Gemini 2.0: 02/14/25

Converting the UI to Angular Material for Angular 17+

Since Angular 17+ introduced Standalone Components and Signal-Based State Management, we'll build the company website using Angular Material with the latest best practices.

Step 1: Create a New Angular Project

Ensure you have Angular 17+ installed. If not, update your CLI:

npm install -g @angular/cli

Create a new Angular project:

ng new company-website --standalone

Navigate into the project folder:

cd company-website

Step 2: Install Angular Material

Run the following command:

ng add @angular/material

Choose a theme (e.g., Indigo/Pink).
Enable global typography and animations.

Step 3: Set Up Routing

Since the website has multiple pages, enable routing during project creation. If skipped, add it manually:

Edit app.config.ts:

import { ApplicationConfig } from '@angular/core';
import { provideRouter, Routes } from '@angular/router';
import { importProvidersFrom } from '@angular/core';
import { provideAnimations } from '@angular/platform-browser/animations';

import { HomeComponent } from './home/home.component';
import { AboutComponent } from './about/about.component';
import { ServicesComponent } from './services/services.component';
import { ContactComponent } from './contact/contact.component';

const routes: Routes = [
  { path: '', component: HomeComponent },
  { path: 'about', component: AboutComponent },
  { path: 'services', component: ServicesComponent },
  { path: 'contact', component: ContactComponent },
];

export const appConfig: ApplicationConfig = {
  providers: [
    provideRouter(routes),
    provideAnimations(),
  ],
};

Step 4: Create Standalone Components

Run the following commands:

ng g component home --standalone
ng g component about --standalone
ng g component services --standalone
ng g component contact --standalone
ng g component navbar --standalone
ng g component footer --standalone

Each component will be created as a Standalone Component.

Step 5: Create Navigation (Header & Footer)

5.1: Navbar using Angular Material

Modify navbar.component.ts:

import { Component } from '@angular/core';
import { MatToolbarModule } from '@angular/material/toolbar';
import { MatButtonModule } from '@angular/material/button';
import { RouterModule } from '@angular/router';

@Component({
  selector: 'app-navbar',
  standalone: true,
  imports: [MatToolbarModule, MatButtonModule, RouterModule],
  templateUrl: './navbar.component.html',
  styleUrls: ['./navbar.component.css']
})
export class NavbarComponent {}

Modify navbar.component.html:

<mat-toolbar color="primary">
  <span>Company Name</span>
  <span class="spacer"></span>
  <a mat-button routerLink="/">Home</a>
  <a mat-button routerLink="/about">About</a>
  <a mat-button routerLink="/services">Services</a>
  <a mat-button routerLink="/contact">Contact</a>
</mat-toolbar>

Modify navbar.component.css:

.spacer {
  flex: 1;
}

Include <app-navbar></app-navbar> in app.component.html.

5.2: Footer with Social Media Links

Modify footer.component.ts:

import { Component } from '@angular/core';
import { MatDividerModule } from '@angular/material/divider';
import { MatIconModule } from '@angular/material/icon';

@Component({
  selector: 'app-footer',
  standalone: true,
  imports: [MatDividerModule, MatIconModule],
  templateUrl: './footer.component.html',
  styleUrls: ['./footer.component.css']
})
export class FooterComponent {}

Modify footer.component.html:

<footer>
  <mat-divider></mat-divider>
  <p>© 2025 Company Name</p>
  <div class="social-links">
    <a href="https://www.facebook.com" target="_blank">
      <mat-icon>facebook</mat-icon>
    </a>
    <a href="https://wa.me/yourwhatsappnumber" target="_blank">
      <mat-icon>chat</mat-icon>
    </a>
    <a href="https://www.instagram.com" target="_blank">
      <mat-icon>photo_camera</mat-icon>
    </a>
    <a href="https://twitter.com" target="_blank">
      <mat-icon>public</mat-icon>
    </a>
  </div>
</footer>

Modify footer.component.css:

footer {
  text-align: center;
  padding: 10px;
  background: #3f51b5;
  color: white;
}
.social-links a {
  color: white;
  text-decoration: none;
  margin: 0 10px;
  padding: 0 5px;
}

Include <app-footer></app-footer> in app.component.html.

Step 6: Build the Home Page with a Stunning Hero Section

Modify home.component.ts:

import { Component } from '@angular/core';
import { MatButtonModule } from '@angular/material/button';
import { MatCardModule } from '@angular/material/card';

@Component({
  selector: 'app-home',
  standalone: true,
  imports: [MatButtonModule, MatCardModule],
  templateUrl: './home.component.html',
  styleUrls: ['./home.component.css']
})
export class HomeComponent {}

Modify home.component.html:

<mat-card class="hero">
  <div class="hero-content">
    <h1>Welcome to Our Company</h1>
    <p>Your success starts here.</p>
    <button mat-raised-button color="accent" routerLink="/services">Our Services</button>
  </div>
</mat-card>

Modify home.component.css:

.hero {
  height: 80vh;
  display: flex;
  justify-content: center;
  align-items: center;
  text-align: center;
  background: url('https://source.unsplash.com/1600x900/?business,technology') no-repeat center center/cover;
  color: white;
}

.hero-content {
  background: rgba(0, 0, 0, 0.5);
  padding: 20px;
  border-radius: 10px;
}

Step 7: Develop Other Pages

Modify about.component.html, services.component.html, and contact.component.html using MatCard for content presentation.

Example for about.component.html:

<mat-card>
  <mat-card-title>About Us</mat-card-title>
  <mat-card-content>
    <p>We are a leading company in...</p>
  </mat-card-content>
</mat-card>

Step 8: Run & Test the Application

Start the Angular development server:

ng serve

Open http://localhost:4200/ in your browser.
Navigate through pages and test the UI.

Step 9: Deploy the Website

To deploy the website using GitHub Pages, run:

ng build --output-path docs --base-href /company-website/

Commit and push changes, then enable GitHub Pages in the repository settings.

Final Notes

✅ You have successfully built a stunning Angular 17+ Material company website! 🚀
Let me know if you need further enhancements! 🎉

________________________________________________________________________

Creating a Teaching Agent using Pydantic-based RAG for Probability and Statistics involves multiple steps:

Loading a textbook (PDF).
Extracting key concepts.
Generating structured lectures.
Using RAG for answering queries.

📌 Lecture Schedule for "Probability and Statistics"

(Assumption: Using a standard Probability & Statistics textbook in PDF format.)

⏳ 3-Hour Lecture Plan

Time	Topic	Key Concepts
Hour 1	Introduction to Probability & Statistics	Definitions, Importance, Applications
Hour 1.5	Probability Basics	Sample Spaces, Events, Probability Axioms
Hour 2	Random Variables & Distributions	Discrete vs. Continuous, PMF, PDF, CDF
Hour 2.5	Statistical Measures	Mean, Variance, Standard Deviation
Hour 3	Inferential Statistics & Hypothesis Testing	Confidence Intervals, p-values

📌 Python Code for Teaching Agent Using Pydantic RAG

Here’s a step-by-step implementation of a RAG-based teaching assistant using Pydantic and ChromaDB.

1️⃣ Load and Extract Knowledge from PDF

from PyPDF2 import PdfReader
from pydantic import BaseModel
import chromadb

# Define a structured format for storing extracted content
class LectureContent(BaseModel):
    title: str
    text: str

# Load and extract content from a PDF
def extract_text_from_pdf(pdf_path):
    reader = PdfReader(pdf_path)
    text = "\n".join([page.extract_text() for page in reader.pages if page.extract_text()])
    return text

# Example Usage
pdf_content = extract_text_from_pdf("Probability_and_Statistics.pdf")
print("PDF Loaded Successfully!")

2️⃣ Store Extracted Knowledge in ChromaDB

from chromadb.config import Settings

# Initialize ChromaDB
client = chromadb.Client(Settings(persist_directory="chroma_db", chroma_db_impl="duckdb+parquet"))

# Create a collection for lectures
collection = client.get_or_create_collection(name="probability_statistics_knowledge")

# Store structured lecture content
lecture = LectureContent(title="Probability Basics", text=pdf_content)
collection.add(
    documents=[lecture.text],
    metadatas=[{"title": lecture.title}]
)
print("Lecture Content Stored in ChromaDB!")

3️⃣ Query the Knowledge Base Using RAG

from scipy.spatial.distance import cosine
import openai

openai.api_key = "YOUR_OPENAI_API_KEY"

# Generate embeddings using OpenAI
def generate_embedding(text):
    response = openai.Embedding.create(input=text, model="text-embedding-ada-002")
    return response["data"][0]["embedding"]

# Query ChromaDB for the most relevant lecture
def query_lecture(query):
    query_embedding = generate_embedding(query)
    results = collection.query(query_embeddings=[query_embedding], n_results=3)
    return results

# Test Query
user_query = "What is the probability of independent events?"
results = query_lecture(user_query)

# Print the most relevant lecture content
for doc, meta in zip(results["documents"], results["metadatas"]):
    print(f"Topic: {meta['title']}\nContent: {doc}\n")

4️⃣ Interactive Teaching Agent Using Gradio

import gradio as gr

# Teaching Agent Function
def teaching_agent(query):
    results = query_lecture(query)
    if results["documents"]:
        return f"Topic: {results['metadatas'][0]['title']}\n\nAnswer: {results['documents'][0]}"
    return "No relevant information found in the knowledge base."

# Gradio Interface
def create_interface():
    with gr.Blocks() as demo:
        gr.Markdown("## Probability & Statistics Teaching Agent")

        question_input = gr.Textbox(label="Ask a Question", placeholder="Enter your question...")
        output = gr.Textbox(label="Answer", interactive=False)
        query_button = gr.Button("Get Answer")

        query_button.click(teaching_agent, inputs=[question_input], outputs=output)

    return demo

if __name__ == "__main__":
    interface = create_interface()
    interface.launch()

✨ Features of the Teaching Agent

1️⃣ Loads a Probability & Statistics textbook in PDF.
2️⃣ Extracts and structures lecture content using Pydantic.
3️⃣ Stores knowledge in ChromaDB for fast retrieval.
4️⃣ Uses OpenAI embeddings for RAG-based search.
5️⃣ Provides a chatbot interface using Gradio.

📌 Next Steps

Extend RAG to Web Content: Load Wikipedia & research papers.
Enhance Query Understanding: Fine-tune retrieval using cosine similarity.
Voice Integration: Convert text responses into speech.

This teaching agent makes learning Probability & Statistics more interactive and intelligent! 🚀 Let me know if you need more enhancements! 🎯

Gemini 2.0

Friday, 14 February 2025

Temp Teaching Agents