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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h1 align=\"center\"><font color=\"yellow\">Researching a Humanitarian Disaster Situation Report Chatbot — Using GPT-4-Turbo and full-context prompting</font></h1>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<font color=\"yellow\">Data Scientist.: Dr.Eddy Giusepe Chirinos Isidro</font>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Link de estudo:\n",
"\n",
"* [Tutorial Disaster Situation](https://towardsdatascience.com/researching-a-humanitarian-disaster-situation-report-chatbot-using-gpt-4-turbo-and-full-context-f742203d495a)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Aqui vamos analisar (seguindo o tutorial, acima) [Relatórios de Situações de Desastres Humanitários na incrível plataforma ReliefWeb](https://reliefweb.int/disasters). \n",
"\n",
"Estes relatórios (conhecidos como “Sitreps”) são vitais para monitorizar e reagir a catástrofes humanitárias em todo o mundo.\n",
"\n",
"\n",
"Usaremos a [ReliefWeb API](https://reliefweb.int/help/api)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import openai\n",
"\n",
"# Substitua sua chave de API OpenAI:\n",
"import openai\n",
"import os\n",
"from dotenv import load_dotenv, find_dotenv\n",
"_ = load_dotenv(find_dotenv()) # read local .env file\n",
"openai.api_key = os.environ['OPENAI_API_KEY']\n",
"\n",
"model = \"gpt-3.5-turbo-1106\"\n",
"\n",
"def run_llm(query, system_prompt, reference_content):\n",
"\n",
" llm_query = {\n",
" \"temperature\": 0.0,\n",
" \"max_tokens\": 1000,\n",
" \"top_p\": 0.95,\n",
" \"frequency_penalty\": 0,\n",
" \"presence_penalty\": 0,\n",
" }\n",
"\n",
" response = openai.ChatCompletion.create(\n",
" model=model,\n",
" messages=[ {\n",
" \"role\":\"system\",\n",
" \"content\": system_prompt\n",
" },\n",
" {\n",
" \"role\":\"user\",\n",
" \"content\": query\n",
" }\n",
" ],\n",
" temperature=llm_query['temperature'],\n",
" max_tokens=llm_query['max_tokens'],\n",
" top_p=llm_query['top_p'],\n",
" frequency_penalty=llm_query['frequency_penalty'],\n",
" presence_penalty=llm_query['presence_penalty'],\n",
" stop=None\n",
" ) \n",
"\n",
" answer = response['choices'][0]['message']['content']\n",
" return answer\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import requests \n",
"import os \n",
"from bs4 import BeautifulSoup \n",
"import re\n",
"import pandas as pd\n",
"import PyPDF2 \n",
"import traceback\n",
"import json\n",
"import ast\n",
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
"import tiktoken\n",
"\n",
"from googletrans import Translator\n",
"translator = Translator()\n",
"\n",
"\n",
"def auto_translate(text):\n",
" \"\"\"\n",
" This function automatically detects language and translates to english \n",
"\n",
" Parameters:\n",
" text(str): The text to be translated\n",
"\n",
" Returns:\n",
" text (str): Translated text if in another language, otherwise \n",
" input text\n",
" \"\"\"\n",
" try:\n",
" lang = translator.detect(text)\n",
" lang = lang.lang\n",
" print(f\"Linguagem detectado: {lang}\")\n",
" q = translator.translate(text, dest='pt')\n",
" text = q.text\n",
" except Exception as e:\n",
" print(\"An exception occurred trying to translate\")\n",
" return text\n",
"\n",
"def get_safe_name(name):\n",
" \"\"\"\n",
" This function takes a string and returns a version of it that is \n",
" safe to use as a filename.\n",
"\n",
" Parameters:\n",
" name (str): The string to be converted to a safe filename.\n",
"\n",
" Returns:\n",
" name (str): The safe filename.\n",
" \"\"\"\n",
" name = str(name)\n",
" name = re.sub(\"[^0-9a-zA-Z]+\", \"_\", name)\n",
" name = re.sub(r\"_$\",\"\", name)\n",
" if len(name) == 0:\n",
" name = 'Unknown' \n",
" return name\n",
"\n",
"def download_pdf(url, download_path): \n",
" \"\"\"\n",
" Function to download a PDF from a URL and save locally\n",
"\n",
" Parameters:\n",
" url (str): Location of online PDF file\n",
" download_path (str): Folder where to save PDF\n",
"\n",
" \"\"\"\n",
" response = requests.get(url) \n",
" with open(download_path, 'wb') as f: \n",
" f.write(response.content) \n",
" \n",
"def save_text(content, file_path): \n",
" \"\"\"\n",
" Function to save text to local file\n",
"\n",
" Parameters:\n",
" content (str): Text to save\n",
" file_path (str): Folder where to save \n",
" \"\"\"\n",
" with open(file_path, 'w') as file: \n",
" print(f'Saving {file_path}')\n",
" file.write(content) \n",
" \n",
"def extract_text_from_pdf(pdf_path): \n",
" \"\"\"\n",
" Function to extract text from PDF file\n",
"\n",
" Parameters:\n",
" pdf_path (str): Path to PDF file\n",
"\n",
" Returns:\n",
" text (str): Text extracted from PDF file\n",
" \"\"\"\n",
" print(pdf_path)\n",
" pdf_reader = PyPDF2.PdfReader(pdf_path) \n",
" text = '' \n",
" for page_num in range(len(pdf_reader.pages)): \n",
" page_obj = pdf_reader.pages[page_num]\n",
" text += page_obj.extract_text() \n",
" return text \n",
"\n",
"def get_rw_data(keyword, filter, sort, fields, endpoint, limit=10, \\\n",
" save_body_to_text=False): \n",
" \"\"\"\n",
" Function to extract data from ReliefWeb API. For API details see:\n",
"\n",
" https://apidoc.rwlabs.org/?utm_medium=blog&utm_source=reliefweb+website&utm_campaign=api+doc+launching+2016_06\n",
"\n",
" Parameters:\n",
" keyword (str): Search string\n",
" filter (dict): ReliefWeb filter json\n",
" sort (dict): ReliefWeb sort json\n",
" fields (list): List of fields to return\n",
" endpoint (str): API Endpoint, eg reports, disasters\n",
" limit (int): Maximum records to return\n",
" save_body_to_text (bool) : Flag to save body to text file, including any PDFs on page\n",
"\n",
" Returns:\n",
" all_data (pandas dataframe): Dataframe of data from API\n",
" \"\"\"\n",
" query = { \n",
" \"appname\": \"myapp\", \n",
" \"query\": { \n",
" \"value\": keyword\n",
" }, \n",
" \"filter\":filter,\n",
" \"sort\": sort,\n",
" \"limit\": limit, \n",
" \"fields\": fields\n",
" } \n",
"\n",
" reliefweb_api_url =\"https://api.reliefweb.int/v1/reports\" # Eddy Adicionou\n",
"\n",
" endpoint = f\"{reliefweb_api_url}/{endpoint}?appname=apidoc&query[value]=\"\n",
" print(f\"Getting {endpoint} ...\")\n",
" \n",
" all_data =[]\n",
" response = requests.post(endpoint, json=query) \n",
" if response.status_code == 200: \n",
" data = response.json() \n",
" for article in data[\"data\"]: \n",
" article_url = article['fields']['url'] \n",
" try:\n",
" r = article['fields']\n",
" print(article_url)\n",
" article_response = requests.get(article_url) \n",
" if save_body_to_text:\n",
" soup = BeautifulSoup(article_response.text, 'html.parser') \n",
" main_content = [p.text for p in soup.find_all('p')] \n",
" article_text = ' '.join(main_content)\n",
" save_text(article_text, docs_folder + '/{}.txt'.format(get_safe_name(article['fields']['title']))) \n",
" for link in soup.find_all('a'): \n",
" href = link.get('href') \n",
" if href.endswith('.pdf'): \n",
" download_path = os.path.join(docs_folder, href.split('/')[-1]) \n",
" if href.startswith('/attachments'):\n",
" pdf_url = f'{reliefweb_pdf_url}{href}'\n",
" else:\n",
" pdf_url = href\n",
" download_pdf(pdf_url, download_path) \n",
" print(f\". Downloaded PDF {download_path} from {pdf_url}\")\n",
" article_text = extract_text_from_pdf(download_path)\n",
" r['article_text'] = article_text\n",
" r['reliefweb_query'] = keyword\n",
" all_data.append(r)\n",
" except Exception as e:\n",
" print(f\"An exception occurred trying to extract {article_url}\")\n",
" tb_str = ''.join(traceback.format_exception(None, e, e.__traceback__))\n",
" print(tb_str)\n",
"\n",
" all_data = pd.DataFrame(all_data)\n",
" for f in ['disaster','theme']:\n",
" if f in list(all_data.columns):\n",
" all_data[f] = all_data[f].astype(str)\n",
" return all_data \n",
" else: \n",
" print(f\"Request failed with status {response.status_code} {response.text}\") \n",
" return None "
]
},
{
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}
],
"metadata": {
"kernelspec": {
"display_name": "venv_GPT4_Disaster",
"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": "3.10.12"
}
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"nbformat": 4,
"nbformat_minor": 2
}
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