280 lines
7.5 KiB
Plaintext
280 lines
7.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "603cd418-504a-4b4d-b1c3-be04febf3e79",
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"metadata": {},
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"source": [
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"# Article Title Generator\n",
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"\n",
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"Summarization use-case in which the user provides an article, which the LLM will analyze to suggest an SEO-optimized title.\n",
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"\n",
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"**NOTES**:\n",
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"\n",
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"1. This version does NOT support website scrapping. You must copy and paste the required article.\n",
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"2. The following models were configured:\n",
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" a. OpenAI gpt-4o-mini\n",
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" b. Llama llama3.2\n",
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" c. Deepseek deepseek-r1:1.5b\n",
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" It is possible to configure additional models by adding the new model to the MODELS dictionary and its\n",
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" initialization to the CLIENTS dictionary. Then, call the model with --> ***answer =\n",
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" get_answer('NEW_MODEL')***.\n",
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"3. Users are encouraged to assess and rank the suggested titles using any headline analyzer tool online.\n",
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" Example: https://www.isitwp.com/headline-analyzer/. "
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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": "e773daa6-d05e-49bf-ad8e-a8ed4882b77e",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Confirming Llama is loaded\n",
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"!ollama pull llama3.2"
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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": "279b0c00-9bb0-4c7f-9c6d-aa0b108274b9",
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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 os\n",
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"from dotenv import load_dotenv\n",
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"from IPython.display import Markdown, 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": "d4730d8d-3e20-4f3c-a4ff-ed2ac0a8aa27",
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"metadata": {},
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"outputs": [],
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"source": [
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"# set environment variables for OpenAi\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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"# validate API Key\n",
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"if not api_key:\n",
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" raise ValueError(\"No API key was found! Please check the .env file.\")"
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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": "1abbb826-de66-498c-94d8-33369ad01885",
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"metadata": {},
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"outputs": [],
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"source": [
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"# constants\n",
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"MODELS = { 'GPT': 'gpt-4o-mini', \n",
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" 'LLAMA': 'llama3.2', \n",
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" 'DEEPSEEK': 'deepseek-r1:1.5b'\n",
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" }\n",
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"\n",
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"CLIENTS = { 'GPT': OpenAI(), \n",
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" 'LLAMA': OpenAI(base_url='http://localhost:11434/v1', api_key='ollama'),\n",
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" 'DEEPSEEK': OpenAI(base_url='http://localhost:11434/v1', api_key='ollama') \n",
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" }"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6f490fe4-32d5-41f3-890d-ecf4e5e01dd4",
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"metadata": {},
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"source": [
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"### Copy & paste your article (without a title)"
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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": "ddd76319-13ce-480b-baa7-cab6a5c88168",
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"metadata": {},
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"outputs": [],
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"source": [
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"# article - copy & paste your article\n",
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"article = \"\"\"\n",
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" REPLACE WITH YOUR ARTICLE CONTENT\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": "1914afad-dbd8-4c1f-8e68-80b0e5d743a9",
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"metadata": {},
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"outputs": [],
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"source": [
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"# system prompt\n",
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"system_prompt = \"\"\"\n",
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" You are an experienced SEO-focused copywriter. The user will provide an article, and your task is to analyze its content and generate the most effective, keyword-optimized title to maximize SEO performance.Respond in Markdown format.\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": "176cfac7-5e6d-4d4a-a1c4-1b63b60de1f7",
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"metadata": {},
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"outputs": [],
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"source": [
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"# user prompt\n",
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"user_prompt = f\"Following the article to be analyzed. Respond in Markdown format./n/n{article}\"\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": "c45fc7d7-08c9-4e34-b427-b928a219bb94",
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"metadata": {},
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"outputs": [],
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"source": [
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"# message list\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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" ]"
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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": "f67b881f-1040-4cf7-82c5-e85f4c0bd252",
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"metadata": {},
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"outputs": [],
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"source": [
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"# call model and get answer\n",
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"def get_answer(model):\n",
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" # set required client\n",
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" client = CLIENTS[model]\n",
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"\n",
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" # call model\n",
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" response = client.chat.completions.create(\n",
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" model=MODELS[model],\n",
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" messages=messages\n",
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" )\n",
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" \n",
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" # return answer\n",
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" return response.choices[0].message.content\n",
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" "
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]
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},
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{
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"cell_type": "markdown",
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"id": "947b42ed-5b43-486d-8af3-e5b671c1fd0e",
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"metadata": {},
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"source": [
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"### Get OpenAI Suggested Title"
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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": "eb6f66e3-ab99-4f76-9358-896cb43c1fa1",
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"metadata": {},
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"outputs": [],
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"source": [
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"# get openAi answer\n",
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"answer = get_answer('GPT')\n",
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"\n",
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"# display openAi answer\n",
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"display(Markdown(f\"### {MODELS['GPT']} Answer\\n\\n{answer}\" ))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "70073ebf-a00a-416b-854d-642d450cd99b",
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"metadata": {},
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"source": [
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"### Get Llama Suggested Title"
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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": "caa190bb-de5f-45cc-b671-5d62688f7b25",
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"metadata": {},
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"outputs": [],
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"source": [
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"# get Llama answer\n",
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"answer = get_answer('LLAMA')\n",
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"\n",
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"# display Llama answer\n",
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"display(Markdown(f\"### {MODELS['LLAMA']} Answer\\n\\n{answer}\" ))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "811edc4f-20e2-482d-ac89-fae9d1b70bed",
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"metadata": {},
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"source": [
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"### Get Deepseek Suggested Title"
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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": "082628e4-ff4c-46dd-ae5f-76578eb017ad",
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"metadata": {},
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"outputs": [],
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"source": [
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"# get Deepseek answer\n",
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"answer = get_answer('DEEPSEEK')\n",
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"\n",
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"# display Deepseek answer\n",
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"display(Markdown(f\"### {MODELS['DEEPSEEK']} Answer\\n\\n{answer}\" ))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7fc404a6-3a91-4c09-89de-867d3d69b4b2",
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"metadata": {},
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"source": [
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"### Suggested future improvements\n",
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"\n",
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"1. Add website scrapping support to replace copy/pasting of articles.\n",
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"2. Improve the system_prompt to provide specific SEO best practices to adopt during the title generation.\n",
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"3. Rephrase the system_prompt to ensure the model provides a single Title (not a list of suggestions). \n",
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"4. Add the logic that would allow each model to assess the recommendations from the different models and \n",
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" select the best among these. "
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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": "cf7403ac-d43b-4493-98bb-6fee94950cb0",
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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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