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| # !pip install --upgrade gradio | |
| import tempfile | |
| import os | |
| import gradio as gr | |
| import requests | |
| import xml.etree.ElementTree as ET | |
| import pandas as pd | |
| import csv | |
| from io import StringIO | |
| from datetime import datetime | |
| import os | |
| import tempfile | |
| # Correctly defined functions for fetching articles and converting them to CSV format | |
| def fetch_articles(keyword, max_results=10): | |
| search_url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi" | |
| search_params = {"db": "pubmed", "term": keyword, "retmax": max_results, "usehistory": "y"} | |
| search_response = requests.get(search_url, params=search_params) | |
| search_tree = ET.fromstring(search_response.content) | |
| id_list = [id_.text for id_ in search_tree.findall('.//Id')] | |
| fetch_url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi" | |
| fetch_params = {"db": "pubmed", "id": ",".join(id_list), "retmode": "xml"} | |
| fetch_response = requests.get(fetch_url, params=fetch_params) | |
| fetch_tree = ET.fromstring(fetch_response.content) | |
| articles = [] | |
| for article in fetch_tree.findall('.//PubmedArticle'): | |
| title = article.find('.//ArticleTitle').text | |
| authors_list = [author.find('.//LastName').text + " " + author.find('.//ForeName').text for author in article.findall('.//Author') if author.find('.//LastName') is not None and author.find('.//ForeName') is not None] | |
| authors_formatted = ", ".join(authors_list) if len(authors_list) <= 2 else authors_list[0] + " et al." | |
| pub_date_element = article.find('.//PubDate') | |
| year = "Unknown" | |
| if pub_date_element is not None: | |
| year_element = pub_date_element.find('Year') | |
| if year_element is not None: | |
| year = year_element.text | |
| journal = article.find('.//Journal/Title').text if article.find('.//Journal/Title') is not None else 'No Journal' | |
| abstract = article.find('.//Abstract/AbstractText').text if article.find('.//Abstract/AbstractText') is not None else 'No Abstract' | |
| article_doi = article.find(".//ArticleId[@IdType='doi']") | |
| doi = article_doi.text if article_doi is not None else "No DOI available" | |
| doi_link = f"https://doi.org/{doi}" if doi != "No DOI available" else "" | |
| articles.append({ | |
| "Title": title, | |
| "Authors": authors_formatted, | |
| "Year": year, | |
| "DOI": doi_link, | |
| "Abstract": abstract, | |
| "Journal": journal, | |
| }) | |
| return articles | |
| def write_articles_to_csv(articles, filename="articles.csv"): | |
| with open(filename, mode='w', newline='', encoding='utf-8') as file: | |
| writer = csv.writer(file) | |
| writer.writerow(['', 'Authors', 'Year', 'Title', 'DOI', 'Abstract', 'Journal']) | |
| for article in articles: | |
| writer.writerow(['[]', article['Authors'], article['Year'], article['Title'], article['DOI'], article['Abstract'], article['Journal']]) | |
| print(f"Articles written to {filename}: {len(articles)}") | |
| def articles_to_csv_string(articles): | |
| output = StringIO() | |
| writer = csv.writer(output) | |
| # writer.writerow(['Authors', 'Year', 'Title', 'DOI', 'Abstract', 'Journal']) | |
| writer.writerow(['Year', 'Title', 'Abstract', 'DOI']) | |
| for article in articles: | |
| # writer.writerow([article['Authors'], article['Year'], article['Title'], article['DOI'], article['Abstract'], article['Journal']]) | |
| writer.writerow([article['Year'], article['Title'], article['Abstract'], article['DOI']]) | |
| output.seek(0) | |
| return output.getvalue() | |
| def generate_filename(keyword1, keyword2, keyword3): | |
| # Format the current timestamp | |
| timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") | |
| # Create a filename that includes the keywords and timestamp | |
| # Note: Filenames need to be safe for the filesystem, so replace or remove characters as necessary | |
| filename = f"articles_{keyword1}_{keyword2}_{keyword3}_{timestamp}.csv".replace(' ', '_').replace('/', '_') | |
| # Ensure the filename length does not exceed filesystem limits | |
| return filename[:255] | |
| def process_inputs(keyword1, keyword2, keyword3): | |
| try: | |
| keywords = f"{keyword1} AND {keyword2} AND {keyword3}" | |
| articles = fetch_articles(keywords, max_results=10) | |
| if not articles: # If no articles were found | |
| df_empty = pd.DataFrame({"Error": ["No articles found or an error occurred."]}) | |
| # Generate a nicer filename | |
| filename = generate_filename(keyword1, keyword2, keyword3) | |
| # Create a temporary file with the specified filename | |
| temp_file_path = os.path.join(tempfile.gettempdir(), filename) | |
| df_empty.to_csv(temp_file_path, index=False) | |
| return df_empty, temp_file_path | |
| # If articles were found | |
| csv_string = articles_to_csv_string(articles) | |
| df = pd.read_csv(StringIO(csv_string)) | |
| filename = generate_filename(keyword1, keyword2, keyword3) | |
| temp_file_path = os.path.join(tempfile.gettempdir(), filename) | |
| df.to_csv(temp_file_path, index=False) | |
| return df, temp_file_path | |
| except Exception as e: | |
| print(f"An error occurred: {e}") | |
| df_empty = pd.DataFrame({"Error": ["An error occurred during processing."]}) | |
| filename = generate_filename(keyword1, keyword2, keyword3) | |
| temp_file_path = os.path.join(tempfile.gettempdir(), filename) | |
| df_empty.to_csv(temp_file_path, index=False) | |
| return df_empty, temp_file_path | |
| iface = gr.Interface( | |
| fn=process_inputs, | |
| inputs=[ | |
| # gr.Textbox(label="First Name", placeholder="Enter your first name here..."), | |
| # gr.Textbox(label="How can I help you today?", placeholder="Your answer (under 50 characters)"), | |
| gr.Textbox(label="Describe your overall concern in life, such as a specific diagnosis?", placeholder="ADHD, CPTSD, depression..."), | |
| gr.Textbox(label="If you had a magic genie, what life situation would you ask to fix?", placeholder="Pressures of University..."), | |
| gr.Textbox(label="Is there a specific therapeutic approach you are intersted in? Leave this blank if there isn't", placeholder="Art therapy, dbt (dialectic behavior therapy), narrative...") | |
| ], | |
| outputs=[ | |
| gr.Dataframe(label="Here are some Research Articles that may interest you today"), | |
| gr.File(label="Download Articles Here. This file will open in a spreadsheet format with links to the recommended articles") | |
| ], | |
| title="MindMender", | |
| description="This tool helps you find research articles related to your mental health concerns. Enter your parameters as keywords." | |
| ) | |
| iface.launch(debug=True) | |