Update app.py

app.py

@@ -1,73 +1,34 @@
-import logging
-import pandas as pd
-import gradio as gr
-from pymed import PubMed
-import urllib.parse
-import urllib.request
-import ipywidgets as widgets
-
-def search_pubmed(search_term, keywords, max_results, tool, email):
-    # Validate the input
-    if max_results is None or max_results < 1:
-        raise ValueError("Max Results must be a positive integer")
-    
-    # Connect to PubMed database
-    pubmed = PubMed(tool=tool, email=email)
+def search_pubmed_with_gradio(search_term, max_results, include_pubmed_id, include_title, include_abstract):
+    pubmed = PubMed(tool="MyTool", email="aalamel@clemson.edu")
     results = pubmed.query(search_term, max_results=max_results)
-    
-    # Prepare the lists to store article information
-    articleList = []
-    articleInfo = []
-
-    # Try to retrieve the articles and process them
-    try:
-        for article in results:
-            articleDict = article.toDict()
-            articleList.append(articleDict)
-    except Exception as e:
-        # Log the error if it occurs
-        logging.error("Error while processing articles: {}".format(e))
-        raise
-
-    # Store the information of each article in articleInfo
-    for article in articleList:
-        pubmedId = article['pubmed_id'].partition('\n')[0]
-        articleInfo.append({u'pubmed_id': pubmedId,
-                          u'title': article['title'],
-                          u'abstract': article['abstract']
-                          })
-
-    # Convert the article information to a Pandas dataframe
-    cardio_abstract = pd.DataFrame.from_dict(articleInfo)
-    
-    # Filter the dataframe based on the selected keywords
-    cardio_abstract = cardio_abstract[keywords]
-    
-    # Return the filtered dataframe
-    return cardio_abstract
-
-def download_csv(b):
-    download_button.description = "Downloading..."
-    download_button.disabled = True
-    input_dict = interface.process(raw=False)
-    search_term = input_dict["Search Term"]
-    keywords = input_dict["Keywords"]
-    max_results = input_dict["Max Results"]
-    dataframe = search_pubmed(search_term, keywords, max_results)
-    dataframe.to_csv("pubmed_results.csv", index=False)
-    download_button.description = "Download CSV"
-    download_button.disabled = False
-
-inputs = [gr.inputs.Textbox(label="Search Term"),
-gr.inputs.Checkbox(["pubmed_id", "title", "abstract"], label="Keywords"),
-gr.inputs.Slider(minimum=1, maximum=10000, default=100, label="Max Results")]
-
-outputs = [gr.outputs.Dataframe(type="pandas")]
-
-interface = gr.Interface(search_pubmed, inputs, outputs, title="PubMed Search")
-
-result = interface.launch(share=True)
-
-download_button = widgets.Button(description="Download CSV")
-download_button.on_click(download_csv)
-display(download_button)
+    article_list = []
+    for article in results:
+        article_dict = article.toDict()
+        if include_pubmed_id:
+            pubmed_id = article_dict['pubmed_id'].partition('\n')[0]
+        else:
+            pubmed_id = ""
+        if include_title:
+            title = article_dict['title']
+        else:
+            title = ""
+        if include_abstract:
+            abstract = article_dict['abstract']
+        else:
+            abstract = ""
+        article_list.append({'pubmed_id': pubmed_id, 'title': title, 'abstract': abstract})
+    df = pd.DataFrame(article_list)
+    return df
+
+interface = gr.Interface(search_pubmed_with_gradio,
+                         [gr.inputs.Textbox(label="Search Term"),
+                          gr.inputs.Slider(minimum=1, maximum=10000, default=100, label="Max Results"),
+                          gr.inputs.Checkbox("pubmed_id", label="Pubmed ID"),
+                          gr.inputs.Checkbox("title", label="Title"),
+                          gr.inputs.Checkbox("abstract", label="Abstract")],
+                         "dataframe",
+                         title="PubMed Search",
+                         description="Enter a keyword or more than a keyword to search in PubMed database")
+                         
+if __name__ == "__main__":
+    interface.launch()