Update app.py

app.py

@@ -2,8 +2,6 @@ import streamlit as st
 from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
 import tensorflow as tf
 
-# Load the fine-tuned model
-model_path = "./model"  # Replace with your model path
 tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
 model = TFAutoModelForSequenceClassification.from_pretrained('spectre0108/roberta-finetune-slangs')
 
@@ -13,10 +11,8 @@ def predict_sentiment(text):
     output = model(tokenized)
     logits = output.logits.numpy()
 
-    # Apply softmax to each row (axis=1)
     probabilities = tf.nn.softmax(logits, axis=1).numpy()
 
-    # Get the predicted label (class with the highest probability)
     predicted_label = tf.argmax(probabilities, axis=1).numpy().item()
     positive_prob = probabilities[0][1]
     negative_prob = probabilities[0][0]