app.py

import gradio as gr
import torch
from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM

# Load model and tokenizer explicitly
model_name = "sazzadul/bangla-med-sum"
tokenizer = AutoTokenizer.from_pretrained(model_name, src_lang="bn_IN")
model = AutoModelForSeq2SeqLM.from_pretrained(
    model_name,
    device_map="auto"
    # low_cpu_mem_usage=True
)

summarizer = pipeline(
    "summarization",
    model=model,
    tokenizer=tokenizer
)

def summarize_text(text):
    try:
        if not text.strip():
            return "আপনার রোগ অথবা সমস্যা সম্পর্কে বিস্তারিত বলুন : "
            
        summary = summarizer(
            text,
            max_length=256,
            min_length=30,
            truncation=True,
            # Directly pass forced_bos_token_id here
            forced_bos_token_id=tokenizer.lang_code_to_id["bn_IN"]
        )[0]['summary_text']
        return summary
    except Exception as e:
        return f"Error during summarization: {str(e)}"

iface = gr.Interface(
    fn=summarize_text,
    inputs=gr.Textbox(lines=5, label="রোগীর সমস্যার বিবরণ (বাংলায়)"),
    outputs=gr.Textbox(lines=5, label="সারাংশ"),
    title="Bangla Medical Problem Summary Generation",
    description="Provide a detailed description of the patient or treatment-related question or problem and get a concise and clear summary",
    allow_flagging="never"
)

if __name__ == "__main__":
    iface.launch(server_name="0.0.0.0", server_port=7860)