app.py

import gradio as gr
import tensorflow as tf
from transformers import BertTokenizer, BertModel

name = "MiVaCod/mbart-neutralization"
tokenizer = BertTokenizer.from_pretrained(name)
model = BertModel.from_pretrained(name)

# Define a function to make predictions
def predict(texts):
    # Tokenize and preprocess the new text
    new_encodings = tokenizer(texts, truncation=True, padding=True, max_length=70, return_tensors='tf')
    new_predictions = model(new_encodings)
    # Make predictions
    new_predictions = model(new_encodings)
    new_labels_pred = tf.argmax(new_predictions.logits, axis=1)
    new_labels_pred = new_labels_pred.numpy()[0]

    labels_list = ["Negative 😠", "Positive 😍"]
    emotion = labels_list[new_labels_pred]
    return emotion

# Create a Gradio interface
iface = gr.Interface(
    fn=predict,
    inputs="text",
    outputs=gr.outputs.Label(num_top_classes = 6),  # Corrected output type
    examples=[["the rock is destined to be the 21st century's new conan and that he's going to make a splash even greater than arnold schwarzenegger , jean-claud van damme or steven segal."],
              ],
    title="Rotten tomatoes classification",
    description="Predict the class associated with a text."
)
# Launch the interfac
iface.launch()