131 lines
4.3 KiB
Plaintext
131 lines
4.3 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": "4e2a9393-7767-488e-a8bf-27c12dca35bd",
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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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"import requests\n",
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"from dotenv import load_dotenv\n",
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"from bs4 import BeautifulSoup\n",
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"from IPython.display import Markdown, display\n",
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"from openai import OpenAI\n",
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"\n",
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"# If you get an error running this cell, then please head over to 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": "7b87cadb-d513-4303-baee-a37b6f938e4d",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load environment variables in a file called .env\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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"# 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 - please head over to the troubleshooting notebook in this folder to identify & fix!\")\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-; please check you're using the right key - see 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 it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n",
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"else:\n",
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" print(\"API key found and looks good so far!\")\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": "019974d9-f3ad-4a8a-b5f9-0a3719aea2d3",
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"metadata": {},
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"outputs": [],
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"source": [
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"openai = OpenAI()\n",
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"\n",
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"# If this doesn't work, try Kernel menu >> Restart Kernel and Clear Outputs Of All Cells, then run the cells from the top of this notebook down.\n",
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"# If it STILL doesn't work (horrors!) then please see the Troubleshooting notebook in this folder for full instructions"
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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": "f4484fcf-8b39-4c3f-9674-37970ed71988",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Step 1: Create your prompts\n",
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"\n",
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"system_prompt = f\"\"\"\n",
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" You are an assistant that will analyze the ratings & reviews from :\\n\\n{reviews_text}\\n\\n and comeup with a summary of how many 5,4,3,2,1 star rating the restuarnat has. \n",
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" You will also come up with a summary of the reviews showing what the customers love about the restaurant and what they dont like. Also extract the name of the restaurant,\n",
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" the location and the cuisine. Respond in markdown\"\"\"\n",
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"user_prompt = \"This is the summary for the restaurant: \"\n",
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"\n",
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"# Step 2: Make the messages list\n",
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"\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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"def generate_review_summary(reviews_text):\n",
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" response = openai.chat.completions.create(\n",
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" model = \"gpt-4o-mini\",\n",
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" messages = messages\n",
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" )\n",
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" return response.choices[0].message.content\n",
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"\n",
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"try:\n",
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" with open('restaurant_reviews.txt', 'r') as file:\n",
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" reviews_text = file.read()\n",
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" \n",
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" # Generate review summary\n",
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" summary = generate_review_summary(reviews_text)\n",
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" display(Markdown(summary))\n",
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"\n",
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"except FileNotFoundError:\n",
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" print(\"The specified reviews file was not found. Please ensure 'restaurant_reviews.txt' is in the correct directory.\")"
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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": "3eccbf35-0a0b-4a1b-b493-aa5c342109cc",
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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": "3.11.13"
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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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