File size: 1,261 Bytes
2e71df7 2f9f9d9 2e71df7 52fe9f8 2f9f9d9 2e71df7 2f9f9d9 2e71df7 2f9f9d9 73e1dac 2f9f9d9 2e71df7 2f9f9d9 2e71df7 2f9f9d9 |
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 |
import tensorflow as tf
import gradio as gr
import numpy as np
# Load the custom model
model = tf.keras.models.load_model("sentiment.h5")
# Define a function to preprocess the input text
def preprocess_text(text):
# Tokenize the text
tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=10000)
tokenizer.fit_on_texts(text)
text = tokenizer.texts_to_sequences(text)
# Pad the sequences to a fixed length
max_len = 30
text = tf.keras.preprocessing.sequence.pad_sequences(text, maxlen=max_len)
return text
# Define a function to get the sentiment scores from the model
def get_sentiment_scores(text):
# Preprocess the text
text = preprocess_text(text)
# Get the sentiment scores from the model
scores = model.predict(text)
# Postprocess the scores to obtain the sentiment label
label = "Positive" if np.round(scores) == 1 else "Negative"
return label
# Define the Gradio interface
interface = gr.Interface(
fn=get_sentiment_scores,
inputs=gr.inputs.Textbox(placeholder="Enter a positive or negative sentence here..."),
outputs=gr.outputs.Textbox(label="Sentiment Label"),
examples=[["This is wonderful!"], ["I hate this product."]]
)
# Launch the interface
interface.launch()
|