Update app.py

app.py

@@ -1,21 +1,22 @@
-import gradio as gr
+import torch
 from fastai.text.all import *
 from blurr.text.data.all import *
-from blurr.text.modeling.all import *
-from transformers import T5Tokenizer, T5ForConditionalGeneration
-from transformers import BartForConditionalGeneration
+from blurr.text.modeling.all import *  # Import only needed functions
+from transformers import T5Tokenizer, T5ForConditionalGeneration  # Use T5 specifically
 
-# Load the pre-trained model and tokenizer
-pretrained_model_name = "facebook/bart-large-cnn"
+# 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)
-learn = load_learner('article_highlights.pkl')
 
 def summarize(article):
-  # Preprocess the input text
-  processed_text = learn.dblock.pipeline(article)
+  # Define your data transformation pipeline here, if applicable
+  # ...
+
+  # Load the exported model
+  learn = load_learner('article_highlights.pkl')
 
   # Generate the summary
-  summary = learn.predict(processed_text)[0]['highlights']
+  summary = learn.predict(article)[0]['highlights']
 
   return summary