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 = pipline(task:"summarization", model=sshleifer/distilbart-cnn-12-6", torch_dtype=torch.bfloat16)
+
+# Run locally
+# model_path = ("../Models/models--sshleifer--distilbart-cnn-12-6/snapshots/"
+#               "a4f8f3ea906ed274767e9906dbaede7531d660ff")
+# text_summary = pipeline("summarization", model=model_path,
+#                 torch_dtype=torch.bfloat16)
+
+
+text="""
+In probability theory and statistics, Bayes' theorem (alternatively Bayes' 
+law or Bayes' rule), named after Thomas Bayes, describes the probability of 
+an event, based on prior knowledge of conditions that might be related to 
+the event.[1] For example, if the risk of developing health problems is 
+known to increase with age, Bayes' theorem allows the risk to an individual 
+of a known age to be assessed more accurately by conditioning it relative 
+to their age, rather than assuming that the individual is typical of the 
+population as a whole.
+
+One of the many applications of Bayes' theorem is Bayesian inference, 
+a particular approach to statistical inference. When applied, the 
+probabilities involved in the theorem may have different probability 
+interpretations. With Bayesian probability interpretation, the theorem 
+expresses how a degree of belief, expressed as a probability, should 
+rationally change to account for the availability of related evidence. 
+Bayesian inference is fundamental to Bayesian statistics. It has been 
+considered to be "to the theory of probability what Pythagoras's theorem 
+is to geometry."[2]
+
+Based on Bayes law both the prevalence of a disease in a given population 
+and the error rate of an infectious disease test have to be taken into account 
+to evaluate the meaning of a positive test result correctly and avoid the 
+base-rate fallacy.
+"""
+
+# returns a list
+# 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="@KitwanaAkil Project 1: Text Summarizer",
+                    description="This application will be used to summarize text.")
+demo.launch()