Create app.py

app.py

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+# Use a pipeline as a high-level helper
+from transformers import pipeline
+import torch
+import gradio as gr
+
+# pipe = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6", torch_dtype=torch.bfloat16)
+
+model_path = "Model/models--sshleifer--distilbart-cnn-12-6/snapshots/a4f8f3ea906ed274767e9906dbaede7531d660ff"
+
+text_summary = pipeline("summarization", model=model_path, torch_dtype=torch.bfloat16)
+
+# text="Why You Can Trust Forbes Advisor Small Business \
+# The Forbes Advisor Small Business team is committed to bringing you unbiased \
+# rankings and information with full editorial independence. We use product data, \
+# strategic methodologies and expert insights to inform all of our content and guide \
+# you in making the best decisions for your business journey.\
+# We reviewed 11 systems to help you find the best blogging platform for your blog or \
+# small business. Our ratings looked at factors that included the platform’s starting \
+# price (including whether it offered a free trial or free version); useful general features,\
+# such as drag-and-drop functionality and search engine optimization (SEO) tools; unique features, \
+# how well the blogging platform fared on third-party review sites and a final review by our experts.\
+# All ratings are determined solely by our editorial team."
+
+# print(text_summary(text)[0])
+
+def summary(input):
+    output = text_summary(input)
+    return output[0]['summary_text']
+
+gr.close_all()
+
+# demo = gr.Interface(fn=summary,inputs="text",outputs='text',title='Text Summarization Gradio Huggingface')
+demo = gr.Interface(fn=summary,
+                    inputs=[gr.Textbox(label="Input text to summarization", lines=6)],
+                    outputs=[gr.Textbox(label="Summarized text", lines=4)],
+                    title='Text Summarization',
+                    description='This application will be used to summarize the text',
+                    concurrency_limit=16)
+
+demo.launch(share=True)