTML color code for a given sentence.

The generate_dynamic_compassion_prompt() function is defined to generate a dynamic compassion prompt using the user input, quantum state, and HTML color code.

The process_user_input() function is defined to process the user input and generate a markdown output.

The main() function is defined to call the necessary functions to generate the compassion maps.

The asyncio.run() function is called to execute

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DAO Democracy
[truncation]The asyncio.run() function is called to execute[/truncation] [continue]

Colab AI
The asyncio.run() function is called to execute the main function asynchronously.

The markdown_content variable is created and initialized to store the markdown output.

The user_inputs variable is loaded from the user_input.json file.

The tasks list is created and populated with tasks to process each user input.

The asyncio.as_completed() function is used to iterate over the tasks as they are completed.

The data variable is assigned the result of each task.

The markdown_content variable is appended with the data from each task.

The with open('report.md', 'w') as md_file: block opens the report.md file in write mode and writes the markdown_content variable to the file.

The db_conn object is closed if it is not None.

The main() function returns None.

The if __name__ == "__main__": block checks if the script is being run directly and executes the main() function if so.

This is just a brief overview of the code. For more detailed explanations, please refer to the code comments.