File size: 1,649 Bytes
9724286 fe90914 5328dbc fe90914 52fe9f8 fe90914 52fe9f8 fe90914 6ed2700 5328dbc 6ed2700 fe90914 6ed2700 fe90914 6ed2700 9a811e6 fe90914 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 |
import gradio as gr
import tensorflow as tf
import numpy as np
# Define a function to preprocess the text input
def preprocess(text):
tokenizer = tf.keras.preprocessing.text.Tokenizer()
tokenizer.fit_on_texts([text])
text = tokenizer.texts_to_sequences([text])
text = tf.keras.preprocessing.sequence.pad_sequences(text, maxlen=500, padding='post', truncating='post')
return text
# Load the pre-trained model
model = tf.keras.models.load_model('sentimentality.h5')
# Define a function to make a prediction on the input text
def predict_sentiment(text):
# Preprocess the text
text = preprocess(text)
# Make a prediction using the loaded model
proba = model.predict(text)[0]
# Normalize the probabilities
proba /= proba.sum()
# Return the probability distribution
return {'Positive': float(proba[0]), 'Negative': float(proba[1]), 'Neutral': float(proba[2])}
# Define the possible classes
classes = ['Positive', 'Negative', 'Neutral']
# Define the Gradio interface
iface = gr.Interface(
fn=predict_sentiment,
inputs=gr.inputs.Textbox(label='Enter text here'),
outputs=gr.outputs.Bar(label='Sentiment Distribution',
colors=['#2a9d8f', '#e76f51', '#264653'],
value=[0.3, 0.3, 0.3],
min=0,
max=1),
title='SENTIMENT ANALYSIS'
)
# Set the class labels for the output
iface.outputs[0].type = 'bar'
iface.outputs[0].label = 'Sentiment Distribution'
iface.outputs[0].determine_labels_from_data = False
iface.outputs[0].labels = classes
# Launch the interface
iface.launch()
|