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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# [WIP] ⚠️"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Google Gemini Overview"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "uv add ipykernel\n",
+ "\n",
+ "uv add google-genai\n",
+ "\n",
+ "uv add pillow"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Initilization"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 122,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Okay, let's break down how AI works, keeping it understandable without getting bogged down in technical jargon. We'll cover the core concepts and processes involved.\n",
+ "\n",
+ "**At its heart, AI aims to enable computers to perform tasks that typically require human intelligence.** This encompasses things like:\n",
+ "\n",
+ "* **Learning:** Improving performance over time based on data.\n",
+ "* **Problem Solving:** Finding solutions to complex issues.\n",
+ "* **Decision Making:** Choosing the best course of action.\n",
+ "* **Understanding Language:** Processing and interpreting written and spoken words.\n",
+ "* **Recognizing Patterns:** Identifying trends and structures in data.\n",
+ "\n",
+ "Here's a breakdown of the key concepts and how they work together:\n",
+ "\n",
+ "**1. Data: The Fuel of AI**\n",
+ "\n",
+ "* AI systems need *vast* amounts of data to learn. This data can be anything:\n",
+ " * **Text:** Documents, articles, code, social media posts\n",
+ " * **Images:** Photos, videos, medical scans\n",
+ " * **Audio:** Speech, music, sounds\n",
+ " * **Numerical data:** Sales figures, sensor readings, scientific measurements\n",
+ "* The *quality* and *quantity* of data are crucial. Bad data leads to poor results.\n",
+ "* Data is used to train the AI model, allowing it to learn patterns and relationships.\n",
+ "\n",
+ "**2. Machine Learning (ML): The Learning Engine**\n",
+ "\n",
+ "* Machine learning is a *subset* of AI focused on giving computers the ability to learn *without being explicitly programmed*.\n",
+ "* Instead of relying on hard-coded rules, ML algorithms identify patterns in data and build models that can make predictions or decisions.\n",
+ "* There are different types of machine learning:\n",
+ " * **Supervised Learning:** The model is trained on labeled data (e.g., images of cats *labeled* as \"cat\" and images of dogs *labeled* as \"dog\"). It learns to predict the label for new, unseen data. Think of it like learning with a teacher.\n",
+ " * **Unsupervised Learning:** The model is trained on unlabeled data. It discovers patterns and structures in the data on its own, such as grouping similar items together. Think of it like exploring and discovering patterns.\n",
+ " * **Reinforcement Learning:** The model learns through trial and error, receiving feedback (rewards or penalties) for its actions. It's often used for tasks like game playing, where the model aims to maximize its reward over time.\n",
+ "\n",
+ "**3. Algorithms: The Step-by-Step Instructions**\n",
+ "\n",
+ "* Algorithms are sets of rules or instructions that the AI model follows to process data, learn, and make decisions.\n",
+ "* ML algorithms vary greatly depending on the task and the type of data. Some common examples include:\n",
+ " * **Linear Regression:** For predicting numerical values based on relationships between variables (e.g., predicting housing prices).\n",
+ " * **Logistic Regression:** For predicting probabilities and classifying data into categories (e.g., spam detection).\n",
+ " * **Decision Trees and Random Forests:** For classification and regression, creating tree-like structures to make decisions based on input features.\n",
+ " * **Support Vector Machines (SVMs):** For classification, finding the best boundary between different categories.\n",
+ " * **Neural Networks:** Inspired by the human brain, using interconnected nodes to process complex data (see Deep Learning below).\n",
+ "\n",
+ "**4. Models: The Results of Learning**\n",
+ "\n",
+ "* After being trained on data using an algorithm, the result is an *AI model*. This model encapsulates what the AI has learned.\n",
+ "* The model can be thought of as a function that takes input data and produces an output (e.g., a prediction, a classification, or an action).\n",
+ "* The accuracy and performance of the model depend on the quality and quantity of data it was trained on and the effectiveness of the chosen algorithm.\n",
+ "\n",
+ "**5. Deep Learning (DL): The Powerful Subset**\n",
+ "\n",
+ "* Deep learning is a more advanced form of machine learning that utilizes *artificial neural networks* with many layers (hence \"deep\").\n",
+ "* These networks can learn complex, hierarchical features from raw data. For example, in an image, a deep learning model might first learn to recognize edges, then shapes, then objects, and finally, the meaning of an image.\n",
+ "* DL has achieved groundbreaking results in areas such as image recognition, natural language processing, and speech recognition.\n",
+ "\n",
+ "**6. Training: The Learning Process**\n",
+ "\n",
+ "* The training process is where the AI model learns from the data.\n",
+ "* It involves:\n",
+ " * **Feeding data:** Giving the algorithm input data and (in the case of supervised learning) the corresponding desired output.\n",
+ " * **Adjusting parameters:** The algorithm adjusts its internal parameters based on the difference between its predictions and the actual values.\n",
+ " * **Optimization:** The goal is to minimize a *loss function* which measures the error in predictions. The model is continuously adjusted until the error is minimized.\n",
+ "* This process can take a significant amount of time and computational power.\n",
+ "\n",
+ "**7. Inference: Using the Trained Model**\n",
+ "\n",
+ "* Once trained, the model is used for *inference* which means making predictions or decisions on new, unseen data.\n",
+ "* This is where the AI system applies what it has learned to perform a specific task.\n",
+ "\n",
+ "**In Summary**\n",
+ "\n",
+ "Here’s a simple analogy:\n",
+ "\n",
+ "Imagine you're teaching a child to recognize cats. You show them many pictures of cats (data) and tell them \"This is a cat.\" (label). The child (the algorithm) looks for patterns in the images (e.g., pointy ears, a tail) and builds a mental model of what a cat looks like. When you show them a new picture, they can (hopefully) identify it as a cat (inference).\n",
+ "\n",
+ "**Key Takeaways:**\n",
+ "\n",
+ "* AI isn't magic; it's a complex set of algorithms and techniques for analyzing data and making decisions.\n",
+ "* Data is the fuel that powers AI systems.\n",
+ "* Machine learning allows computers to learn from data without explicit programming.\n",
+ "* Deep learning is a powerful subfield that utilizes neural networks for complex tasks.\n",
+ "* Training is a crucial process where the AI model learns from data.\n",
+ "\n",
+ "This explanation is a simplified overview, but hopefully it gives you a clearer understanding of the basic principles of how AI works. The field of AI is rapidly evolving, but the fundamentals we've discussed here remain core to its operation.\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "from google import genai\n",
+ "import os\n",
+ "from getpass import getpass\n",
+ "\n",
+ "# Configure the API key\n",
+ "os.environ[\"GOOGLE_API_KEY\"] = os.getenv(\"GOOGLE_API_KEY\") or getpass(\n",
+ " \"Enter Google API Key: \"\n",
+ ")\n",
+ "\n",
+ "client = genai.Client()\n",
+ "\n",
+ "response = client.models.generate_content(\n",
+ " model='gemini-2.0-flash-exp', contents='How does AI work?'\n",
+ ")\n",
+ "print(response.text)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 123,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> Hey Gemini! \n",
+ "\n",
+ "Hey\n",
+ " there! How can I help you today? What's on your mind?\n",
+ "\n",
+ "\n",
+ "None\n"
+ ]
+ }
+ ],
+ "source": [
+ "from google import genai\n",
+ "\n",
+ "client = genai.Client(http_options={'api_version': 'v1alpha'})\n",
+ "model_id = \"gemini-2.0-flash-exp\"\n",
+ "config = {\"response_modalities\": [\"TEXT\"]}\n",
+ "\n",
+ "message = \"Hey Gemini!\"\n",
+ "\n",
+ "async with client.aio.live.connect(model=model_id, config=config) as session:\n",
+ " print(\"> \", message, \"\\n\")\n",
+ " await session.send(message, end_of_turn=True)\n",
+ "\n",
+ " async for response in session.receive():\n",
+ " print(response.text)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 124,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The next total solar eclipse in the United States will occur on **March 30, 2033**, but it will only be visible in Alaska. The path of totality will sweep across the northwestern part of the state, from Utqiagvik down to Nome.\n",
+ "\n",
+ "The next total solar eclipse visible from the contiguous United States will be on **August 22, 2044**. Totality will only be visible in Montana, North Dakota, and South Dakota.\n",
+ "\n",
+ "There will be another total solar eclipse visible in North America on **August 23, 2044**, however, the path of totality from this eclipse will only touch parts of Greenland, Canada, and the U.S.\n",
+ "\n",
+ "\n",
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