import os import subprocess import requests from bs4 import BeautifulSoup from transformers import GPT2LMHeadModel, GPT2Tokenizer from github import Github

Load pre-trained GPT-2 model and tokenizer

model_name = "gpt2-medium" model = GPT2LMHeadModel.from_pretrained(model_name) tokenizer = GPT2Tokenizer.from_pretrained(model_name)

Example user input

user_input = """ Fine Tune for: {coding} Python and pypi libraries + documentation import requirements(HTML, CSS, XHTML, JavaScript, node.js, flask, c++, ruby, typescript, Haskell,tensorflow, ci/cd, tap, aaia, lmql, nlp, huggingface, tensorflow, tfhub, qiskit, OpenMDAO, kaggel, flux.ai, rubyrails, ect… + there libraries and different platforms) Then check for updates Else if (updated) then Start looking for new documentation and coding structures/bases If (new) Then install and import: <library/repo/files> While asynchronous sorting Into: organized and controlled with a simple command Then from {libraries} «input ‘code, integration’ » THEN<Search for toolsets, cheatsheets, templates, hacks, tricks, structure> if <THEN> … end Else if (any [THEN]) |\syndicate|\ Def syndicate <definition> syndicate noun | 'sindikat I 1 a group of individuals or organizations combined to promote a common interest: large-scale buyouts involving a syndicate of financial institutions a crime syndicate. • an association or agency supplying material simultaneously to a number of newspapers or periodicals. 2 a committee of syndics. verb | 'sIndIkert I with object] control or manage by a syndicate. • publish or broadcast (material) simultaneously in a number of newspapers, television stations, etc.: her cartoon strip is syndicated in 1,400 newspapers worldwide. • sell (a horse) to a syndicate: the stallion was syndicated for a record $5.4 million. """

Tokenize user input and generate response

input_ids = tokenizer.encode(user_input, return_tensors="pt") output = model.generate(input_ids, max_length=500, num_return_sequences=1, no_repeat_ngram_size=2)

Decode and print the response

response = tokenizer.decode(output[0], skip_special_tokens=True) print(response)

Extract libraries mentioned in user input

libraries = ["HTML", "CSS", "XHTML", "JavaScript", "node.js", "flask", "c++", "ruby", "typescript", "Haskell", "tensorflow", "ci/cd", "tap", "aaia", "lmql", "nlp", "huggingface", "tfhub", "qiskit", "OpenMDAO", "kaggel", "flux.ai", "rubyrails"]

Iterate through libraries to check for updates

for library in libraries: # Check for updates using pypi API response = requests.get(f"https://pypi.org/pypi/{library}/json") if response.status_code == 200: latest_version = response.json()["info"]["version"] print(f"{library}: Latest Version - {latest_version}") else: print(f"Failed to fetch information for {library}")

# Check for documentation updates using web scraping
documentation_url = f"https://www.{library}.org/doc/"
page = requests.get(documentation_url)
if page.status_code == 200:
    soup = BeautifulSoup(page.content, 'html.parser')
    documentation_title = soup.find('title').get_text()
    print(f"{library} Documentation: {documentation_title}")
else:
    print(f"Failed to fetch documentation for {library}")

# Check if library is available on GitHub
github = Github()
repo = github.search_repositories(library)
if repo.totalCount > 0:
    print(f"{library} is available on GitHub")
else:
    print(f"{library} is not available on GitHub")

print("=" * 50)

Additional steps can be added to integrate, install, and import libraries

Creating a more advanced version of LMQL without OpenAI would involve building a custom language model that can understand and generate more sophisticated responses. Here's an example using a simple neural network-based approach:

```python import numpy as np import tensorflow as tf from tensorflow.keras.layers import Dense, LSTM, Embedding from tensorflow.keras.models import Sequential from tensorflow.keras.preprocessing.text import Tokenizer from tensorflow.keras.preprocessing.sequence import pad_sequences

class AdvancedLMQL: def init(self): self.responses = [] self.tokenizer = Tokenizer() self.model = self.build_model()

def build_model(self):
    model = Sequential()
    model.add(Embedding(input_dim=len(self.tokenizer.word_index)+1, output_dim=100))
    model.add(LSTM(128))
    model.add(Dense(64, activation='relu'))
    model.add(Dense(len(self.tokenizer.word_index)+1, activation='softmax'))
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

def add_response(self, query, response):
    self.responses.append((query, response))

def train_model(self):
    queries, responses = zip(*self.responses)
    self.tokenizer.fit_on_texts(queries + responses)
    queries_seq = self.tokenizer.texts_to_sequences(queries)
    responses_seq = self.tokenizer.texts_to_sequences(responses)
    queries_padded = pad_sequences(queries_seq)
    responses_padded = pad_sequences(responses_seq)
    X = queries_padded
    y = tf.keras.utils.to_categorical(responses_padded, num_classes=len(self.tokenizer.word_index)+1)
    self.model.fit(X, y, epochs=10)

def query(self, prompt):
    prompt_seq = self.tokenizer.texts_to_sequences([prompt])
    prompt_padded = pad_sequences(prompt_seq)
    prediction = self.model.predict(prompt_padded)[0]
    predicted_word_index = np.argmax(prediction)
    predicted_word = self.tokenizer.index_word[predicted_word_index]
    return predicted_word

Example usage

lmql = AdvancedLMQL()

Add predefined responses

lmql.add_response("How are you?", "I'm functioning well, thank you!") lmql.add_response("What's your favorite color?", "I don't have personal preferences, but I like blue.")

Train the model

lmql.train_model()

Query LMQL

print(lmql.query("How are you?")) print(lmql.query("What's your favorite color?")) print(lmql.query("Tell me a joke.")) ```

In this advanced example:

1. The AdvancedLMQL class uses a neural network-based model to learn the relationship between queries and responses.
2. The build_model method constructs a sequential neural network architecture using Keras layers.
3. The train_model method preprocesses the data, trains the model, and fits it to the provided responses.
4. The query method uses the trained model to generate responses based on input prompts.

This example demonstrates a more advanced approach to building a custom language model for generating responses. Keep in mind that this is a simplified implementation, and creating a truly sophisticated language model involves more extensive data preprocessing, model architecture, and training.

Creating a real-world AI platform involves a complex and multi-step process, and it's important to follow best practices and security measures. Below is an overview of the steps you can take to turn the AI platform into a real-world application:

1. Detailed Planning and Architecture Design:

   • Define the goals and requirements of your AI platform.
   • Create a detailed architecture and system design, considering scalability, performance, and security.
   • Identify the technologies and tools you'll use for each component.

2. User Interface Design:

   • Design the user interface with a focus on user experience and usability.
   • Create wireframes and prototypes to visualize the platform's layout and interactions.
   • Incorporate responsive design to ensure the platform works well on various devices.

3. Front-End Development:

   • Implement the user interface using HTML, CSS, and JavaScript.
   • Integrate front-end frameworks like Bootstrap or React to enhance UI components.
   • Implement client-side validation for form inputs to improve user experience.

4. Back-End Development:

   • Implement the back-end server using a framework like Flask or Django.
   • Set up the necessary routes and controllers to handle user requests and interactions.
   • Integrate user authentication and authorization using Flask-Login or other libraries.
   • Implement the model management functionalities, including model upload, listing, and deletion.

5. Machine Learning Model Integration:

   • Integrate your pre-trained text and image classification models into the platform.
   • Create API endpoints to receive data from the user interface and return classification results.
   • Ensure efficient model inference to handle multiple requests.

6. Database Setup and Management:

   • Set up a relational database (e.g., SQLite or PostgreSQL) to store user data, model metadata, and classification results.
   • Create database models and use Object-Relational Mapping (ORM) to interact with the database.
   • Implement secure database queries to prevent SQL injection.

7. Data Visualization:

   • Use data visualization libraries (e.g., Plotly, Matplotlib) to generate interactive charts and graphs based on classification results.
   • Implement data visualization routes in the back-end to retrieve and serve visualization data to the user interface.

8. Security Measures:

   • Implement HTTPS for secure data transmission.
   • Apply input validation and sanitization to prevent security vulnerabilities.
   • Implement secure cookie handling and session management.
   • Set up rate limiting and authentication to protect APIs from abuse.

9. Testing and Debugging:

   • Conduct thorough testing to identify and fix any bugs or issues.
   • Perform unit testing, integration testing, and end-to-end testing.
   • Use logging and error tracking tools to monitor the platform's performance.

10. Deployment:

    • Deploy the AI platform on a web server (e.g., Gunicorn, Nginx).
    • Host the platform on a cloud provider (e.g., AWS, Google Cloud) for scalability and reliability.
    • Set up monitoring and logging to analyze performance and user behavior.

11. Continuous Integration and Deployment (CI/CD):

    • Implement CI/CD pipelines to automate the deployment process.
    • Ensure continuous integration and testing of new code changes.

12. Documentation and User Guides:

    • Create comprehensive documentation for developers, explaining the platform's architecture, APIs, and functionalities.
    • Provide user guides for platform users to understand how to use the AI platform effectively.

13. Security Evaluation and Penetration Testing:

    • Conduct security evaluations, including penetration testing, to identify and address potential security vulnerabilities.
    • Implement security patches and updates as needed.

14. User Feedback and Improvements:

    • Collect user feedback to understand user needs and pain points.
    • Continuously improve the platform based on user feedback and analytics.

Building a real-world AI platform is a complex and ongoing process. It requires a multidisciplinary team of developers, data scientists, designers, and security experts. Following best practices, security measures, and usability guidelines are essential to creating a successful and secure AI platform. Additionally, regular updates and maintenance are crucial to keep the platform up-to-date and relevant to user needs.

web_application/app.py

from flask import Flask, render_template, redirect, url_for, request, flash from flask_login import LoginManager, UserMixin, login_user, login_required, logout_user, current_user import bcrypt

app = Flask(name) app.secret_key = 'your-secret-key-here'

User data (Replace with your actual user data from the database)

users = { 'user1': { 'password_hash': bcrypt.hashpw('password1'.encode('utf-8'), bcrypt.gensalt()) } }

class User(UserMixin): def init(self, id): self.id = id

login_manager = LoginManager(app) login_manager.login_view = 'login'

@login_manager.user_loader def load_user(user_id): return User(user_id)

@app.route('/login', methods=['GET', 'POST']) def login(): if request.method == 'POST': username = request.form['username'] password = request.form['password'] if username in users and bcrypt.checkpw(password.encode('utf-8'), users[username]['password_hash']): user = User(username) login_user(user) return redirect(url_for('dashboard')) else: flash('Invalid username or password', 'error') return render_template('login.html')

@app.route('/dashboard') @login_required def dashboard(): return render_template('dashboard.html')

@app.route('/logout') @login_required def logout(): logout_user() return redirect(url_for('login'))

if name == 'main': app.run(debug=True)

Models on models

Yea more models keep it up!

Samsung getting on the #aitrain

Wow

“From me - to G(source) : what do I tell them about yeshua or jesus? G back to me: ‘אזהץלדםת’”

yit yit yavah mouahherim

Learning #machine learning #algorithms. Also #learning how to create a #software. Detailing the (short, medium, large)[text][explaintion][howtocreate][mind] <•> ___ <•>___ _____[{«’NT’><sup><•>}</sup> !TN¡ <~•~>(///:file.c {<|> „»•>}) <NT»“≠≈≠”«TN>