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# Import packages:
import numpy as np
import matplotlib.pyplot as plt
import re
# tensorflow imports:
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import losses
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing
from tensorflow.keras.optimizers import RMSprop
# # keras imports:
from keras.models import Model
from keras.layers import LSTM, Activation, Dense, Dropout, Input, Embedding, RepeatVector, TimeDistributed
from keras.preprocessing.text import Tokenizer
from keras_preprocessing import sequence
from tensorflow.keras.utils import to_categorical
from keras.callbacks import EarlyStopping
from keras.models import Sequential
from keras import layers
from keras.backend import clear_session
import pickle
import gradio as gr
import yake
import spacy
from spacy import displacy
import streamlit as st
import spacy_streamlit
nlp = spacy.load('en_core_web_sm')
import torch
import tensorflow as tf
from transformers import RobertaTokenizer, RobertaModel
from transformers import AutoModelForSequenceClassification
from transformers import TFAutoModelForSequenceClassification
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
model = AutoModelForSequenceClassification.from_pretrained("paragon-analytics/bert_resil")
kw_extractor = yake.KeywordExtractor()
custom_kw_extractor = yake.KeywordExtractor(lan="en", n=2, dedupLim=0.2, top=10, features=None)
max_words = 2000
max_len = 111
# load the model from disk
filename = 'resil_lstm_model.sav'
lmodel = pickle.load(open(filename, 'rb'))
# load the model from disk
filename = 'tokenizer.pickle'
tok = pickle.load(open(filename, 'rb'))
def process_final_text(text):
X_test = str(text).lower()
l = []
l.append(X_test)
test_sequences = tok.texts_to_sequences(l)
test_sequences_matrix = sequence.pad_sequences(test_sequences,maxlen=max_len)
lstm_prob = lmodel.predict(test_sequences_matrix.tolist()).flatten()
lstm_pred = np.where(lstm_prob>=0.5,1,0)
encoded_input = tokenizer(X_test, return_tensors='pt')
output = model(**encoded_input)
scores = output[0][0].detach().numpy()
scores = tf.nn.softmax(scores)
# Get Keywords:
keywords = custom_kw_extractor.extract_keywords(X_test)
letter = []
score = []
for i in keywords:
if i[1]>0.4:
a = "+++"
elif (i[1]<=0.4) and (i[1]>0.1):
a = "++"
elif (i[1]<=0.1) and (i[1]>0.01):
a = "+"
else:
a = "NA"
letter.append(i[0])
score.append(a)
keywords = [(letter[i], score[i]) for i in range(0, len(letter))]
# Get NER:
# NER:
doc = nlp(text)
sp_html = displacy.render(doc, style="ent", page=True, jupyter=False)
NER = (
""
+ sp_html
+ ""
)
return {"Resilience": float(scores.numpy()[1]), "Non-Resilience": float(scores.numpy()[0])},keywords,NER
def main(prob1):
text = str(prob1)
obj = process_final_text(text)
return obj[0],obj[1],obj[2]
title = "Welcome to **ResText** 🪐"
description1 = """
This app takes text (up to a few sentences) and predicts whether the text contains resilience messaging. Resilience messaging is a text message that is about being able to a) "adapt to change” and b) “bounce back after illness or hardship". The predictive model is a fine-tuned RoBERTa NLP model. Just add your text and hit Create & Analyze. Or, simply click on one of the examples to see how it works. ✨
"""
with gr.Blocks(title=title) as demo:
gr.Markdown(f"## {title}")
gr.Markdown(description1)
gr.Markdown("""---""")
prob1 = gr.Textbox(label="Enter Your Text Here:",lines=2, placeholder="Type it here ...")
submit_btn = gr.Button("Create & Analyze")
#text = gr.Textbox(label="Text:",lines=2, placeholder="Please enter text here ...")
#submit_btn2 = gr.Button("Analyze")
with gr.Column(visible=True) as output_col:
label = gr.Label(label = "Predicted Label")
impplot = gr.HighlightedText(label="Important Words", combine_adjacent=False).style(
color_map={"+++": "royalblue","++": "cornflowerblue",
"+": "lightsteelblue", "NA":"white"})
NER = gr.HTML(label = 'NER:')
submit_btn.click(
main,
[prob1],
[label,impplot,NER], api_name="ResText"
)
gr.Markdown("### Click on any of the examples below to see how it works:")
gr.Examples([["Please stay at home and avoid unnecessary trips."],["Please stay at home and avoid unnecessary trips. We will survive this."],["We will survive this."],["Watch today’s news briefing with the latest updates on COVID-19 in Connecticut."],["So let's keep doing what we know works. Let's stay strong, and let's beat this virus. I know we can, and I know we can come out stronger on the other side."]], [prob1], [label,impplot,NER], main, cache_examples=True)
demo.launch() |