Update app.py

app.py

@@ -1,24 +1,24 @@
 import torch
 from fastai.text.all import *
-from blurr.text.data.all import *
-from blurr.text.modeling.all import *  # Import only needed functions
-from transformers import T5Tokenizer, T5ForConditionalGeneration  # Use T5 specifically
+from blurr.data.all import *
+from blurr.modeling.all import *
+from transformers import BartForConditionalGeneration
 
 # Load the pre-trained model and tokenizer (adjust for Bart if needed)
 pretrained_model_name = "facebook/bart-large-cnn"  # Or "facebook/bart-base"
-hf_tokenizer = T5Tokenizer.from_pretrained(pretrained_model_name)
+hf_tokenizer = BartTokenizer.from_pretrained(pretrained_model_name)
 
 def summarize(article):
-  # Define your data transformation pipeline here, if applicable
-  # ...
+    # Define your data transformation pipeline here, if applicable
+    # ...
 
-  # Load the exported model
-  learn = load_learner('article_highlights.pkl')
+    # Load the exported model
+    learn = load_learner('article_highlights.pkl')
 
-  # Generate the summary
-  summary = learn.predict(article)[0]['highlights']
+    # Generate the summary
+    summary = learn.blurr_generate(article)[0]
 
-  return summary
+    return summary
 
 # Create the Gradio interface
 iface = gr.Interface(
@@ -31,4 +31,4 @@ iface = gr.Interface(
 )
 
 # Launch the Gradio interface
-iface.launch()
+iface.launch()