Create app.py

app.py

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+import torch
+import gradio as gr
+from transformers import pipeline
+model_path=(".venv/models--sshleifer--distilbart-cnn-12-6/snapshots/a4f8f3ea906ed274767e9906dbaede7531d660ff")
+
+pipe=pipeline("summarization", model="sshleifer/distilbart-cnn-12-6", torch_dtype=torch.bfloat16, device="cpu")
+#pipe=pipeline("summarization", model="facebook/bart-large-cnn", torch_dtype=torch.bfloat16, device="cpu")
+
+#pipe = pipeline("summarization", model="sshleifer/distilbart-cnn-6-6")
+text_summary=pipeline(task="summarization", model=model_path, torch_dtype=torch.bfloat16)
+
+
+def summary(input):
+    #max_input_len=1024
+    #if len(full_text.split()) > max_input_len:
+    #    full_text = " ".join(full_text.split()[:max_input_len])
+    output=text_summary(input)
+    return output[0]['summary_text']
+
+gr.close_all()
+
+demo=gr.Interface(fn=summary,
+                  inputs=[gr.Textbox(label='Input Text to Summarize', lines=1)],
+                  outputs=[gr.Textbox(label='Summarized Text', lines=4)],
+                  title='KS Text Summarizer',
+                  description='This application will be used to summarize a corpus')
+
+demo.launch()