def main():
activites = ["Summary", "Named Entity Recognition", "Search", "Keywords"]
choice = st.sidebar.selectbox("Select Activity", activites)
if choice == "Summary":
html_temp = """
<div style="background-color:#16A085;"><p style="color:white;font-size:60px;">Text Summarizer</p></div>
"""
components.html(html_temp)
text = st.text_area("Input Text For Summary", height=300)
if st.button("summarize"):
st.success(summary(text))
text_range = st.sidebar.slider("Summarize words Range", 25, 500)
# Named Entity Recognition
elif choice == "Named Entity Recognition":
html_temp1 = """
<div style="background-color:#16A085;"><p style="color:white;font-size:60px;">Text Tokenizer</p></div>
"""
components.html(html_temp1)
row_data = st.text_area("write Text For Tokenizer")
docx = nlp(row_data)
if st.button("Tokenizer"):
spacy_streamlit.visualize_tokens(
docx, attrs=['text', 'pos_', 'dep_', 'ent_type_'])
if st.button("NER"):
spacy_streamlit.visualize_ner(docx,
labels=nlp.get_pipe('ner').labels)
if st.button("Text Relationship"):
spacy_streamlit.visualize_parser(docx)
#Search Bar
elif choice == "Search":
html_temp4 = """
<div style="background-color:#16A085;"><p style="color:white;font-size:60px;,text-align:center;">Search Bar</p></div>
"""
components.html(html_temp4)
row_text = st.text_input("Search Anything")
google = Google(license=None)
if st.button("search"):
for search_result in google.search(row_text):
st.write(search_result.text)
st.warning(search_result.url)
def calc_main():
st.title("Nimbus Words")
st.sidebar.header("Input Options")
expander_bar = st.beta_expander("How To Use This App")
expander_bar.markdown("""
**Use the Dropdown Box located within the sidebar on the left to choose 1 of the 6 AI text editing features offered by Nimbus Words.**
1) **Summarizer:** Paste in text that will be summarized by our AI model. The first text box will do an automated summary of our program's recommended word count, and the second box beneath that will provide a summary of the exact word count you choose using the slider located within the sidebar.
2) **Tokenizer:** Paste in text that will be analyzed by our AI model. The **Tokenizer** button will provide a breakdown on each word within the phrase, for example 'Google' is an organization, or 'Jeff Bezos' is a proper noun. The **NER** button will display all named entities, for example 'Steve Jobs' is a person. The **Text Relationship** button will display a visual graph of the dependency each word has within a sentence or phrase.
3) **Synonyms:** Paste in text that will be analyzed by our AI model. The **Synonyms** button will provide you with synonyms to the inputted attribute. The **Definition** checkbox will provide definitions for the attribute. The **Example** checkbox will provide examples of the given attribute in a sentence.
4) **Translator:** Paste in text that will be translated by our AI model. The **Translate** button will translate the inputted text into one of the many languages that we have provided, and we will automatically detect which language the inputted text is written in.
5) **Search:** Paste in text that will be preprcoessed by our AI model. The **Search** button will do a filtered search for your input.
6) **Spell Correction:** Paste in text that will be spell-checked by our AI model. The **Correct** button will offer a correct spelling for any grammatical error that are detected. The **Pluralize**, **Singularize**, **Comparative** and **Superlative** checkboxes do exactly as they say, and ouput those options for the input you provided.
""")
activites = [
"Summary", "Tokenizer", "Synonyms", "Translator", "Search",
"Spell Correction"
]
choice = st.sidebar.selectbox("Select Activity", activites)
if choice == "Summary":
st.title('AI Text Summarizer')
text = st.text_area("Input Text For Summary", height=300)
if st.button("Summarize"):
st.success(summary(text))
text_range = st.sidebar.slider("Summarize words Range", 25, 500)
text = st.text_area("Input Text For Summary", height=250)
if st.button("Summarize with Custom Word Count"):
st.warning(summarize(text, word_count=text_range))
# Tokenizer
elif choice == "Tokenizer":
st.title('Text Tokenizer')
row_data = st.text_area("write Text For Tokenizer")
docx = nlp(row_data)
if st.button("Tokenizer"):
spacy_streamlit.visualize_tokens(
docx, attrs=['text', 'pos_', 'dep_', 'ent_type_'])
if st.button("NER"):
spacy_streamlit.visualize_ner(docx,
labels=nlp.get_pipe('ner').labels)
if st.button("Text Relationship"):
spacy_streamlit.visualize_parser(docx)
# synonyms
elif choice == "Synonyms":
st.title('Synonym Generator')
text = st.text_area("Enter Text")
if st.button("Synonyms"):
for syn in wordnet.synsets(text):
for i in syn.lemmas():
st.success(i.name())
if st.checkbox("Definition"):
for syn in wordnet.synsets(text):
st.warning(syn.definition())
if st.checkbox("Example"):
for syn in wordnet.synsets(text):
st.success(syn.examples())
# Translator
elif choice == "Translator":
st.title('Speech Tranlation')
row_text = st.text_area("Enter Your Text For Translation", height=300)
translation_text = TextBlob(row_text)
list1 = ["en", "ta", "pa", "gu", "hi", "ur", "kn", "bn", "te"]
a = st.selectbox("select", list1)
if st.button("search"):
#input1 = TextBlob("Simple is better than complex")
st.success(translation_text.translate(to=a))
#Search Bar
elif choice == "Search":
st.title('Web Search')
row_text = st.text_input("Search Anything")
google = Google(license=None)
if st.button("search"):
for search_result in google.search(row_text):
st.write(search_result.text)
st.warning(search_result.url)
elif choice == "Spell Correction":
st.title('AI Spell Correction')
text_data = st.text_area("Enter Text Here")
a = TextBlob(text_data)
if st.button("Correct"):
st.success(a.correct())
st.title('Pluralize & Singularize')
text_data1 = st.text_input("Enter a word For pluralize / singularize")
if st.checkbox("Pluralize"):
st.warning(pluralize(text_data1))
if st.checkbox("Singularize"):
st.warning(singularize(text_data1))
st.title('Compartitive & Superlative')
text2 = st.text_input("Enter Text For comparative & superlative")
if st.checkbox("Comparative"):
st.success(comparative(text2))
if st.checkbox("Superlative"):
st.success(superlative(text2))
st.markdown(desc_text)
st.write("**ENTITY:**")
st.write(record_entity)
st.write("")
st.write("")
st.write("")
st.write("")
# LANGUAGE DEPENDENCY VISUALISER
text = st.text_input('Enter text to analyse: ')
if text:
nlp = spacy.load("en_core_web_sm")
doc = nlp(text)
spacy_streamlit.visualize_parser(doc)
st.sidebar.write("")
st.sidebar.write("")
st.sidebar.write("")
st.sidebar.write("")
# RESET TEXT FILES
if st.sidebar.button("Reset text files"):
success_file = open("yes.txt", "w")
success_file.close()
fail_file = open("no.txt", "w")
fail_file.close()
else:
def calc_main():
st.write("Nimbus Words")
st.sidebar.header("Input Options")
activites = ["Summary", "Tokenizer","Synonyms","Translator","Search","Spell Correction"]
choice = st.sidebar.selectbox("Select Activity",activites)
if choice == "Summary":
st.title('AI Text Summarizer')
text = st.text_area("Input Text For Summary",height=300)
if st.button("summarize"):
st.success(summary(text))
text_range= st.sidebar.slider("Summarize words Range",25,500)
text = st.text_area("Input Text For Summary",height=250)
if st.button("custom summarization"):
st.warning(summarize(text,word_count=text_range))
# Tokenizer
elif choice == "Tokenizer":
st.title('Text Tokenizer')
row_data = st.text_area("write Text For Tokenizer")
docx= nlp(row_data)
if st.button("Tokenizer"):
spacy_streamlit.visualize_tokens(docx,attrs=['text','pos_','dep_','ent_type_'])
if st.button("NER"):
spacy_streamlit.visualize_ner(docx,labels=nlp.get_pipe('ner').labels)
if st.button("Text Relationship"):
spacy_streamlit.visualize_parser(docx)
# synonyms
elif choice == "Synonyms":
st.title('Synonym Generator')
text = st.text_area("Enter Text")
if st.button("Find"):
for syn in wordnet.synsets(text):
for i in syn.lemmas():
st.success(i.name())
if st.checkbox("Defination"):
for syn in wordnet.synsets(text):
st.warning(syn.definition())
if st.checkbox("Example"):
for syn in wordnet.synsets(text):
st.success(syn.examples())
# Translator
elif choice == "Translator":
st.title('Speech Tranlation')
row_text = st.text_area("Enter Your Text For Translation",height=300)
translation_text = TextBlob(row_text)
list1 = ["en","ta","pa","gu","hi","ur","kn","bn","te"]
a= st.selectbox("select",list1)
if st.button("search"):
#input1 = TextBlob("Simple is better than complex")
st.success(translation_text.translate(to=a))
#Search Bar
elif choice == "Search":
st.title('Web Search')
row_text= st.text_input("Search Anything")
google = Google(license=None)
if st.button("search"):
for search_result in google.search(row_text):
st.write(search_result.text)
st.warning(search_result.url)
elif choice == "Spell Correction":
st.title('AI Spell Correction')
text_data = st.text_area("Enter Text Here")
a = TextBlob(text_data)
if st.button("Correct"):
st.success(a.correct())
st.title('Pluralize & Singularize')
text_data1 = st.text_input("Enter a word For pluralize / singularize")
if st.checkbox("pluralize"):
st.warning(pluralize(text_data1))
if st.checkbox("singularize"):
st.warning(singularize(text_data1))
st.title('Compartitive & Superlative')
text2 = st.text_input("Enter Text For comparative & superlative")
if st.checkbox("comparative"):
st.success(comparative(text2))
if st.checkbox("superlative"):
st.success(superlative(text2))
# POS
_colors, pos_annotated_names = display_sidebar_for_category(
"POS", "Parts of speech")
colors.update(_colors)
# CASES
_colors, cases_annotated_names = display_sidebar_for_category(
"CASES", "Cases and genders")
colors.update(_colors)
# Main part - outputs
with st.beta_expander("Parts of speech annotations"):
list_annotated_text = split_annotated_text(
get_annotated_text("POS", text, pos_annotated_names, colors))
for annotations in list_annotated_text:
annotated_text(*annotations)
with st.beta_expander("Cases and genders annotations"):
list_annotated_text = split_annotated_text(
get_annotated_text("CASES", text, cases_annotated_names, colors))
for annotations in list_annotated_text:
annotated_text(*annotations)
with st.beta_expander("Text tokens table", expanded=False):
st.dataframe(tokens_df)
with st.beta_expander("Visualise parser", expanded=False):
visualize_parser(get_doc(text), title=None)
n_words = st.sidebar.slider("Page Rank", 1, 20, 10)
summary, ranks = page_rank(n_words, page_graph)
word_dict = {word: ranks[word] for word in summary[:n_words]}
plt.barh(list(word_dict.keys()), list(word_dict.values()), align='center')
plt.gca().invert_yaxis()
st.pyplot()
#NER POS
st.subheader("Sentence")
text = st.selectbox("", sentences)
doc = nlp(text)
visualize_ner(doc,
labels=nlp.get_pipe("ner").labels,
show_table=False,
title='Named Entity Recognition')
visualize_parser(doc)
#Umap 3D
st.subheader("Embedding Visualization")
words = [
'weakness', 'suddenly', 'strength', 'laughter', 'cause', 'dementia',
'scar', 'exam', 'moment', 'slowly', 'contract', 'disease', 'appearance',
'rest', 'my', 'dreadful', 'concern', 'rush', 'doctor', 'husband', 'maybe',
'problem', 'part', 'pull', 'compensate', 'concerned', 'heal', 'function',
'probably', 'hate', 'something', 'there', 'person', 'specialise',
'particularly', 'work', 'lot', 'explain', 'most', 'side', 'because',
'thing', 'people', 'damage', 'happy', 'progress', 'hear', 'oh', 'short',
'happen', 'shrinkage', 'age', 'why', 'fine', 'ahead', 'think', 'many',
'again', 'whatever', 'quite', 'rim', 'move', 'ageing', 'go', 'wound',
'okay', 'computer', 'nobody', 'mind', 'memory', 'whatsoever', 'like',
'fair', 'pen', 'fact', 'short-term', 'one', 'pointer', 'chunk', 'anything',