import torch
import gradio as gr

# Warning: Pipeline type "summarization" is no longer supported in transformers v5.
# You must load the model directly or downgrade to v4.x with:
# 'pip install "transformers<5.0.0'
from transformers import pipeline

text_summary = pipeline("summarization", model="Falconsai/text_summarization", dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32)

def summary(input_text):
    if not input_text.strip():
        return "Please enter document text to summarize."

    # Approximate token count using words
    input_tokens = len(input_text.split())

    # Check if word count is less than 5
    if input_tokens < 5:
        return "Insufficient text to summarize."

    # Proceed with summarization
    output = text_summary(input_text, truncation=True)
    summary_text = output[0]['summary_text']

    # Calculate output tokens (approximate using word count)
    output_tokens = len(summary_text.split())

    # Debug print statement
    print(f"Input tokens: {input_tokens}, Output tokens: {output_tokens}")

    return summary_text

gr.close_all()

demo = gr.Interface(fn=summary,
                    inputs=[gr.Textbox(label="Input text to summarize", lines=8, placeholder="Paste document text here..." )],
                    outputs=[gr.Textbox(label="Summarized text", lines=4)],
                    title="AI Text summarization",
                    description="Powered by FalconsAI T5 Sequence-to-Sequence model.",
                    flagging_mode="never")

demo.launch()
# demo.launch(share=True)  # Creates a public link