Soumen commited on
Commit
9531d63
1 Parent(s): 54ee49c

Update app.py

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Files changed (1) hide show
  1. app.py +25 -10
app.py CHANGED
@@ -30,6 +30,7 @@ import os
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  import torch
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  from transformers import AutoTokenizer, AutoModelWithLMHead
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  # NLP Pkgs
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  from textblob import TextBlob
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  import spacy
@@ -49,7 +50,11 @@ def text_analyzer(my_text):
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  # tokens = [ token.text for token in docx]
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  allData = [('"Token":{},\n"Lemma":{}'.format(token.text,token.lemma_))for token in docx ]
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  return allData
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-
 
 
 
 
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  # Function For Extracting Entities
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  @st.experimental_singleton
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  def entity_analyzer(my_text):
@@ -68,16 +73,8 @@ def main():
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  + This is a Natural Language Processing(NLP) Based App useful for basic NLP task
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  NER,Sentiment, Spell Corrections and Summarization
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  """)
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-
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- #Text Corrections
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- if st.checkbox("Spell Corrections"):
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- st.subheader("Correct Your Text")
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- message = st.text_area("Enter the Text","Type please ..")
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- if st.button("Spell Corrections"):
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- st.text("Using TextBlob ..")
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- st.success(TextBlob(message).correct())
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  # Entity Extraction
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- elif st.checkbox("Show Named Entities"):
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  st.subheader("Analyze Your Text")
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  message = st.text_area("Enter your Text","Typing Here ..")
@@ -93,6 +90,24 @@ def main():
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  blob = TextBlob(message)
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  result_sentiment = blob.sentiment
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  st.success(result_sentiment)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  def change_photo_state():
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  st.session_state["photo"]="done"
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  st.subheader("Summary section, feed your image!")
 
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  import torch
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  from transformers import AutoTokenizer, AutoModelWithLMHead
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+
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  # NLP Pkgs
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  from textblob import TextBlob
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  import spacy
 
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  # tokens = [ token.text for token in docx]
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  allData = [('"Token":{},\n"Lemma":{}'.format(token.text,token.lemma_))for token in docx ]
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  return allData
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+ @st.experimental_singleton
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+ def load_models():
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+ tokenizer = AutoTokenizer.from_pretrained('gpt2-large')
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+ model = GPT2LMHeadModel.from_pretrained('gpt2-large')
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+ return tokenizer, model
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  # Function For Extracting Entities
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  @st.experimental_singleton
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  def entity_analyzer(my_text):
 
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  + This is a Natural Language Processing(NLP) Based App useful for basic NLP task
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  NER,Sentiment, Spell Corrections and Summarization
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  """)
 
 
 
 
 
 
 
 
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  # Entity Extraction
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+ if st.checkbox("Show Named Entities"):
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  st.subheader("Analyze Your Text")
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  message = st.text_area("Enter your Text","Typing Here ..")
 
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  blob = TextBlob(message)
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  result_sentiment = blob.sentiment
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  st.success(result_sentiment)
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+ #Text Corrections
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+ elif st.checkbox("Spell Corrections"):
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+ st.subheader("Correct Your Text")
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+ message = st.text_area("Enter the Text","Type please ..")
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+ if st.button("Spell Corrections"):
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+ st.text("Using TextBlob ..")
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+ st.success(TextBlob(message).correct())
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+ elif st.checkbox("Text Generation"):
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+ st.subheader("Generate Text")
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+ tokenizer, model = load_models()
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+ message = st.text_area("Enter the Text","Type please ..")
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+ input_ids = tokenizer(message, return_tensors='pt').input_ids
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+ if st.button("Generate"):
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+ st.text("Using Hugging Face Trnsformer, Contrastive Search ..")
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+ output = model.generate(input_ids, max_length=128)
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+ st.success("Output:\n" + 100 * '-')
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+ st.success(tokenizer.decode(output[0], skip_special_tokens=True)))
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+ st.success("" + 100 * '-')
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  def change_photo_state():
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  st.session_state["photo"]="done"
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  st.subheader("Summary section, feed your image!")