Update app.py

app.py

@@ -1,22 +1,27 @@
 import gradio as gr
 from transformers import T5Tokenizer, T5ForConditionalGeneration
+import torch
 
-# Load tokenizer and model (small T5 variant, CPU only)
 model_name = "t5-small"
 
 tokenizer = T5Tokenizer.from_pretrained(model_name)
 model = T5ForConditionalGeneration.from_pretrained(model_name)
+model.eval()  # set model to evaluation mode
+
+device = torch.device("cpu")  # explicitly set device to CPU
+model.to(device)
 
 def generate_text(input_text):
-    # Prepare input tokens
-    input_ids = tokenizer.encode(input_text, return_tensors="pt")
-    # Generate output tokens (max length 100)
-    outputs = model.generate(input_ids, max_length=100, num_beams=5, early_stopping=True)
-    # Decode to text
+    input_ids = tokenizer.encode(input_text, return_tensors="pt").to(device)
+    outputs = model.generate(
+        input_ids,
+        max_length=100,
+        num_beams=5,
+        early_stopping=True
+    )
     result = tokenizer.decode(outputs[0], skip_special_tokens=True)
     return result
 
-# Build Gradio interface
 demo = gr.Interface(
     fn=generate_text,
     inputs=gr.Textbox(lines=5, label="Input Text"),