Update app.py

app.py

@@ -1,73 +1,24 @@
 import torch
 import gradio as gr
 from transformers import pipeline
-import logging
-
-# Set up logging
-logging.basicConfig(level=logging.INFO)
 
 # Use a pipeline as a high-level helper
 device = 0 if torch.cuda.is_available() else -1
-text_summary = pipeline("summarization", model="facebook/bart-large-cnn", device=device, torch_dtype=torch.bfloat16)
-
-# Function for summarization with enhancements
-def summary(input, summary_type="medium"):
-    # Check for empty input
-    if not input.strip():
-        return "Error: Please provide some text to summarize."
-
-    # Calculate input length
-    input_length = len(input.split())
-    logging.info(f"Input length: {input_length} words")
-
-    # Handle input that's too short
-    if input_length < 10:
-        return "Error: Input is too short. Please provide at least 10 words."
+text_summary = pipeline("summarization", model="Falconsai/text_summarization",device=device,torch_dtype=torch.bfloat16)
 
-    # Handle input that's too long for the model
-    if input_length > 512:
-        return "Warning: Input exceeds the model's limit of 512 tokens. Please shorten the input text."
-
-    # Adjust max/min lengths based on the summary type
-    if summary_type == "short":
-        max_output_tokens = max(10, input_length // 4)
-    elif summary_type == "medium":
-        max_output_tokens = max(20, input_length // 2)
-    elif summary_type == "long":
-        max_output_tokens = max(30, (3 * input_length) // 4)
-    min_output_tokens = max(10, input_length // 6)
-
-    # Generate summary
-    output = text_summary(input, max_length=max_output_tokens, min_length=min_output_tokens, truncation=True)
+def summary(input):
+    output = text_summary(input)
     return output[0]['summary_text']
 
-# Function to save the output summary to a file
-def save_summary(summary_text):
-    """Save the summarized text to a file."""
-    with open("summary_output.txt", "w") as file:
-        file.write(summary_text)
-    return "Summary saved to 'summary_output.txt'."
-
-# Gradio interface setup
 gr.close_all()
 
 # Create the Gradio interface
 demo = gr.Interface(
     fn=summary,
-    inputs=[
-        gr.Textbox(label="INPUT THE PASSAGE TO SUMMARIZE", lines=15, placeholder="Paste your text here."),
-        gr.Dropdown(["short", "medium", "long"], label="SUMMARY LENGTH", value="medium")
-    ],
-    outputs=[
-        gr.Textbox(label="SUMMARIZED TEXT", lines=10, placeholder="Your summarized text will appear here."),
-        gr.Button("Save Summary", click=save_summary)
-    ],
+    inputs=[gr.Textbox(label="INPUT THE PASSAGE TO SUMMARIZE", lines=10)],
+    outputs=[gr.Textbox(label="SUMMARIZED TEXT", lines=4)],
     title="PAVISHINI @ GenAI Project 1: Text Summarizer",
-    description=(
-        "This application summarizes input text. "
-        "The output length can be short, medium, or long based on your selection."
-    ),
-    live=True
+    description="This application is used to summarize the text"
 )
 
 demo.launch()