Update app.py

app.py

@@ -42,7 +42,7 @@ import pdf2image
 # NLP Pkgs
 from textblob import TextBlob 
 import spacy
-from gensim.summarization import summarize
+#from gensim.summarization import summarize
 import requests
 import cv2
 import numpy as np
@@ -173,20 +173,20 @@ def main():
         if st.checkbox("Spell Corrections for English"):
             st.success(TextBlob(text).correct())
         if st.checkbox("Text Generation"):
-            ok = st.button("Generate")
-            if ok:
-                tokenizer, model = load_models()
-                input_ids = tokenizer(text, return_tensors='pt').input_ids
-                st.text("Using Hugging Face Transformer, Contrastive Search ..")
-                output = model.generate(input_ids, max_length=128)
-                st.success(tokenizer.decode(output[0], skip_special_tokens=True))
-        if st.checkbox("Mark here, Text Summarization for English or Bangla!"):
+            #ok = st.button("Generate")
+            #if ok:
+            tokenizer, model = load_models()
+            input_ids = tokenizer(text, return_tensors='pt').input_ids
+            st.text("Using Hugging Face Transformer, Contrastive Search ..")
+            output = model.generate(input_ids, max_length=128)
+            st.success(tokenizer.decode(output[0], skip_special_tokens=True))
+        #if st.checkbox("Mark here, Text Summarization for English or Bangla!"):
 			#st.subheader("Summarize Your Text for English and Bangla Texts!")
 			#message = st.text_area("Enter the Text","Type please ..")
 			#st.text("Using Gensim Summarizer ..")
 			#st.success(mess)
-            summary_result = summarize(text)
-            st.success(summary_result)
+            #summary_result = summarize(text)
+            #st.success(summary_result)
         if st.checkbox("Mark to better English Text Summarization!"):
 			#st.title("Summarize Your Text for English only!")
             tokenizer = AutoTokenizer.from_pretrained('t5-base')