Update app.py

app.py

@@ -42,7 +42,7 @@ import pdf2image
 # NLP Pkgs
 from textblob import TextBlob 
 import spacy
-from gensim.summarize import summarize
+#from gensim.summarization import summarize
 import requests
 import cv2
 import numpy as np
@@ -114,14 +114,14 @@ def main():
 + Tokenization(POS tagging) & Lemmatization(root mean) using Spacy
 + Named Entity Recognition(NER)/Trigger word detection using SpaCy
 + Sentiment Analysis using TextBlob
-+ Document/Text Summarization using Gensim/T5 both for Bangla Extractive and English Abstractive.
++ Document/Text Summarization using T5 for English Abstractive.
     	""")
     def change_photo_state():
         st.session_state["photo"]="done"
     st.subheader("Please, feed your image/text, features/services will appear automatically!")
     message = st.text_input("Type your text here!")
-    camera_photo = st.camera_input("Take a photo, Containing English or Bangla texts", on_change=change_photo_state)
-    uploaded_photo = st.file_uploader("Upload Bangla or English Image/ English PDF",type=['jpg','png','jpeg','pdf'], on_change=change_photo_state)
+    camera_photo = st.camera_input("Take a photo, Containing English texts", on_change=change_photo_state)
+    uploaded_photo = st.file_uploader("Upload your PDF",type=['jpg','png','jpeg','pdf'], on_change=change_photo_state)
     if "photo" not in st.session_state:
         st.session_state["photo"]="not done"
     if st.session_state["photo"]=="done" or message:
@@ -178,13 +178,13 @@ def main():
             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(message)
-            summary_result = summarize(text)
-            st.success(summary_result)
+        # 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(message)
+        #     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')