import gradio as gr
from transformers import T5Tokenizer, T5ForConditionalGeneration
from urllib.parse import unquote

# Load the T5 model and tokenizer
tokenizer = T5Tokenizer.from_pretrained("t5-small")
model = T5ForConditionalGeneration.from_pretrained("t5-small")

# Function to summarize text
def summarize_text(text):
    input_text = "summarize: " + text
    inputs = tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True)
    summary_ids = model.generate(inputs, max_length=50, num_beams=4, early_stopping=True)
    return tokenizer.decode(summary_ids[0], skip_special_tokens=True)

# Function to get text from URL query parameters
def get_text_from_url(request: gr.Request):
    params = request.query_params
    input_text = params.get("text", "")
    return unquote(input_text)  # Decode URL-encoded text

# Wrap inside a Blocks container
with gr.Blocks() as demo:
    textbox = gr.Textbox(lines=5, placeholder="Enter text to summarize...")
    output = gr.Textbox(label="Summary")
    button = gr.Button("Summarize")

    button.click(summarize_text, inputs=textbox, outputs=output)
    demo.load(get_text_from_url, outputs=textbox)  # Now inside Blocks

demo.launch()