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app.py

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+#!pip install -q torch transformers gradio
+
+import os
+import io
+from IPython.display import Image, display, HTML
+from PIL import Image
+from transformers import pipeline
+
+get_completion = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
+
+def summarize(input):
+    output = get_completion(input)
+    return output[0]['summary_text']
+
+import gradio as gr
+
+
+gr.close_all()
+demo = gr.Interface(fn=summarize,
+                    inputs=[gr.Textbox(label="Text to summarize", lines=6)],
+                    outputs=[gr.Textbox(label="Result", lines=3)],
+                    title="Text summarization with distilbart-cnn",
+                    description="Summarize any text using the `sshleifer/distilbart-cnn-12-6` model under the hood!"
+                   )
+demo.launch(share=True,debug=True)
+

requirements.txt

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+torch
+transformers
+gradio