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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
+
+# load the model from disk
+filename = 'lstm_model.sav'
+lmodel = pickle.load(open(filename, 'rb'))
+
+# load the model from disk
+filename = 'tokenizer.pickle'
+tok = pickle.load(open(filename, 'rb'))
+
+def main(X):
+    X_test = str(X).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)
+    return {"Persuasive": float(lstm_prob[0]), "Non-Persuasive": 1-float(lstm_prob[0])}
+    
+    
+    
+title = "PrsTalk Application"
+description = """
+This applicaiton takes text as input and predicts to what extent it is persuasive. Click on the example sentence to see how it works! 
+"""
+
+with gr.Blocks(title=title) as demo:
+    gr.Markdown(f"## {title}")
+    gr.Markdown(description)
+    text = gr.Textbox(label="Text:",lines=2, placeholder="Please enter text here ...")
+    submit_btn = gr.Button("Analyze")
+#     tweet_btn = gr.Button("Tweet")
+    with gr.Column(visible=True) as output_col:
+        label = gr.Label(label = "Predicted Label")
+        
+
+    submit_btn.click(
+        get_res_score,
+        text,
+        [label, plot1, plot2, impplot, s1,s2], api_name="PrsTalk"
+    )
+
+    
+    
+    gr.Markdown("## Example:")
+    gr.Examples(["What is performance? Zero to Sixty or Sixty to Zero? How a car performs a quarter mile or a quarter century? Is performance about the joy of driving or the importance of surviving?\
+ To us performance is not about doing one thing well ... it is about doing everything well .. because in the end everything matters.\
+ Performance without compromise.\
+ That is what drives you..... Mercedes Benz","Exhilaration. Unlike any other. Mercedes Benz delivers heart-racing performance with a blend of precision engineering and a little lightning under the hood. For those who see power as the ultimate luxury."], 
+                [text], [label], main, cache_examples=True)
+    
+demo.launch(share = True
+            , auth=("prstalk", "prstalk")
+           )