demo

app.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, BartTokenizer, BartForConditionalGeneration, pipeline
+import numpy as np
+import torch
+from textstat import textstat
+
+
+
+MAX_LEN = 256
+NUM_BEAMS = 4
+EARLY_STOPPING = True
+N_OUT = 4
+
+
+
+cwi_tok = AutoTokenizer.from_pretrained('twigs/cwi-regressor')
+cwi_model = AutoModelForSequenceClassification.from_pretrained('twigs/cwi-regressor')
+simpl_tok = BartTokenizer.from_pretrained('twigs/bart-text2text-simplifier')
+simpl_model = BartForConditionalGeneration.from_pretrained('twigs/bart-text2text-simplifier')
+cwi_pipe = pipeline('text-classification', model=cwi_model, tokenizer=cwi_tok, function_to_apply='none', device=0)
+fill_pipe = pipeline('fill-mask', model=simpl_model, tokenizer=simpl_tok, top_k=1, device=0)
+
+
+def id_replace_complex(s, threshold=0.4):
+
+  # get all tokens
+  tokens = re.compile('\w+').findall(s)
+  cands = [f"{t}. {s}" for t in tokens]
+  # get complex tokens
+  # if score >= threshold select tokens[idx]
+  compl_tok = [tokens[idx] for idx, x in enumerate(
+      cwi_pipe(cands)) if x['score'] >= threshold]
+
+  # potentially parallelizable, depends on desired behaviour
+  for t in compl_tok:
+    idx = s.index(t)
+    s = s[:idx] + '<mask>' + s[idx+len(t):]
+    # get top candidate for mask fill in complex token
+    s = fill_pipe(s)[0]['sequence']
+
+  return s, compl_tok
+
+
+def generate_candidate_text(s, model, tokenizer, tokenized=False):
+
+  out = simpl_tok([s], max_length=256, padding="max_length",  truncation=True, return_tensors='pt').to('cuda') if not tokenized else s
+
+  generated_ids = model.generate(
+      input_ids=out['input_ids'],
+      attention_mask=out['attention_mask'],
+      use_cache=True,
+      decoder_start_token_id=simpl_model.config.pad_token_id,
+      num_beams=NUM_BEAMS,
+      max_length=MAX_LEN,
+      early_stopping=EARLY_STOPPING,
+      num_return_sequences=N_OUT
+  )
+
+  return  [tokenizer.decode(ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)[
+      1:] for ids in generated_ids]
+
+
+def rank_candidate_text(sentences):
+    """ Currently being done with simple FKGL """
+    fkgl_scores = [textstat.flesch_kincaid_grade(s) for s in sentences]
+    return sentences[np.argmin(fkgl_scores)]
+    
+
+def full_pipeline(source, simpl_model, simpl_tok, tokens, lexical=False):
+  
+  modified, complex_words  = id_replace_complex(source, threshold=0.2) if lexical else source, None
+  cands = generate_candidate_text(tokens+modified, simpl_model, simpl_tok)
+  output = rank_candidate_text(cands)
+  return output, complex_words
+
+
+aug_tok = ['c_', 'lev_', 'dep_', 'rank_', 'rat_', 'n_syl_']
+tokens = ['CharRatio', 'LevSim', 'DependencyTreeDepth',
+          'WordComplexity', 'WordRatio']
+
+default_values = [0.8, 0.6, 0.9, 0.8, 0.9, 1.9]
+user_values = default_values
+tok_values = dict((t, default_values[idx]) for idx, t in enumerate(tokens))
+
+example_sentences = ["A matchbook is a small cardboard folder (matchcover) enclosing a quantity of matches and having a coarse striking surface on the exterior.",
+                     "If there are no strong land use controls, buildings are built along a bypass, converting it into an ordinary town road, and the bypass may eventually become as congested as the local streets it was intended to avoid.",
+                     "Plot Captain Caleb Holt (Kirk Cameron) is a firefighter in Albany, Georgia and firmly keeps the cardinal rule of all firemen, \"Never leave your partner behind\".",
+                     "Britpop emerged from the British independent music scene of the early 1990s and was characterised by bands influenced by British guitar pop music of the 1960s and 1970s."]
+
+
+def main():
+
+    st.title("Make it Simple")
+
+    with st.expander("Example sentences"):
+        for s in example_sentences:
+            st.code(body=s)
+
+    with st.form(key="form"):
+        input_sentence = st.text_area("Original sentence")
+        tok = st.multiselect(
+            label="Tokens to augment the sentence", options=tokens, default=tokens)
+        if (tok):
+            st.text("Select the desired intensity")
+            for idx, t in enumerate(tok):
+                user_values[idx] = st.slider(
+                    t, min_value=0., max_value=1., value=tok_values[t], step=0.1, key=t)
+
+        submit = st.form_submit_button("Process")
+        if (submit):
+            
+            tokens = [t+str(v) for t, v in zip(aug_tok, user_values)]
+            output, words = full_pipeline(input_sentence, simpl_model, simpl_tok, tokens)
+            
+            with st.container():
+                st.write("Original sentence:")
+                st.write(input_sentence)
+                st.write("Output sentence:")
+                st.write(output)
+
+
+if __name__ == '__main__':
+    main()

requirements.txt

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+transformers
+torch
+numpy
+textstat