from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, GenerationConfig
import gradio as gr

# Load model
tokenizer = AutoTokenizer.from_pretrained('AyushSoni14/text-summarizer-model')
model = AutoModelForSeq2SeqLM.from_pretrained('AyushSoni14/text-summarizer-model')
tokenizer.model_max_length = 1024

# Config
gen_config = GenerationConfig(
    max_length=150,
    min_length=40,
    length_penalty=2.0,
    num_beams=4,
    early_stopping=True
)

# Summarization function
def summarize(blog_post):
    input = tokenizer(blog_post, max_length=1024, truncation=True, return_tensors='pt')
    summary_ids = model.generate(input['input_ids'], generation_config=gen_config)
    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
    return summary

# Gradio UI
iface = gr.Interface(
    fn=summarize,
    inputs=gr.Textbox(lines=15, label="Enter Text to Summarize"),
    outputs=gr.Textbox(label="Summary"),
    title="Text Summarizer",
    description="Enter a long paragraph or blog post to get a summarized version."
)

iface.launch(share=True)