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import gradio as gr
import tensorflow as tf
# 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
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')
# 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])}
# Create a Gradio interface
iface = gr.Interface(
fn=predict_sentiment,
inputs=gr.inputs.Textbox(label="Enter text here", lines=5, placeholder="Type here to analyze sentiment..."),
outputs=gr.outputs.Label(label="Sentiment", default="Neutral", font_size=30)
)
# Add the possible classes to the output plot
classes = ["Positive", "Negative", "Neutral"]
iface.outputs[0].choices = classes
# Launch the interface
iface.launch()
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