app.py

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()