Create app.py

app.py

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+import torch
+import gradio as gr
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+text_summary = pipeline(
+    task="summarization",
+    model="sshleifer/distilbart-cnn-12-6",
+    torch_dtype=torch.bfloat16
+)
+
+# Local model path
+# model_path = ("../Models/models--sshleifer--distilbart-xsum-12-6/snapshots/5b2e376c845c201ddc34ec0e55fd1ad9890ba5ee")
+#
+# # Summarization pipeline
+# text_summary = pipeline(
+#     "summarization",
+#     model=model_path,
+#     torch_dtype=torch.bfloat16
+# )
+
+# Sample text
+# text = '''Elon Reeve Musk FRS (/ˈiːlɒn/ EE-lon; born June 28, 1971) is a businessman,
+# known for his leadership of Tesla, SpaceX, X (formerly Twitter), and the Department
+# of Government Efficiency (DOGE). Musk has been the wealthiest person in the world
+# since 2021; as of May 2025, Forbes estimates his net worth to be US$424.7 billion.'''
+#
+# # Run summarization
+# print(text_summary(text))
+def summary(input):
+    output =text_summary(input)
+    return output[0]['summary_text']
+gr.close_all()
+# demo = gr.Interface(fn=summary,inputs="text",outputs="text")
+demo=gr.Interface(fn=summary,inputs=[gr.Textbox(label="Input text to summarize",lines=6)],
+                 outputs=[gr.Textbox(label="Summarized text",lines=4)],
+                  title="@SahibhimGenAI Project 1: Text Summarizer",
+                  description= "THIS APPLICATION WILL BE USED TO SUMMARIZE A TEXT TO SUMMARY")
+demo.launch()
+