| import gradio as gr | |
| import tensorflow as tf | |
| import numpy as np | |
| # Load the pre-trained model | |
| model = tf.keras.models.load_model('sentimentality.h5') | |
| # 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 | |
| def sentiment_analysis(text): | |
| scores = model.predict(text) | |
| del scores["compound"] | |
| return scores | |
| iface = gr.Interface( | |
| fn=sentiment_analysis, | |
| inputs=gr.Textbox(placeholder="Enter a positive or negative sentence here..."), | |
| outputs="label", | |
| interpretation="default", | |
| examples=[["This is wonderful!"]]) | |
| iface.launch() | |