app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-small")
model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-small")

# Note generator function
def generate_notes(prompt):
    # Add task prefix for FLAN-T5
    input_text = f"Summarize or make concise notes: {prompt}"
    inputs = tokenizer(input_text, return_tensors="pt", truncation=True)
    outputs = model.generate(**inputs, max_new_tokens=200)
    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return result

# Gradio interface
iface = gr.Interface(
    fn=generate_notes,
    inputs=gr.Textbox(lines=5, placeholder="Enter your topic or text here..."),
    outputs="text",
    title="🧠 Free Note Generator (FLAN-T5-small)",
    description="Generate summarized notes using Google's FLAN-T5-small model — runs 100% free on CPU in Hugging Face Spaces."
)

iface.launch()