Update app.py

app.py

@@ -3,14 +3,14 @@ import re
 import gradio as gr
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
 import torch
-import torch
-torch.device("cpu")
 import torch.nn.functional as F
 from transformers import pipeline
 
 # Load the pre-trained model and tokenizer
 tokenizer = AutoTokenizer.from_pretrained("Ahmed235/roberta_classification")
 model = AutoModelForSequenceClassification.from_pretrained("Ahmed235/roberta_classification")
+device = torch.device("cpu")
+model = model.to(device)  # Move the model to the CPU
 
 # Create a summarization pipeline
 summarizer = pipeline("summarization", model="Falconsai/text_summarization")
@@ -30,6 +30,7 @@ def predict_pptx_content(file_path):
 
     # Tokenize and encode the cleaned text
     input_encoding = tokenizer(cleaned_text, truncation=True, padding=True, return_tensors="pt")
+    input_encoding = {key: val.to(device) for key, val in input_encoding.items()}  # Move input tensor to CPU
 
     # Perform inference
     with torch.no_grad():