Update app.py

app.py

@@ -1,10 +1,16 @@
+# !pip install --upgrade gradio
+
+import tempfile
+import os
 import gradio as gr
-print(gr.__version__)
 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):
@@ -34,7 +40,6 @@ def fetch_articles(keyword, max_results=10):
 
         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'
-       # mesh_terms = [mesh.text for mesh in article.findall('.//MeshHeading/DescriptorName')]
         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 ""
@@ -46,7 +51,6 @@ def fetch_articles(keyword, max_results=10):
             "DOI": doi_link,
             "Abstract": abstract,
             "Journal": journal,
-           # "MeSH Terms": "; ".join(mesh_terms)
         })
 
     return articles
@@ -68,19 +72,44 @@ def articles_to_csv_string(articles):
     output.seek(0)
     return output.getvalue()
 
-def process_inputs(first_name, help_message, keyword1, keyword2, keyword3):
-    if len(help_message) > 50:
-        return pd.DataFrame([{"Error": "Message exceeds 50 characters. Please shorten your message."}])
-    
-    # Combine keywords with "AND" for a broader search, not strictly as MeSH terms
-    keywords = f"{keyword1} AND {keyword2} AND {keyword3}"
-    articles = fetch_articles(keywords, max_results=10)
-    if articles:
-        csv_string = articles_to_csv_string(articles)  # Convert articles to CSV string
-        df = pd.read_csv(StringIO(csv_string))  # Convert CSV string to DataFrame
-        return df
-    else:
-        return pd.DataFrame([{"Error": "No articles found for the given keywords."}])
+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=[
@@ -90,7 +119,11 @@ iface = gr.Interface(
         gr.Textbox(label="What is this session's presenting concern?", placeholder="anxiety..."),
         gr.Textbox(label="What type of activity are you interested in?", placeholder="Art therapy, dbt, narrative...")
     ],
-    outputs=gr.Dataframe(label="Related Research Articles"),
+    outputs=[
+    gr.Dataframe(label="Related Research Articles"),
+    gr.File(label="Download Articles as CSV")
+    ],
+
     title="Workshop Session Planner",
     description="This tool helps you find research articles related to your professional practice. Enter your parameters as keywords."
 )