Update app.py

app.py

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+import torch
+import gradio as gr
+from accelerate import Accelerator
+from transformers import pipeline
+
+# Initialize the accelerator
+accelerator = Accelerator()
+
+# Print a description of the current configuration
+print("Accelerator State:", accelerator.state)
+
+# Define the path to your custom model
+model_path = (
+    "../Models/models--sshleifer--distilbart-cnn-12-6/snapshots"
+    "/a4f8f3ea906ed274767e9906dbaede7531d660ff"
+)
+
+# Initialize the text summarization pipeline
+try:
+    text_summary = pipeline(
+        "summarization",
+        model=model_path,
+        torch_dtype=torch.bfloat16,  # Use bfloat16 for better performance on supported hardware
+        device=0 if torch.cuda.is_available() else -1  # Use GPU if available
+    )
+except Exception as e:
+    print(f"Error initializing the summarization pipeline: {e}")
+    raise
+
+# Define the Gradio interface function
+def summary(input_text):
+    try:
+        output = text_summary(input_text)
+        return output[0]['summary_text']
+    except Exception as e:
+        return f"An error occurred while summarizing: {e}"
+
+# Close any existing Gradio interfaces
+gr.close_all()
+
+# Define the Gradio interface
+demo = gr.Interface(
+    fn=summary,
+    inputs=[gr.Textbox(label="Input text to summarize", lines=6)],
+    outputs=[gr.Textbox(label="Summarized text", lines=4)],
+    title="GenAIProject01: Text Summarizer",
+    description="THIS APPLICATION SUMMARIZE INPUT TEXT USING A PRE-TRAINED MODEL."
+)
+
+# Launch the Gradio app
+demo.launch()