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("summarization", model="sshleifer/distilbart-cnn-12-3", torch_dtype= torch.bfloat16)
+
+# model_path = "../Models/models--sshleifer--distilbart-cnn-12-6/snapshots/a4f8f3ea906ed274767e9906dbaede7531d660ff"
+# text_summary = pipeline("summarization", model=model_path, torch_dtype= torch.bfloat16)
+
+# text = "A computer is a machine that can be programmed to automatically carry out sequences of arithmetic " \
+# "or logical operations (computation). Modern digital electronic computers can perform generic sets of " \
+# "operations known as programs, which enable computers to perform a wide range of tasks. The term computer " \
+# "system may refer to a nominally complete computer that includes the hardware, operating system, software, " \
+# "and peripheral equipment needed and used for full operation; or to a group of computers that are linked and " \
+# "function together, such as a computer network or computer cluster."
+# 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="Project 1. Text Summarizer",
+                    description="This application will be used to summarize the text using Hugging-Face pre-trained model")
+demo.launch()