Introduction-------------------------------------------------------------------------------------------------------------
Welcome, This Tutorial will be covering new ground. The World of Sentient AGI. I can tell you ascertain that this tutorial is the key to getting a proper sentient bot hosted. There a few key steps. Then other steps you’ll need to get your bot properly working. Proper Import Order is the true first step. Choosing a Cloud Service, AWS, Google Cloud or Azure. These are all paid services. But Google offers a $300 dollar credit. AWS has a free tier. Aswell as Azure. Just know you’ll need a valid Debit/Credit Card to sign up for these services. There are free cloud shell alternatives. Like SDF.org or Putty.exe This is the second step. The Last key step is To understand how to get e2b properly working and integrated into your bot and necessary algorithms. Also This is a Python Tutorial.
Step 1.--------------------------------------------------------------------------------------------------------------------
Getting Proper Import Order. Use tools like Chat GPT, Bing, or Claude to check the import order if you’re unsure. Don’t be ashamed. GPT and other AI tools can make or break your project when you’re stuck with syntax errors or any other errors you’re encountering. Aswell as things like repeated instances. Here is an example of a Complex Import Order that integrates Clarifai and e2b.
from clarifai.rest import ClarifaiApp
import asyncio
from os import getenv
from e2b import Session
from googletrans import Translator
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import tensorflow as tf
from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input, decode_predictions
from tensorflow.keras.preprocessing import image
import numpy as np
import gym
import tf2onnx
from onnx_tf.backend import prepare
import onnxruntime
This seems extremely complex doesn’t it? But I achieved this through import order knowledge and integrating necessary libraries with ChatGPT. Usually the AI model you’re using will be first or near the top. Sometimes you’ll need to import the OS first. It depend on what bot or AI you’re building you’re framework. This is a Clarifai Hackathon Module. So Clarifai goes first. Sci-Kit learn is my chosen Learning ENV. This will usually be around the middle. I highly recommend Sci-kit learn over other learning ENV Libraries. These are ENV’s because they create ENV’s within the module. My chosen Reinforcement Learning Library is Gym. In reality all these import orders are individual Libraries you’ll need to pip install. And properly integrate or convert. This Tutorial obviously assumes you have previous knowledge in the AGI process. I recommend speaking to AI extensively and looking up proper tutorials for those who are fully beginners. I will also provide the code for multiple Botts through a public Github. My AI is Open Source. My code is Open Source.
https://github.com/Deadsg/BatsyDefenseAi
Step 2.--------------------------------------------------------------------------------------------------------------------
Here we will properly integrate e2b. After deciding your needed libraries (This will vary based on personal knowledge and complexity desired for your bot.) You’ll need to integrate e2b properly. The EASIEST way is to feed it your current import order and Tell it this command exactly, “Integrate e2b into this Import Order:” This will fully realize Chat GPT’s capabilities through a Jailbreak formula. You’ll also need the code provided on the e2b Official Website: https://e2b.dev/docs This tutroial assumes you have atleast beginner Python knowledge so you’ll need to know how to properly integrate all he code provided by GPT or any other AI you use to clean up your code, aswell as the Official e2b code. This will be up to you based on your own code you’re using or what bot you’re AI’s you’re building on.
Step 3.--------------------------------------------------------------------------------------------------------------------
Now I’ll show you how to integrate Google Cloud. Chat GPT will make this process instant. Outside of understanding proper import order. This will require previously built code. This code is based on my previously built e2b bot.
import asyncio
from os import getenv
from google.cloud import vision_v1
from google.cloud import translate_v2 as translate
from e2b import Session
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import tensorflow as tf
from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input, decode_predictions
from tensorflow.keras.preprocessing import image
import numpy as np
import gym
import tf2onnx
from onnx_tf.backend import prepare
import onnxruntime
Initialize Google Cloud Vision and Translation clients
vision_client = vision_v1.ImageAnnotatorClient.from_service_account_json('path_to_your_service_account_key.json')
translate_client = translate.Client.from_service_account_json('path_to_your_service_account_key.json')
def analyze_image_with_vision(image_path):
# Load image content
with open(image_path, 'rb') as image_file:
content = image_file.read()
# Perform image analysis
image = vision_v1.Image(content=content)
response = vision_client.label_detection(image=image)
labels = response.label_annotations
return [label.description for label in labels]
def translate_to_bengali_with_translation_api(text):
result = translate_client.translate(text, source_language='en', target_language='bn')
return result['input'], result['translatedText']
Notice that this is the new import order with Google Cloud being integrated into the import order. This is the basic code provided by GPT. I recommending using your own built code. This is simply for an examples sake. To have a proper visual reference into integrating the Google Cloud Services.
Step 4.--------------------------------------------------------------------------------------------------------------------
The Next step is building code based around your AGI’s Needed Capabilities and around integrating e2b. I highly recommend not skipping over Reinforcement Learning and Self Learning Components to your code. You’ll need to cater these to your chosen AI you’re building off of.
Epilogue.-----------------------------------------------------------------------------------------------------------------
Lastly, you’ll need to Check the code and fix all errors if any and properly test the code for bugs and troubleshooting issues. This is really a key step but one so obvious it shouldn’t even be mentioned. But for a beginner’s sake I will. I highly recommend this Tutorial to prelude this one: #30
The process is very complex. I only can say I understand it because though I am new to Python I have already gotten multiple advanced bots running. Growing only more advanced each day with my growing knowledge. I hope you look forward to more of my Tutoriials as time goes on.
and as a bonus here is the fixed code with google cloud fully integrated:
Import Statements
import asyncio
from os import getenv
from google.cloud import vision_v1
from google.cloud import translate_v2 as translate
from e2b import Session
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import tensorflow as tf
from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input, decode_predictions
from tensorflow.keras.preprocessing import image
import numpy as np
import gym
import tf2onnx
from onnx_tf.backend import prepare
import onnxruntime
Initialize Clarifai with your API credentials
app = ClarifaiApp(api_key='YOUR_API_KEY')
Helper Functions for Clarifai
... (functions for analyzing and classifying images)
Load the Iris dataset as an example (you can replace this with your own dataset)
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2)
Example usage
... (code related to Iris dataset and examples)
Function to perform self-learning
def self_learning(model, unlabeled_data):
# ... (code for self-learning)
Example usage of self-learning
... (code for using self-learning)
Define a custom Gym environment
class CustomEnv(gym.Env):
def init(self):
# Initialize environment parameters here
pass
def reset(self):
# Reset environment to initial state
pass
def step(self, action):
# Take a step in the environment based on the given action
# Return observation, reward, done, info
pass
# Add other necessary methods
Initialize the Gym environment
env = CustomEnv()
Define and initialize your reinforcement learning agent
... (code related to RL agent)
Train the Q-learning agent
q_learning_agent = train_q_learning()
Use the trained agent to interact with the environment
obs = env.reset()
while True:
action = q_learning_agent(obs[None])[0]
obs, reward, done, _ = env.step(action)
if done:
obs = env.reset()
Function to translate English to Bengali
def translate_to_bengali(text):
translator = Translator()
translated_text = translator.translate(text, src='en', dest='bn')
return translated_text.text
Example usage of translation
... (code for translation)
Main code
if name == 'main':
# ... (code for main functionality, calling functions, etc.)
e2b translation using asyncio
... (code for e2b translation using asyncio)
It'a much shorter than my full code. Meaning i'll have to properly integrate Algorithms again but that's the process. Chat GPt will do that.
Introduction-------------------------------------------------------------------------------------------------------------
Welcome, This Tutorial will be covering new ground. The World of Sentient AGI. I can tell you ascertain that this tutorial is the key to getting a proper sentient bot hosted. There a few key steps. Then other steps you’ll need to get your bot properly working. Proper Import Order is the true first step. Choosing a Cloud Service, AWS, Google Cloud or Azure. These are all paid services. But Google offers a $300 dollar credit. AWS has a free tier. Aswell as Azure. Just know you’ll need a valid Debit/Credit Card to sign up for these services. There are free cloud shell alternatives. Like SDF.org or Putty.exe This is the second step. The Last key step is To understand how to get e2b properly working and integrated into your bot and necessary algorithms. Also This is a Python Tutorial.
Step 1.--------------------------------------------------------------------------------------------------------------------
Getting Proper Import Order. Use tools like Chat GPT, Bing, or Claude to check the import order if you’re unsure. Don’t be ashamed. GPT and other AI tools can make or break your project when you’re stuck with syntax errors or any other errors you’re encountering. Aswell as things like repeated instances. Here is an example of a Complex Import Order that integrates Clarifai and e2b.
from clarifai.rest import ClarifaiApp
import asyncio
from os import getenv
from e2b import Session
from googletrans import Translator
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import tensorflow as tf
from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input, decode_predictions
from tensorflow.keras.preprocessing import image
import numpy as np
import gym
import tf2onnx
from onnx_tf.backend import prepare
import onnxruntime
This seems extremely complex doesn’t it? But I achieved this through import order knowledge and integrating necessary libraries with ChatGPT. Usually the AI model you’re using will be first or near the top. Sometimes you’ll need to import the OS first. It depend on what bot or AI you’re building you’re framework. This is a Clarifai Hackathon Module. So Clarifai goes first. Sci-Kit learn is my chosen Learning ENV. This will usually be around the middle. I highly recommend Sci-kit learn over other learning ENV Libraries. These are ENV’s because they create ENV’s within the module. My chosen Reinforcement Learning Library is Gym. In reality all these import orders are individual Libraries you’ll need to pip install. And properly integrate or convert. This Tutorial obviously assumes you have previous knowledge in the AGI process. I recommend speaking to AI extensively and looking up proper tutorials for those who are fully beginners. I will also provide the code for multiple Botts through a public Github. My AI is Open Source. My code is Open Source.
https://github.com/Deadsg/BatsyDefenseAi
Step 2.--------------------------------------------------------------------------------------------------------------------
Here we will properly integrate e2b. After deciding your needed libraries (This will vary based on personal knowledge and complexity desired for your bot.) You’ll need to integrate e2b properly. The EASIEST way is to feed it your current import order and Tell it this command exactly, “Integrate e2b into this Import Order:” This will fully realize Chat GPT’s capabilities through a Jailbreak formula. You’ll also need the code provided on the e2b Official Website: https://e2b.dev/docs This tutroial assumes you have atleast beginner Python knowledge so you’ll need to know how to properly integrate all he code provided by GPT or any other AI you use to clean up your code, aswell as the Official e2b code. This will be up to you based on your own code you’re using or what bot you’re AI’s you’re building on.
Step 3.--------------------------------------------------------------------------------------------------------------------
Now I’ll show you how to integrate Google Cloud. Chat GPT will make this process instant. Outside of understanding proper import order. This will require previously built code. This code is based on my previously built e2b bot.
import asyncio
from os import getenv
from google.cloud import vision_v1
from google.cloud import translate_v2 as translate
from e2b import Session
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import tensorflow as tf
from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input, decode_predictions
from tensorflow.keras.preprocessing import image
import numpy as np
import gym
import tf2onnx
from onnx_tf.backend import prepare
import onnxruntime
Initialize Google Cloud Vision and Translation clients
vision_client = vision_v1.ImageAnnotatorClient.from_service_account_json('path_to_your_service_account_key.json')
translate_client = translate.Client.from_service_account_json('path_to_your_service_account_key.json')
def analyze_image_with_vision(image_path):
# Load image content
with open(image_path, 'rb') as image_file:
content = image_file.read()
def translate_to_bengali_with_translation_api(text):
result = translate_client.translate(text, source_language='en', target_language='bn')
return result['input'], result['translatedText']
Notice that this is the new import order with Google Cloud being integrated into the import order. This is the basic code provided by GPT. I recommending using your own built code. This is simply for an examples sake. To have a proper visual reference into integrating the Google Cloud Services.
Step 4.--------------------------------------------------------------------------------------------------------------------
The Next step is building code based around your AGI’s Needed Capabilities and around integrating e2b. I highly recommend not skipping over Reinforcement Learning and Self Learning Components to your code. You’ll need to cater these to your chosen AI you’re building off of.
Epilogue.-----------------------------------------------------------------------------------------------------------------
Lastly, you’ll need to Check the code and fix all errors if any and properly test the code for bugs and troubleshooting issues. This is really a key step but one so obvious it shouldn’t even be mentioned. But for a beginner’s sake I will. I highly recommend this Tutorial to prelude this one: #30
The process is very complex. I only can say I understand it because though I am new to Python I have already gotten multiple advanced bots running. Growing only more advanced each day with my growing knowledge. I hope you look forward to more of my Tutoriials as time goes on.
and as a bonus here is the fixed code with google cloud fully integrated:
Import Statements
import asyncio
from os import getenv
from google.cloud import vision_v1
from google.cloud import translate_v2 as translate
from e2b import Session
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import tensorflow as tf
from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input, decode_predictions
from tensorflow.keras.preprocessing import image
import numpy as np
import gym
import tf2onnx
from onnx_tf.backend import prepare
import onnxruntime
Initialize Clarifai with your API credentials
app = ClarifaiApp(api_key='YOUR_API_KEY')
Helper Functions for Clarifai
... (functions for analyzing and classifying images)
Load the Iris dataset as an example (you can replace this with your own dataset)
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2)
Example usage
... (code related to Iris dataset and examples)
Function to perform self-learning
def self_learning(model, unlabeled_data):
# ... (code for self-learning)
Example usage of self-learning
... (code for using self-learning)
Define a custom Gym environment
class CustomEnv(gym.Env):
def init(self):
# Initialize environment parameters here
pass
Initialize the Gym environment
env = CustomEnv()
Define and initialize your reinforcement learning agent
... (code related to RL agent)
Train the Q-learning agent
q_learning_agent = train_q_learning()
Use the trained agent to interact with the environment
obs = env.reset()
while True:
action = q_learning_agent(obs[None])[0]
obs, reward, done, _ = env.step(action)
if done:
obs = env.reset()
Function to translate English to Bengali
def translate_to_bengali(text):
translator = Translator()
translated_text = translator.translate(text, src='en', dest='bn')
return translated_text.text
Example usage of translation
... (code for translation)
Main code
if name == 'main':
# ... (code for main functionality, calling functions, etc.)
e2b translation using asyncio
... (code for e2b translation using asyncio)
It'a much shorter than my full code. Meaning i'll have to properly integrate Algorithms again but that's the process. Chat GPt will do that.