Create new file

app.py

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+import streamlit as st
+from multiprocessing import Process
+from annotated_text import annotated_text
+from bs4 import BeautifulSoup
+import pandas as pd
+import torch
+import math
+import re
+import json
+import requests
+import spacy
+import errant
+import time
+import os
+
+def start_server():   
+    os.system("python3 -m spacy download en_core_web_sm")
+    os.system("uvicorn GrammarTokenize:app --port 8080 --host 0.0.0.0 --workers 2")
+
+def load_models():
+    if not is_port_in_use(8080):
+        with st.spinner(text="Loading models, please wait..."):
+            proc = Process(target=start_server, args=(), daemon=True)
+            proc.start()
+            while not is_port_in_use(8080):
+                time.sleep(1)
+            st.success("Model server started.")
+    else:
+        st.success("Model server already running...")
+    st.session_state['models_loaded'] = True
+
+def is_port_in_use(port):
+    import socket
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        return s.connect_ex(('0.0.0.0', port)) == 0
+
+if 'models_loaded' not in st.session_state:
+    st.session_state['models_loaded'] = False
+
+
+def show_highlights(input_text, corrected_sentence):
+    try:
+        strikeout = lambda x: '\u0336'.join(x) + '\u0336'
+        highlight_text = highlight(input_text, corrected_sentence)
+        color_map = {'d':'#faa', 'a':'#afa', 'c':'#fea'}
+        tokens = re.split(r'(<[dac]\s.*?<\/[dac]>)', highlight_text)
+        annotations = []
+        for token in tokens:
+            soup = BeautifulSoup(token, 'html.parser')
+            tags = soup.findAll()
+            if tags:
+                _tag = tags[0].name
+                _type = tags[0]['type']
+                _text = tags[0]['edit']
+                _color = color_map[_tag]
+
+                if _tag == 'd':
+                    _text = strikeout(tags[0].text)
+
+                annotations.append((_text, _type, _color))
+            else:
+                annotations.append(token)
+        annotated_text(*annotations)
+    except Exception as e:
+        st.error('Some error occured!' + str(e))
+        st.stop()
+
+def show_edits(input_text, corrected_sentence):
+    try:
+        edits = get_edits(input_text, corrected_sentence)
+        df = pd.DataFrame(edits, columns=['type','original word', 'original start', 'original end', 'correct word', 'correct start', 'correct end'])
+        df = df.set_index('type')
+        st.table(df)
+    except Exception as e:
+        st.error('Some error occured!')
+        st.stop()
+
+def highlight(orig, cor):
+      edits = _get_edits(orig, cor)
+      orig_tokens = orig.split()
+
+      ignore_indexes = []
+
+      for edit in edits:
+          edit_type = edit[0]
+          edit_str_start = edit[1]
+          edit_spos = edit[2]
+          edit_epos = edit[3]
+          edit_str_end = edit[4]
+
+          # if no_of_tokens(edit_str_start) > 1 ==> excluding the first token, mark all other tokens for deletion
+          for i in range(edit_spos+1, edit_epos):
+            ignore_indexes.append(i)
+
+          if edit_str_start == "":
+              if edit_spos - 1 >= 0:
+                  new_edit_str = orig_tokens[edit_spos - 1]
+                  edit_spos -= 1
+              else:
+                  new_edit_str = orig_tokens[edit_spos + 1]
+                  edit_spos += 1
+              if edit_type == "PUNCT":
+                st = "<a type='" + edit_type + "' edit='" + \
+                    edit_str_end + "'>" + new_edit_str + "</a>"
+              else:
+                st = "<a type='" + edit_type + "' edit='" + new_edit_str + \
+                    " " + edit_str_end + "'>" + new_edit_str + "</a>"
+              orig_tokens[edit_spos] = st
+          elif edit_str_end == "":
+            st = "<d type='" + edit_type + "' edit=''>" + edit_str_start + "</d>"
+            orig_tokens[edit_spos] = st
+          else:
+            st = "<c type='" + edit_type + "' edit='" + \
+                edit_str_end + "'>" + edit_str_start + "</c>"
+            orig_tokens[edit_spos] = st
+
+      for i in sorted(ignore_indexes, reverse=True):
+        del(orig_tokens[i])
+
+      return(" ".join(orig_tokens))
+
+
+def _get_edits(orig, cor):
+    orig = annotator.parse(orig)
+    cor = annotator.parse(cor)
+    alignment = annotator.align(orig, cor)
+    edits = annotator.merge(alignment)
+
+    if len(edits) == 0:  
+        return []
+
+    edit_annotations = []
+    for e in edits:
+        e = annotator.classify(e)
+        edit_annotations.append((e.type[2:], e.o_str, e.o_start, e.o_end,  e.c_str, e.c_start, e.c_end))
+            
+    if len(edit_annotations) > 0:
+        return edit_annotations
+    else:    
+        return []
+
+def get_edits(orig, cor):
+    return _get_edits(orig, cor)        
+
+def get_correction(input_text):
+    correct_request = "http://0.0.0.0:8080/correct?input_sentence="+input_text
+    correct_response = requests.get(correct_request)
+    correct_json = json.loads(correct_response.text)
+    scored_corrected_sentence = correct_json["scored_corrected_sentence"]
+    
+    corrected_sentence, score = scored_corrected_sentence
+    st.markdown(f'##### Corrected text:')
+    st.write('')
+    st.success(corrected_sentence)
+    exp1 = st.expander(label='Show highlights', expanded=True)
+    with exp1:
+        show_highlights(input_text, corrected_sentence)
+    exp2 = st.expander(label='Show edits')
+    with exp2:
+        show_edits(input_text, corrected_sentence)
+          
+        
+if __name__ == "__main__":
+    
+        st.title('Grammar Styler')
+        st.subheader('Grammar and sentence structure restyler')
+        examples = [
+                     "I looked at the med cabinet and meds are out.  Can you order me more?",
+                    "Been spendin my whole life jus to her dat song",
+                    "whatdjya think about dat?",
+                    "Lets git sum holesome waves and go surfin"
+                    ]
+
+        if not st.session_state['models_loaded']:
+            load_models()                     
+
+        import en_core_web_sm
+        nlp = en_core_web_sm.load()
+        annotator = errant.load('en', nlp)
+
+        st.markdown(f'##### Try it now:')
+        input_text = st.selectbox(
+        label="Choose an example",
+        options=examples
+        )
+        st.write("(or)")
+        input_text = st.text_input(
+            label="Bring your own sentence",
+            value=input_text
+        )
+
+        if input_text.strip(): 
+            get_correction(input_text)