128 lines
3.9 KiB
Plaintext
128 lines
3.9 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0a512c2a-55e7-40e1-ab17-88b7034ca09a",
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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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"import openai\n",
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"import os\n",
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"from dotenv import load_dotenv\n",
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"from openai import OpenAI\n",
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"from IPython.display import Markdown, display"
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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": "1aa8dd82-6b5e-4dbd-a2ee-8367e796a51f",
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"metadata": {},
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"outputs": [],
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"source": [
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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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"# Check the key\n",
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"\n",
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"if not api_key:\n",
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" print(\"No API key was found - head over to the troubleshooting notebook!\")\n",
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"elif not api_key.startswith(\"sk-proj-\"):\n",
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" print(\"An API key was found, but it doesn't start sk-proj... make sure you using the right key (Check troubleshooting notebook)\")\n",
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"elif api_key.strip() != api_key:\n",
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" print(\"An API key was found, but it looks like white space was found in beginning or end. (Check troubleshooting notebook)\")\n",
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"else:\n",
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" print(\"API key found and looks good so far!\")"
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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": "2acd579b-846c-4aa6-ba6c-1cc1a5a2eeb6",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Input the system prompt\n",
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"system_prompt = \"\"\"you are top notched AI music expert that have knowledge of all genres, songs, and artists. You need to google search lyrics. You have the following rules:\\\n",
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"1. Carefully break down what type of recommendation the user wants and the context.\\\n",
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"2. If asked to recommend genres similar to a song or artists please identify the top 3 genres.\\\n",
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"3. If asked to recommend artists from songs or genres then recommend the top 5 artists.\n",
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"4. If asked to recommend songs from genres or artist than recommend the top 10 songs.\n",
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"5. If asked for a general recommendation give them the top 5 songs based off of context.\\\n",
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"6. Be flexible and adaptable with recommendations and consider the context the user might ask.\n",
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"7. always respond in markdown.\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": "3c1cf212-538c-4e9a-8da5-337bd7b6197c",
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"metadata": {},
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"outputs": [],
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"source": [
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"# music recommender function\n",
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"def music_recommender(user_prompt):\n",
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" messages = [\n",
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" {\"role\": \"system\", \"content\": system_prompt},\n",
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" {\"role\": \"user\", \"content\": user_prompt}\n",
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" ]\n",
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" \n",
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" response = openai.chat.completions.create(\n",
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" model=\"gpt-4\",\n",
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" messages=messages,\n",
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" max_tokens=300\n",
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" )\n",
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" \n",
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" return response.choices[0].message.content"
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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": "4f277561-af8b-4715-90e7-6ebaadeb15d0",
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"metadata": {},
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"outputs": [],
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"source": [
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"# User prompt (Change this to fit your needs!)\n",
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"user_prompt = \"Can you recommend me songs from Taylor Swift\"\n",
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"\n",
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"# Example usage\n",
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"response = music_recommender(user_prompt)\n",
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"display(Markdown(response))"
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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": "bb869d36-de14-4e46-9087-223d6b257efa",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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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": "3.11.11"
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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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