Update app.py

app.py

@@ -1,27 +1,33 @@
 import gradio as gr
 from fastai.text.all import *
-from blurr.text.modeling.all import load
+from blurr.text.data.all import *
+from blurr.text.modeling.all import *
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+from transformers import BartForConditionalGeneration
 
-# Load the exported model
-learn = load_learner('article_highlights.pkl')
+# Load the pre-trained model and tokenizer
+pretrained_model_name = "facebook/bart-large-cnn"
+hf_tokenizer = T5Tokenizer.from_pretrained(pretrained_model_name)
+learn = load_learner('article_highlights_part3.pkl')
 
-def generate_summary(article_text):
-    # Perform inference using the loaded model
-    summary = learn.blurr_generate_summary(article_text)
-    return summary
+def summarize(article):
+  # Preprocess the input text
+  processed_text = learn.dblock.pipeline(article)
 
+  # Generate the summary
+  summary = learn.predict(processed_text)[0]['highlights']
+
+  return summary
+
+# Create the Gradio interface
 iface = gr.Interface(
-    fn=generate_summary,
+    fn=summarize,
     inputs="text",
     outputs="text",
-    title="Article Summarizer",
+    title="Article Summarizer (Part 3)",
     description="Enter an article and get a summary.",
-    examples=[
-        ["Text of an article goes here..."]
-    ]
+    examples=[["This is an example article..."]]
 )
 
-# Update: Use load from the evaluate library
-squad_metric = load("squad")
-
+# Launch the Gradio interface
 iface.launch()