Update app.py

app.py

@@ -30,6 +30,7 @@ import os
 import torch
 from transformers import AutoTokenizer, AutoModelWithLMHead
 
+
 # NLP Pkgs
 from textblob import TextBlob 
 import spacy
@@ -49,7 +50,11 @@ def text_analyzer(my_text):
 	# tokens = [ token.text for token in docx]
 	allData = [('"Token":{},\n"Lemma":{}'.format(token.text,token.lemma_))for token in docx ]
 	return allData
-
+@st.experimental_singleton
+def load_models():
+    tokenizer = AutoTokenizer.from_pretrained('gpt2-large')
+    model = GPT2LMHeadModel.from_pretrained('gpt2-large')
+    return tokenizer, model
 # Function For Extracting Entities
 @st.experimental_singleton
 def entity_analyzer(my_text):
@@ -68,16 +73,8 @@ def main():
     	+ This is a Natural Language Processing(NLP) Based App useful for basic NLP task
          NER,Sentiment, Spell Corrections and Summarization
     	""")
-
-    #Text Corrections
-	if st.checkbox("Spell Corrections"):
-		st.subheader("Correct Your Text")
-		message = st.text_area("Enter the Text","Type please ..")
-		if st.button("Spell Corrections"):
-			st.text("Using TextBlob ..")
-			st.success(TextBlob(message).correct())
 	# Entity Extraction
-	elif st.checkbox("Show Named Entities"):
+	if st.checkbox("Show Named Entities"):
 		st.subheader("Analyze Your Text")
 
 		message = st.text_area("Enter your Text","Typing Here ..")
@@ -93,6 +90,24 @@ def main():
 			blob = TextBlob(message)
 			result_sentiment = blob.sentiment
 			st.success(result_sentiment)
+    #Text Corrections
+	elif st.checkbox("Spell Corrections"):
+		st.subheader("Correct Your Text")
+		message = st.text_area("Enter the Text","Type please ..")
+		if st.button("Spell Corrections"):
+			st.text("Using TextBlob ..")
+			st.success(TextBlob(message).correct())
+	elif st.checkbox("Text Generation"):
+		st.subheader("Generate Text")
+		tokenizer, model = load_models()
+		message = st.text_area("Enter the Text","Type please ..")
+		input_ids = tokenizer(message, return_tensors='pt').input_ids
+		if st.button("Generate"):
+			st.text("Using Hugging Face Trnsformer, Contrastive Search ..")
+			output = model.generate(input_ids, max_length=128)
+            st.success("Output:\n" + 100 * '-')
+            st.success(tokenizer.decode(output[0], skip_special_tokens=True)))
+            st.success("" + 100 * '-')
 	def change_photo_state():
 		st.session_state["photo"]="done"
 	st.subheader("Summary section, feed your image!")