email upload

app.py

@@ -65,15 +65,18 @@ def present(email_file, labels, multilingual=False):
     sentence_list = get_sentences(further_cleaned_text)
     
     entities = parse_query(sentence_list, labels, threshold=0.3, nested_ner=False, model_name="urchade/gliner_base", multilingual=multilingual)
+    
+    # Format entities for DataFrame: Convert list of dicts to list of lists
+    entities_data = [[entity['text'], entity['label']] for entity in entities]
 
     email_info = {
         "Subject": email.subject,
         "From": email.from_,
         "To": email.to,
         "Date": email.date,
-        "Extracted Entities": entities
+        "Extracted Entities": entities_data  # Adjusted for DataFrame
     }
-    return [email_info[key] for key in ["Subject", "From", "To", "Date"]] + [entities]
+    return [email_info[key] for key in ["Subject", "From", "To", "Date"]] + [entities_data]
 
 labels = ["PERSON", "PRODUCT", "DEAL", "ORDER", "ORDER PAYMENT METHOD", "STORE", "LEGAL ENTITY", "MERCHANT", "FINANCIAL TRANSACTION", "UNCATEGORIZED", "DATE"]
 
@@ -93,7 +96,7 @@ demo = gr.Interface(
         gr.components.Textbox(label="From"),
         gr.components.Textbox(label="To"),
         gr.components.Textbox(label="Date"),
-        gr.components.Dataframe(label="Extracted Entities")
+        gr.components.Dataframe(headers=["Text", "Label"], label="Extracted Entities")
     ],
     title="Email Info Extractor",
     description="Upload an email file (.eml) to extract its details and detected entities."