Upload app.py

app.py

@@ -12,16 +12,25 @@ from heapq import nlargest
 import warnings
 from sklearn.feature_extraction.text import TfidfVectorizer
 import numpy as np
+from transformers import PegasusForConditionalGeneration
 
 warnings.filterwarnings("ignore")
 
-pegasus = gr.Interface.load("huggingface/google/pegasus-xsum") 
-
 def get_wiki_original_text(inp):
     text = wikipedia.summary(inp)
     return text
 
 
+def  get_wiki_summary_by_pegasus(inp):
+    text = wikipedia.summary(inp)
+    tokenizer = gr.Interface.load("huggingface/google/pegasus-xsum") 
+    tokens = tokenizer(text, truncation=True, padding="longest", return_tensors="pt")
+    model = PegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum")
+    summary = model.generate(**tokens)
+    return tokenizer.decode(summary)
+    
+
+
 def get_wiki_summary_by_lem(inp):
     text = wikipedia.summary(inp)
 
@@ -111,7 +120,7 @@ sample = [['Europe'],['Great Depression'],['Crocodile Dundee']]
 iface = Parallel(gr.Interface(fn=get_wiki_original_text, inputs=gr.inputs.Textbox(label="Text"), outputs="text", description='Original Text'),
                  gr.Interface(fn=get_wiki_summary_by_lem, inputs=gr.inputs.Textbox(label="Text"), outputs="text", description='Summary 1'),
                  gr.Interface(fn=get_wiki_summary_by_tfidf, inputs=gr.inputs.Textbox(label="Text"), outputs="text", description='Summary 2'),
-                 pegasus,
+                 gr.Interface(fn=get_wiki_summary_by_pegasus, inputs=gr.inputs.Textbox(label="Text"), outputs="text", description='Summary 3'),
                  title= 'Text Summarizer', 
                  description = desc,
                  examples=sample,