Merge pull request #698 from abhayas/community-contribution-week1

Community contribution week1

week1/community-contributions/abhayas/week1 EXERCISE.ipynb

@@ -0,0 +1,246 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fe12c203-e6a6-452c-a655-afb8a03a4ff5",
+   "metadata": {},
+   "source": [
+    "# End of week 1 exercise\n",
+    "\n",
+    "To demonstrate your familiarity with OpenAI API, and also Ollama, build a tool that takes a technical question,  \n",
+    "and responds with an explanation. This is a tool that you will be able to use yourself during the course!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "c1070317-3ed9-4659-abe3-828943230e03",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import requests\n",
+    "from dotenv import load_dotenv\n",
+    "from bs4 import BeautifulSoup\n",
+    "from openai import OpenAI\n",
+    "import ollama\n",
+    "from IPython.display import Markdown, clear_output, display"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4a456906-915a-4bfd-bb9d-57e505c5093f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# constants\n",
+    "\n",
+    "MODEL_GPT = 'gpt-4o-mini'\n",
+    "MODEL_LLAMA = 'llama3.2'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a8d7923c-5f28-4c30-8556-342d7c8497c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set up environment\n",
+    "load_dotenv(override=True)\n",
+    "apikey = os.getenv(\"OPENAI_API_KEY\")\n",
+    "openai = OpenAI()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3f0d0137-52b0-47a8-81a8-11a90a010798",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# here is the question; type over this to ask something new\n",
+    "\n",
+    "question = \"\"\"\n",
+    "Please explain what this code does and why:\n",
+    "yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "d9630ca0-fa23-4f80-8c52-4c51b0f25534",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "messages = [\n",
+    "    {\n",
+    "        \"role\":\"system\",\n",
+    "        \"content\" : '''You are a technical adviser. the student is learning llm engineering \n",
+    "                        and you will be asked few lines of codes to explain with an example. \n",
+    "                        mostly in python'''\n",
+    "    },\n",
+    "    {\n",
+    "        \"role\":\"user\",\n",
+    "        \"content\":question\n",
+    "    }\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "60ce7000-a4a5-4cce-a261-e75ef45063b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "This line of code uses a generator in Python to yield values from a set comprehension. Let’s break it down:\n",
+       "\n",
+       "1. **`{book.get(\"author\") for book in books if book.get(\"author\")}`**:\n",
+       "   - This is a set comprehension that creates a set of unique authors from a collection called `books`.\n",
+       "   - `books` is expected to be a list (or any iterable) where each item (called `book`) is likely a dictionary.\n",
+       "   - The expression `book.get(\"author\")` attempts to retrieve the value associated with the key `\"author\"` from each `book` dictionary.\n",
+       "   - The `if book.get(\"author\")` condition filters out any books where the `author` key does not exist or is `None`, ensuring only valid author names are included in the set.\n",
+       "   - Since it’s a set comprehension, any duplicate authors will be automatically removed, resulting in a set of unique authors.\n",
+       "\n",
+       "2. **`yield from`**:\n",
+       "   - The `yield from` syntax is used within a generator function to yield all values from another iterable. In this case, it is yielding each item from the set created by the comprehension.\n",
+       "   - This means that when this generator function is called, it will produce each unique author found in the `books` iterable one at a time.\n",
+       "\n",
+       "### Summary\n",
+       "The line of code effectively constructs a generator that will yield unique authors from a list of book dictionaries, where each dictionary is expected to contain an `\"author\"` key. The use of `yield from` allows the generator to yield each author in the set without further iteration code. This approach is efficient and neatly combines filtering, uniqueness, and yielding into a single line of code."
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "stream = openai.chat.completions.create(\n",
+    "    model=MODEL_GPT,\n",
+    "    messages=messages,\n",
+    "    stream=True)\n",
+    "stringx = \"\"\n",
+    "print(stream)\n",
+    "for x in stream:\n",
+    "    if getattr(x.choices[0].delta, \"content\", None):\n",
+    "        stringx+=x.choices[0].delta.content\n",
+    "        clear_output(wait=True)\n",
+    "        display(Markdown(stringx))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "4d482c69-b61a-4a94-84df-73f1d97a4419",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "Let's break down this line of code:\n",
+       "\n",
+       "**Code Analysis**\n",
+       "\n",
+       "```python\n",
+       "yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n",
+       "```\n",
+       "\n",
+       "**Explanation**\n",
+       "\n",
+       "This is a Python generator expression that uses the `yield from` syntax.\n",
+       "\n",
+       "Here's what it does:\n",
+       "\n",
+       "1. **List Comprehension**: `{...}` is a list comprehension, which generates a new list containing the results of an expression applied to each item in the input iterable (`books`).\n",
+       "2. **Filtering**: The condition `if book.get(\"author\")` filters out any items from the `books` list where `\"author\"` is not present as a key-value pair.\n",
+       "3. **Dictionary Lookup**: `.get(\"author\")` looks up the value associated with the key `\"author\"` in each dictionary (`book`) and returns it if found, or `None` otherwise.\n",
+       "\n",
+       "**What does `yield from` do?**\n",
+       "\n",
+       "The `yield from` keyword is used to \"forward\" the iteration of another generator (or iterable) into this one. In other words, instead of creating a new list containing all the values generated by the inner iterator (`{book.get(\"author\") for book in books if book.get(\"author\")}`), it yields each value **one at a time**, as if you were iterating over the original `books` list.\n",
+       "\n",
+       "**Why is this useful?**\n",
+       "\n",
+       "By using `yield from`, we can create a generator that:\n",
+       "\n",
+       "* Only generates values when they are actually needed (i.e., only when an iteration is requested).\n",
+       "* Does not consume extra memory for creating an intermediate list.\n",
+       "\n",
+       "This makes it more memory-efficient, especially when dealing with large datasets or infinite iterations.\n",
+       "\n",
+       "**Example**\n",
+       "\n",
+       "Suppose we have a list of books with authors:\n",
+       "```python\n",
+       "books = [\n",
+       "    {\"title\": \"Book 1\", \"author\": \"Author A\"},\n",
+       "    {\"title\": \"Book 2\", \"author\": None},\n",
+       "    {\"title\": \"Book 3\", \"author\": \"Author C\"}\n",
+       "]\n",
+       "```\n",
+       "If we apply the generator expression to this list, it would yield:\n",
+       "```python\n",
+       "yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n",
+       "```\n",
+       "The output would be: `['Author A', 'Author C']`\n",
+       "\n",
+       "Note that the second book (\"Book 2\") is skipped because its author is `None`."
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "text = \"\"\n",
+    "for obj in ollama.chat(\n",
+    "    model=MODEL_LLAMA,\n",
+    "    messages=messages,\n",
+    "    stream=True):\n",
+    "    text+=obj.message.content\n",
+    "    clear_output(wait=True)\n",
+    "    display(Markdown(text))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ef1194fc-3c9c-432c-86cc-f77f33916188",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

week1/community-contributions/abhayas/week1_day2_summarize_web_using_ollama_local.ipynb

@@ -0,0 +1,122 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "86282ee7-659b-46b4-b06a-06a54a6b6030",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bs4 import BeautifulSoup\n",
+    "import requests\n",
+    "from IPython.display import Markdown, display\n",
+    "import ollama"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "35f9aacd-8145-4332-b2ab-f805b2ba8ddc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response = requests.get(\"https://news.google.com/home?hl=en-IN&gl=IN&ceid=IN:en\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2adc4cdf-27ba-4be0-8323-bcaff7ef0a48",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bs = BeautifulSoup(response.content, \"html.parser\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d00724a-64cc-4cfc-9556-869626a5aacd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "finalconent = bs.select(\"body\")[0].get_text(separator=\"\\n\", strip=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b89bc4c6-d370-4202-9455-cc382517e45e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "OLLAMA_API = \"http://127.0.0.1:11434/api/chat\"\n",
+    "MODEL = \"llama3.2\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "accb7cc0-4f07-4cbe-87ef-c1c4759e6425",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "messages = [\n",
+    "    {\"role\": \"system\", \"content\": \"Your role to summarize given content from a website igoring the navigations\"},\n",
+    "    {\"role\": \"user\", \"content\": finalconent}\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89d3bc8c-0e52-412b-9b26-788cc15d2495",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "response = ollama.chat(model=MODEL, messages=messages)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8c64ffb8-eeb3-45ef-9e41-8515decacbaf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Markdown(response['message']['content'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72a4eb5d-40c4-4f7c-87ab-a21db32b81c9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}