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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
# Define examples
examples = [
["The team won the championship in a thrilling match!"],
["The stock market saw a significant drop today."],
["The prime minister announced new economic reforms."],
["The latest smartphone has cutting-edge features."],
["The actor delivered a stellar performance in the new movie."],
]
# Custom CSS for a fascinating dark theme
custom_css = """
body {
background: linear-gradient(135deg, #1a1a2e, #16213e, #0f3460); /* Dark blue gradient */
color: #ff3e3e; /* Vibrant red text color */
font-family: 'Arial', sans-serif;
}
h1, h2, p {
color: #ff3e3e; /* Red for headings and descriptions */
text-shadow: 2px 2px 4px #000000; /* Text shadow for a glowing effect */
}
input[type="text"] {
background-color: #16213e; /* Dark input background */
color: #ffffff; /* White text in input fields */
border: 2px solid #0f3460; /* Blue border */
border-radius: 8px;
}
button {
background: linear-gradient(45deg, #ff3e3e, #0f3460); /* Gradient button */
color: #ffffff;
border: none;
border-radius: 8px;
padding: 10px 20px;
font-size: 16px;
cursor: pointer;
box-shadow: 2px 2px 10px rgba(0, 0, 0, 0.5);
transition: transform 0.2s;
}
button:hover {
transform: scale(1.05); /* Slight zoom effect on hover */
box-shadow: 2px 2px 15px rgba(255, 62, 62, 0.8);
}
.gradio-container {
border-radius: 15px;
padding: 20px;
background: rgba(0, 0, 0, 0.7); /* Transparent black background for the main container */
box-shadow: 0px 0px 15px #0f3460; /* Glowing effect for the container */
}
"""
# Interface definition
interface = gr.Interface(
fn=predict_sport_class,
inputs=gr.Textbox(lines=2, placeholder="Enter Article here..."),
outputs=gr.Label(num_top_classes=1),
title="Topic Classification App",
description="Classify topics into one of these categories: sport, business, politics, tech, entertainment.",
examples=examples,
css=custom_css, # Apply custom CSS
)
# Launch the interface
interface.launch()
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