Update app.py

app.py

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+import gradio as gr
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+import json
+
+# Load configurations
+NUM_WORDS = 1000
+MAXLEN = 120
+PADDING = 'post'
+OOV_TOKEN = "<OOV>"
+
+with open('tokenizer.json', 'r') as f:
+        tokenizer = tf.keras.preprocessing.text.tokenizer_from_json(f.read())
+
+# Load the trained model
+model = tf.keras.models.load_model("model.h5")
+
+# Function to convert sentences to padded sequences
+def seq_and_pad(sentences, tokenizer, padding, maxlen):
+    sequences = tokenizer.texts_to_sequences(sentences)
+    padded_sequences = pad_sequences(sequences, maxlen=maxlen, padding=padding)
+    return padded_sequences
+
+# Function to predict the class of a sentence
+def predict_sport_class(sentence):
+    # Convert the sentence to a padded sequence
+    sentence_seq = seq_and_pad([sentence], tokenizer, PADDING, MAXLEN)
+    # Make a prediction
+    prediction = model.predict(sentence_seq)
+    # Get the predicted label
+    predicted_label = np.argmax(prediction)
+    # Mapping the label value back to the original label
+    label_mapping = {0: "sport", 1: "business", 2: "politics", 3: "tech", 4: "entertainment"}
+    # Get the predicted class label
+    predicted_class = label_mapping[predicted_label]
+    return predicted_class
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=predict_sport_class,
+    inputs=gr.Textbox(lines=2, placeholder="Enter a sentence here..."),
+    outputs=gr.Label(num_top_classes=1),
+    title="Text Classification App",
+    description="Enter a sentence to classify it into one of the following categories: sport, business, politics, tech, entertainment.",
+)
+
+interface.launch()
+