Error handling

app.py

@@ -29,10 +29,9 @@ with gr.Blocks(css="#htext span {white-space: pre-line}") as demo:
                         ctrlsum_file = gr.File(label="Input File")
                         ctrlsum_str = gr.TextArea(label="Input String", max_lines=5)
                         with gr.Column():
-                            gr.Markdown("* Set the length of text used for summarization. Length 0 will exert no control over length.")
                             # ctrlsum_file_beams = gr.Number(label="Number of beams for beam search", value=1, precision=0)
                             # ctrlsum_file_sequences = gr.Number(label="Number of generated summaries", value=1, precision=0)
-                            ctrlsum_file_length = gr.Radio(label="Length", value=0, choices=[0, 50, 100, 200])
+                            ctrlsum_file_length = gr.Radio(label="Length", value=0, choices=[0, 50, 100, 200], info="Set the maximum limit for the length of text generated in the output summary. Length 0 implies no restriction on the summary length.")
                             kw = gr.Radio(visible=False)
                             ctrlsum_file_keywords = gr.Textbox(label="Keywords", max_lines=1)
                         with gr.Row():
@@ -40,6 +39,8 @@ with gr.Blocks(css="#htext span {white-space: pre-line}") as demo:
                     ctrlsum_file_output = gr.Textbox(
                         elem_id="htext",
                         label="Summary",
+                        show_label=True,
+                        show_copy_button=True
                     )
                 ctrlsum_file_examples = gr.Examples(
                     examples=[["examples/H01-1042_body.txt", 50, "automatic evaluation technique", "", ""],
@@ -185,4 +186,4 @@ with gr.Blocks(css="#htext span {white-space: pre-line}") as demo:
         )
 
 
-demo.launch(share=False)
+demo.queue().launch(share=False)

controlled_summarization.py

@@ -3,6 +3,7 @@ import torch
 from SciAssist import Summarization
 import os
 import requests
+import gradio as gr
 from datasets import load_dataset
 print(f"Is CUDA available: {torch.cuda.is_available()}")
 # True
@@ -107,7 +108,8 @@ def ctrlsum_for_str(input, length=None, keywords=None) -> List[Tuple[str, str]]:
 def ctrlsum_for_file(input=None, length=None, keywords="", text="", url="") -> List[Tuple[str, str, str]]:
     if input == None and url == "":
         if text == "":
-            return None, "Input cannot be left blank.", None
+            gr.Warning("Input cannot be left blank!")
+            return None, None, None
         else:
             return ctrlsum_for_str(text, length, keywords), text, None
     else:
@@ -132,7 +134,8 @@ def ctrlsum_for_file(input=None, length=None, keywords="", text="", url="") -> L
 
                 filename = download_pdf(url, './cache/')
             else:
-                "Invalid url(Not PDF)!", None, None
+                gr.Warning("Invalid URL (Not PDF)!")
+                return None, None, None
         else:
             filename = input.name
         if keywords != "":
@@ -150,11 +153,15 @@ def ctrlsum_for_file(input=None, length=None, keywords="", text="", url="") -> L
             results = ctrlsum_pipeline.predict(filename,
                                                save_results=False, length=length, keywords=keywords, num_beams=1)
         else:
-            return "File Format Error !", None, filename
+            gr.Warning("File Format Error! Please upload .txt or .pdf files.")
+            return None, None, None
 
         output = []
         for res in results["summary"]:
             output.append(f"{res}\n\n")
+        if results["raw_text"] == "":
+            gr.Warning("Unable to parse File! Please try a different file.")
+            return None, None, None
         return "".join(output), results["raw_text"], filename
 
 

description.py

@@ -44,8 +44,6 @@ To **test on strings**, simply input a string.
 '''
 
 ctrlsum_file_md = '''
-This is the demo for **CocoSciSum**.
-
 ## Controlled Summarization uses FLAN-T5 to generate user-customised summaries from your input file or URL link.
 
 To **test on a file**, the input can be: