app.py

import gradio as gr
from transformers import pipeline
import time

# Show loading status
print("Loading model...")
start_time = time.time()

# Initialize model with error handling
try:
    model = pipeline(
        "text2text-generation",
        model="humarin/chatgpt_paraphraser_on_T5_base",
        device="cpu"
    )
    load_time = time.time() - start_time
    print(f"Model loaded successfully in {load_time:.2f} seconds")
except Exception as e:
    print(f"Model loading failed: {str(e)}")
    raise

def paraphrase(text):
    try:
        output = model(
            f"paraphrase: {text}",
            max_length=128,
            num_beams=5,
            temperature=0.7,
        )
        return output[0]["generated_text"]
    except Exception as e:
        print(f"Generation error: {str(e)}")
        return text

# Create interface with modern Gradio API
with gr.Blocks() as demo:
    gr.Markdown("## T5 Paraphraser")
    
    with gr.Row():
        input_text = gr.Textbox(label="Input Text", lines=3)
        output_text = gr.Textbox(label="Paraphrased Text", lines=3)
    
    submit_btn = gr.Button("Paraphrase")
    submit_btn.click(
        fn=paraphrase,
        inputs=input_text,
        outputs=output_text,
        api_name="predict"  # Standard endpoint name
    )
    
    gr.Examples(
        examples=["The quick brown fox jumps over the lazy dog."],
        inputs=input_text
    )

# Launch configuration for current Gradio
demo.launch(
    server_name="0.0.0.0",
    server_port=7860,
    show_api=True
)