# Import necessary libraries
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 *
import pathlib

# Load the learner
inf_learn = load_learner(fname=Path("laptop_summarizer_1.pkl"))

# 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
iface = 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 of the interface
    description="Enter your requirements & get valuable insight from Guru."
)

# Start the Gradio app
iface.launch(inline=False)