# Import necessary libraries
import blurr
import gradio as gr
from pathlib import Path
from fastai.text.all import *
from blurr.text.data.all import *
from blurr.text.modeling.all import *

# Manually download and prepare SQuAD dataset
from datasets import load_dataset
squad = load_dataset("squad")

# Load the learner without using SQuAD
inf_learn = load_learner(fname=Path("laptop_summarizer_1.pkl"), trust_remote_code=True)

# Define a function to generate summaries using your model
def generate_summary(input_text):
    prediction = inf_learn.blurr_generate(input_text)
    generated_text = prediction[0]['generated_texts']
    return generated_text

# Create an interface for the model
interface = gr.Interface(
    fn=generate_summary,  # The function to generate summaries
    inputs=gr.inputs.Textbox(),  # Input field for text
    outputs=gr.outputs.Textbox(),  # Output field for generated text
    live=True,  # Whether to update results in real-time
    title="Laptop Guru",  # Title of the interface
    description="Enter your requirements & get valuable insight from Guru."  # Description of the interface
)

# Start the Gradio app
interface.launch(inline=True, trust_remote_code=True)