Create app.py

app.py

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+import nltk
+import ntlk_utils #nltk are download in different file
+from nltk.corpus import wordnet as wn
+from nltk.tokenize import sent_tokenize
+
+import streamlit as st
+import torch
+from transformers import T5ForConditionalGeneration,T5Tokenizer
+
+import time
+
+
+st.set_page_config(
+    page_title = "Home",
+)
+st.title("NLP Shortcut")
+st.subheader("ParaSummarize is an advanced Natural Language Processing (NLP) model tailored to simplify the process of digesting lengthy paragraphs. With ParaSummarize, complex texts are distilled into concise, coherent summaries with just a click. This invaluable tool empowers researchers, students, and professionals to save time and gain quick insights from extensive content.")
+
+@st.cache_resource
+def get_model():
+    summary_model = T5ForConditionalGeneration.from_pretrained('t5-base')
+    summary_tokenizer = T5Tokenizer.from_pretrained('t5-base')
+    return summary_model,summary_tokenizer
+
+summary_model,summary_tokenizer = get_model()
+
+input_summary = st.text_area("Input the text to get the summary:",placeholder="Enter the text", height=200) # height in pixel
+button = st.button("Press to summarise")
+
+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")
+
+  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=1000)
+
+
+  dec = [tokenizer.decode(ids,skip_special_tokens=True) for ids in outs]
+  summary = dec[0]
+  summary = postprocesstext(summary)
+  summary= summary.strip()
+
+  return summary
+
+if input_summary and button:
+    with st.spinner('Please wait...model is processing your input'):
+        time.sleep(5)
+        summarized_text = summarizer(input_summary,summary_model,summary_tokenizer)
+    st.success("Success")
+    st.balloons()
+    st.write(summarized_text)
+    
+    #print("Original:   ",input_summary)
+    #print("After :   ",summarized_text)