Upload gradio_app_py.py

gradio_app_py.py

@@ -0,0 +1,54 @@
+# -*- coding: utf-8 -*-
+"""gradio_app.py
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1OQvi3I_q3WfavYBpjovCYfv2SPYt__pF
+"""
+
+import json
+import gradio as gr
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.text import tokenizer_from_json
+import tensorflow_addons as tfa
+
+# Load the pre-trained model and tokenizer
+model = tf.keras.models.load_model('baseline.h5')
+
+# Assuming you have already loaded the tokenizer configuration from the JSON file.
+# Replace 'path' with the actual path to the directory where 'tokenizer.json' is saved.
+with open('tokenizer.json', 'r', encoding='utf-8') as f:
+    tokenizer_config = json.load(f)
+
+tokenizer = tf.keras.preprocessing.text.tokenizer_from_json(tokenizer_config)
+
+# Define the labels for classification
+labels = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']
+
+def classify_comment(comment):
+    # Tokenize the comment and convert it into sequences
+    comment_sequence = tokenizer.texts_to_sequences([comment])
+    comment_sequence = tf.keras.preprocessing.sequence.pad_sequences(comment_sequence, maxlen=200)
+
+    # Make predictions
+    predictions = model.predict(comment_sequence)[0]
+    results = dict(zip(labels, predictions))
+
+    return results
+
+# Create the Gradio interface
+comment_input = gr.inputs.Textbox(label="Enter your comment here")
+output_text = gr.outputs.Textbox(label="Classification Results")
+
+iface = gr.Interface(
+    fn=classify_comment,
+    inputs=comment_input,
+    outputs=output_text,
+    live=True  # Set to True for live updates without needing to restart the server
+)
+
+# Launch the Gradio app
+iface.launch()
+