Create Text_Summary_app.py

Text_Summary_app.py

@@ -0,0 +1,30 @@
+import gradio as gr
+import os
+from transformers import pipeline, AutoTokenizer
+
+# Initialize summarizer and tokenizer
+summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6", tokenizer="sshleifer/distilbart-cnn-12-6")
+tokenizer = AutoTokenizer.from_pretrained("sshleifer/distilbart-cnn-12-6")
+
+def summarize_text(input_text):
+    """Summarizes the given input text."""
+    max_length = tokenizer.model_max_length
+    inputs = tokenizer(input_text, truncation=True, max_length=max_length, return_tensors="pt")
+    summary_ids = summarizer.model.generate(inputs.input_ids, max_length=50, min_length=10, do_sample=False)
+    summary_text = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    return {"summary": summary_text}
+
+def generate_summary(input):
+    output = summarize_text(input)
+    return output["summary"]  # Return the summary directly
+
+gr.close_all()
+demo = gr.Interface(
+    fn=generate_summary,
+    inputs=[gr.Textbox(label="Text to summarize", lines=6)],
+    outputs=[gr.Textbox(label="Summary", lines=3)],
+    title="Text Summarization",
+    description="Summarize text using the 'shleifer/distilbart-cnn-12-6' language model.",
+)
+
+demo.launch()