Update app.py

app.py

@@ -3,13 +3,15 @@ import gradio as gr
 import subprocess
 subprocess.check_call(["pip", "install", "transformers"])
 subprocess.check_call(["pip", "install", "torch"])
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-tokenizer = AutoTokenizer.from_pretrained("balaramas/mbart-enhiriser")
-model = AutoModelForSeq2SeqLM.from_pretrained("balaramas/mbart-enhiriser")
-
+from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
+from transformers import pipeline
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
+tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
 def summariser(ar_en, lang):
+    summ = summarizer(ar_en, max_length=130, min_length=30, do_sample=False)['summary_text']
     tokenizer.src_lang = "en_XX"
-    encoded_ar = tokenizer(ar_en, return_tensors="pt")
+    encoded_ar = tokenizer(summ, return_tensors="pt")
     if(lang=='Hindi'):
         coi='hi_IN'
     if(lang=='Gujrati'):
@@ -28,7 +30,7 @@ def summariser(ar_en, lang):
 iface = gr.Interface(
     fn=summariser, 
     inputs=[gr.Textbox(label="Enter the paragraph in English", placeholder="Type here..."), gr.Radio(["Hindi", "Gujrati", "Bengali", "Tamil"], label="Language to be summarised in:")], 
-    outputs="text",
+    outputs=gr.Textbox(label="Summarised Text"),
     title="English to Indic Summariser"
 )
 iface.launch()