Update app.py

app.py

@@ -2,9 +2,10 @@ import gradio as gr
 from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
 import torch
 
-# Load the tokenizer and model
-tokenizer = DistilBertTokenizer.from_pretrained("./fine_tuned_distilbert_imdb")
-model = DistilBertForSequenceClassification.from_pretrained("./fine_tuned_distilbert_imdb")
+# ✅ Load the model from Hugging Face Hub
+MODEL_NAME = "Kaiyeee/fine_tuned_distilbert_imdb"  # Update this with your actual repo name
+tokenizer = DistilBertTokenizer.from_pretrained(MODEL_NAME)
+model = DistilBertForSequenceClassification.from_pretrained(MODEL_NAME)
 
 def predict_sentiment(text):
     inputs = tokenizer(text, return_tensors="pt", padding="max_length", truncation=True, max_length=128)
@@ -15,7 +16,7 @@ def predict_sentiment(text):
     sentiment = "positive" if predicted_class_id == 1 else "negative"
     return sentiment
 
-# Create a Gradio interface
+# ✅ Create a Gradio interface
 demo = gr.Interface(
     fn=predict_sentiment,
     inputs=gr.Textbox(lines=5, placeholder="Enter text for sentiment analysis..."),