Create web_app.py

web_app.py

@@ -0,0 +1,71 @@
+__author__ = "Baishali Dutta"
+__copyright__ = "Copyright (C) 2022 Baishali Dutta"
+__license__ = "Apache License 2.0"
+__version__ = "0.1"
+
+# -------------------------------------------------------------------------
+#                           Import Libraries
+# -------------------------------------------------------------------------
+import pickle
+
+import gradio as gr
+from keras.models import load_model
+from keras.preprocessing.sequence import pad_sequences
+
+from source.config import *
+from source.data_cleaning import clean_text
+
+# -------------------------------------------------------------------------
+#                     Load Existing Model and Tokenizer
+# -------------------------------------------------------------------------
+
+# load the trained model
+rnn_model = load_model(MODEL_LOC)
+
+# load the tokenizer
+with open(TOKENIZER_LOC, 'rb') as handle:
+    tokenizer = pickle.load(handle)
+
+
+# -------------------------------------------------------------------------
+#                           Main Application
+# -------------------------------------------------------------------------
+
+def make_prediction(input_comment):
+    """
+    Predicts the toxicity of the specified comment
+    :param input_comment: the comment to be verified
+    """
+    input_comment = clean_text(input_comment)
+    input_comment = input_comment.split(" ")
+
+    sequences = tokenizer.texts_to_sequences(input_comment)
+    sequences = [[item for sublist in sequences for item in sublist]]
+
+    padded_data = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)
+    result = rnn_model.predict(padded_data, len(padded_data), verbose=1)
+
+    return \
+        {
+            "Toxic": str(result[0][0]),
+            "Very Toxic": str(result[0][1]),
+            "Obscene": str(result[0][2]),
+            "Threat": str(result[0][3]),
+            "Insult": str(result[0][4]),
+            "Hate": str(result[0][5]),
+            "Neutral": str(result[0][6])
+        }
+
+
+comment = gr.inputs.Textbox(lines=17, placeholder="Enter your comment here")
+
+title = "Comments Toxicity Detection"
+description = "This application uses a Bidirectional Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) " \
+              "model to predict the inappropriateness of a comment"
+
+gr.Interface(fn=make_prediction,
+             inputs=comment,
+             outputs="label",
+             title=title,
+             description=description) \
+    .launch()