Create app.py

app.py

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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')
+
+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)
+    
+    # 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(lstm_prob[0]), "Non-Resilience": 1-float(lstm_prob[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 = """
+Just add your text and hit Create & Analyze ✨   
+"""
+
+description2 = """
+Although encouraged, you don't have to fill all the boxes. Just try the ones that matter to you. After getting your first score, modify your answers and hit Create & Analyze again 🤞
+""" 
+
+with gr.Blocks(title=title) as demo:
+    gr.Markdown(f"## {title}")
+    gr.Markdown("""![marketing](file/marketing.jpg)""")
+    gr.Markdown(description1)
+    gr.Markdown("""---""")
+    gr.Markdown(description2)
+    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="ResTalk"
+    )
+
+    
+    
+    gr.Markdown("### Click on any of the examples below to see how it works:")
+    gr.Examples([["It is difficult to write persuasive product descriptions."], ["Talking to your friends about their problems with drugs and alcohol might not be easy."], ["When experiencing depression, I couldn't get out of bed or focus.", ["Up to 6 million homeless animals enter shelters nationwide every year."]], [prob1], [label,impplot,NER], main, cache_examples=True)
+    
+demo.launch()