first version

app.py

@@ -1,3 +1,24 @@
 import gradio as gr
 
-gr.load("models/liamvbetts/bart-news-summary-v1").launch()
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("liamvbetts/bart-large-cnn-v4")
+model = AutoModelForSeq2SeqLM.from_pretrained("liamvbetts/bart-large-cnn-v4")
+
+def summarize(article):
+    inputs = tokenizer(article, return_tensors="pt").input_ids
+    outputs = model.generate(inputs, max_new_tokens=128, do_sample=False)
+    summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return summary
+
+# Create Gradio interface
+input_text = gr.Textbox(lines=10, label="Input Text")
+output_text = gr.Textbox(label="Summary")
+
+gr.Interface(
+    fn=summarize,
+    inputs=input_text,
+    outputs=output_text,
+    title="News Summary App",
+    description="Enter a news text and get its summary."
+).launch()

requirements.txt

@@ -0,0 +1,6 @@
+gradio
+transformers
+datasets
+evaluate
+accelerate
+torch