app.py

import gradio as gr
import tensorflow as tf
from transformers import DistilBertTokenizer, TFDistilBertForSequenceClassification

tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
model = TFDistilBertForSequenceClassification.from_pretrained("Elegbede/Distilbert_FInetuned_For_Text_Classification")
# Define a function to make predictions
def predict(texts):
  # Tokenize and preprocess the new text
  new_encodings = tokenizer(text, truncation=True, padding=True, max_length=70, return_tensors='tf')

  # Make predictions
  new_predictions = model(new_encodings)
  new_labels_pred = tf.argmax(new_predictions.logits, axis=1)

  labels_dict = {0: 'Sadness 😭', 1: 'Joy 😂', 2: 'Love 😍', 3: 'Anger 😠', 4: 'Fear 😨', 5: 'Surprise 😲'}

  # Assuming 'new_labels_pred' contains the predicted class index
  predicted_emotion = labels_dict[new_labels_pred[0].numpy()]
  return predicted_emotion

iface = gr.Interface(
  fn=predict,
  inputs="text",
  outputs="text",
  title="Emotion Classifier",
  description="Predict the emotion from text using a fine-tuned DistilBERT model ",
)
# Launch the interfac
iface.launch()