Update app.py

app.py

@@ -23,43 +23,45 @@ t5_model = AutoModelWithLMHead.from_pretrained(t5_model_path)
 t5_tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-summarize-news")
 
 def generate_text_summarization(sum_type,article):
-
-    if sum_type == 'BART Extractive Text Summarization':
-        inputs = bart_extractive_tokenizer([article], max_length=1024, return_tensors='pt')
-        summary_ids = bart_extractive_model.generate(inputs['input_ids'], num_beams=4, min_length=60, max_length=300, early_stopping=True)
-
-        summary = [bart_extractive_tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids]
-        print(type(summary))
-        print(summary)
-        summary= summary[0] 
-        doc = readablility_nlp(summary)
-        summary_score = round(doc._.flesch_kincaid_reading_ease,2)
-        summarized_data = {
-            "summary" : summary,
-            "score" : summary_score
-        }
-
-    if sum_type == 'T5 Abstractive Text Summarization':
-        inputs = t5_tokenizer.encode(article, return_tensors="pt", max_length=2048)
-        summary_ids = t5_model.generate(inputs,
-                                num_beams=2,
-                                no_repeat_ngram_size=2,
-                                min_length=100,
-                                max_length=300,
-                                early_stopping=True)
-
-        summary = t5_tokenizer.decode(summary_ids[0], skip_special_tokens=True)
-        print(type(summary))
-        print(summary)
-        doc = readablility_nlp(summary)
-        summary_score = round(doc._.flesch_kincaid_reading_ease,2)
-        summarized_data = {
-            "summary" : summary,
-            "score" : summary_score
-        }
-        
-    save_data_and_sendmail(paragraph, sum_type, result_dic)
-    return summary
+    if article.strip():
+        if sum_type == 'BART Extractive Text Summarization':
+            inputs = bart_extractive_tokenizer([article], max_length=1024, return_tensors='pt')
+            summary_ids = bart_extractive_model.generate(inputs['input_ids'], num_beams=4, min_length=60, max_length=300, early_stopping=True)
+    
+            summary = [bart_extractive_tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids]
+            print(type(summary))
+            print(summary)
+            summary= summary[0] 
+            doc = readablility_nlp(summary)
+            summary_score = round(doc._.flesch_kincaid_reading_ease,2)
+            summarized_data = {
+                "summary" : summary,
+                "score" : summary_score
+            }
+    
+        if sum_type == 'T5 Abstractive Text Summarization':
+            inputs = t5_tokenizer.encode(article, return_tensors="pt", max_length=2048)
+            summary_ids = t5_model.generate(inputs,
+                                    num_beams=2,
+                                    no_repeat_ngram_size=2,
+                                    min_length=100,
+                                    max_length=300,
+                                    early_stopping=True)
+    
+            summary = t5_tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+            print(type(summary))
+            print(summary)
+            doc = readablility_nlp(summary)
+            summary_score = round(doc._.flesch_kincaid_reading_ease,2)
+            summarized_data = {
+                "summary" : summary,
+                "score" : summary_score
+            }
+            
+        save_data_and_sendmail(paragraph, sum_type, result_dic)
+        return summary
+    else:
+        raise gr.Error("Please enter text in inputbox!!!!")
     
 input_text=gr.Textbox(lines=5, label="Paragraph")
 input_radio= gr.Radio(['BART Extractive Text Summarization','T5 Abstractive Text Summarization'],label='Select summarization',value='BART Extractive Text Summarization')