import gradio as gr
from transformers import pipeline
from helper import extract_text_from_pdf, extract_text_from_image, generate_todo

summarizer = pipeline("summarization", model="facebook/bart-large-cnn")

def process_input(file):
    file_type = file.name.split('.')[-1]

    if file_type.lower() == "pdf":
        text = extract_text_from_pdf(file.name)
    elif file_type.lower() in ["png", "jpg", "jpeg"]:
        text = extract_text_from_image(file.name)
    else:
        text = file.read().decode("utf-8")
    
    if not text.strip():
        return "No content found", ""
    
    summary = summarizer(text, max_length=120, min_length=30, do_sample=False)[0]['summary_text']
    todos = generate_todo(summary)
    
    return summary, todos

iface = gr.Interface(
    fn=process_input,
    inputs=gr.File(type="file", label="Upload text/pdf/image"),
    outputs=[
        gr.Textbox(label="Summary", lines=5),
        gr.Textbox(label="To-Do List", lines=5)
    ],
    title="🧠 Multimodal Productivity Agent",
    description="Upload text, PDFs, or images to generate a summary and actionable to-do list."
)

iface.launch()