Update app.py

app.py

@@ -1,33 +1,64 @@
 import gradio as gr
-from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
 
-# Load summarization pipeline
-summarizer = pipeline(
-    "summarization",
-    model="facebook/bart-large-cnn"
-)
+MODEL_NAME = "facebook/bart-large-cnn"
 
-def summarize_text(text):
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = model.to(device)
+
+
+def summarize(text, max_length, min_length):
     if not text.strip():
         return "Please enter some text to summarize."
-    
-    summary = summarizer(
+
+    inputs = tokenizer(
         text,
-        max_length=150,
-        min_length=40,
-        do_sample=False
+        return_tensors="pt",
+        max_length=1024,
+        truncation=True
+    ).to(device)
+
+    summary_ids = model.generate(
+        inputs["input_ids"],
+        num_beams=4,
+        max_length=max_length,
+        min_length=min_length,
+        early_stopping=True
     )
-    
-    return summary[0]['summary_text']
-
-# Gradio Interface
-demo = gr.Interface(
-    fn=summarize_text,
-    inputs=gr.Textbox(lines=15, placeholder="Paste your text here..."),
-    outputs="text",
-    title="Advanced BART Text Summarizer",
-    description="Summarize long text using facebook/bart-large-cnn"
-)
-
-if __name__ == "__main__":
-    demo.launch()
+
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    return summary
+
+
+with gr.Blocks(title="Advanced BART Summarizer") as demo:
+    gr.Markdown("# Advanced BART Text Summarizer")
+    gr.Markdown("Summarization using facebook/bart-large-cnn")
+
+    input_text = gr.Textbox(
+        lines=12,
+        placeholder="Enter long article or paragraph here..."
+    )
+
+    with gr.Row():
+        max_len = gr.Slider(50, 300, value=150, label="Max Summary Length")
+        min_len = gr.Slider(20, 100, value=40, label="Min Summary Length")
+
+    output_text = gr.Textbox(
+        lines=8,
+        label="Generated Summary"
+    )
+
+    summarize_btn = gr.Button("Summarize")
+
+    summarize_btn.click(
+        summarize,
+        inputs=[input_text, max_len, min_len],
+        outputs=output_text
+    )
+
+demo.launch()