173 lines
4.4 KiB
Plaintext
173 lines
4.4 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "712506d5",
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"metadata": {},
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"source": [
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"This is my week 1 exercise experiment.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3058139d",
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"metadata": {},
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"outputs": [],
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"source": [
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"#Imports\n",
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"\n",
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"import os\n",
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"\n",
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"from dotenv import load_dotenv\n",
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"from IPython.display import Markdown, display, update_display\n",
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"from openai import OpenAI"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "dd4d9f32",
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"metadata": {},
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"outputs": [],
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"source": [
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"#Constants andn Initializing GPT\n",
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"\n",
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"MODEL_GPT = 'gpt-4o-mini'\n",
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"MODEL_LLAMA = 'llama3.2'\n",
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"OLLAMA_BASE_URL = \"http://localhost:11434/v1\"\n",
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"openai = OpenAI()\n",
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"ollama = OpenAI(base_url=OLLAMA_BASE_URL, api_key='ollama')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0199945b",
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"metadata": {},
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"outputs": [],
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"source": [
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"#Check API key\n",
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"\n",
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"load_dotenv(override=True)\n",
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"api_key = os.getenv('OPENAI_API_KEY')\n",
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"\n",
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"if api_key and api_key.startswith('sk-proj-') and len(api_key)>10:\n",
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" print(\"API key looks good so far\")\n",
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"else:\n",
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" print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a671fa0f",
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"metadata": {},
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"outputs": [],
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"source": [
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"#Prompts\n",
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"\n",
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"system_prompt = \"\"\"\n",
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"You are a senior software coding master. \n",
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"You will help explain an input of code, check if there are errors and correct them.\n",
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"Show how this code works and suggest other ways of writing this code efficiently if there is an alternative.\n",
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"Respond to a user who is a beginner. \"\"\"\n",
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"\n",
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"question = \"\"\"\n",
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"Please explain what this code does and why:\n",
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"yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n",
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"Show some examples on the use of this code.\n",
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"\"\"\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1fbc6aa5",
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"metadata": {},
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"outputs": [],
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"source": [
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"#Function to stream response of output from OpenAI API\n",
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"\n",
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"def code_examiner_stream(question):\n",
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" stream = openai.chat.completions.create(\n",
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" model=MODEL_GPT,\n",
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" messages=[\n",
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" {\"role\": \"system\", \"content\": system_prompt},\n",
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" {\"role\": \"user\", \"content\": question}\n",
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" ],\n",
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" stream=True\n",
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" ) \n",
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" response = \"\"\n",
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" display_handle = display(Markdown(\"\"), display_id=True)\n",
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" for chunk in stream:\n",
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" response += chunk.choices[0].delta.content or ''\n",
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" update_display(Markdown(response), display_id=display_handle.display_id)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "07d93dba",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"code_examiner_stream(question)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fb7184cb",
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"metadata": {},
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"outputs": [],
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"source": [
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"#Function for Ollama (locally) to reponse with output.\n",
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"\n",
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"def code_examiner_ollama(question):\n",
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" response = ollama.chat.completions.create(\n",
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" model=MODEL_LLAMA,\n",
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" messages=[\n",
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" {\"role\": \"system\", \"content\":system_prompt},\n",
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" {\"role\": \"user\", \"content\": question}\n",
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" ],\n",
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" )\n",
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" result = response.choices[0].message.content\n",
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" display(Markdown(result))\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"code_examiner_ollama(question)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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