Create app.py

app.py

@@ -0,0 +1,63 @@
+import gradio as gr
+
+import nltk
+import random
+import numpy as np
+import torch
+from transformers import T5ForConditionalGeneration,T5Tokenizer
+summary_model = T5ForConditionalGeneration.from_pretrained('t5-base')
+summary_tokenizer = T5Tokenizer.from_pretrained('t5-base')
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+summary_model = summary_model.to(device)
+
+
+nltk.download('punkt')
+nltk.download('brown')
+nltk.download('wordnet')
+from nltk.corpus import wordnet as wn
+from nltk.tokenize import sent_tokenize
+
+def set_seed(seed: int):
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+
+set_seed(42)
+
+def postprocesstext (content):
+  final=""
+  for sent in sent_tokenize(content):
+    sent = sent.capitalize()
+    final = final +" "+sent
+  return final
+
+def summarizer(text,model,tokenizer):
+  text = text.strip().replace("\n"," ")
+  text = "summarize: "+text
+  # print (text)
+  max_len = 512
+  encoding = tokenizer.encode_plus(text,max_length=max_len, pad_to_max_length=False,truncation=True, return_tensors="pt").to(device)
+
+  input_ids, attention_mask = encoding["input_ids"], encoding["attention_mask"]
+
+  outs = model.generate(input_ids=input_ids,
+                                  attention_mask=attention_mask,
+                                  early_stopping=True,
+                                  num_beams=3,
+                                  num_return_sequences=1,
+                                  no_repeat_ngram_size=2,
+                                  min_length = 75,
+                                  max_length=300)
+
+
+  dec = [tokenizer.decode(ids,skip_special_tokens=True) for ids in outs]
+  summary = dec[0]
+  summary = postprocesstext(summary)
+  summary= summary.strip()
+
+  return summary
+
+demo = gr.Interface(fn=summarizer, inputs="text", outputs="text")
+demo.launch()