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"""GUI using Gradio."""
from __future__ import annotations
import os
from functools import lru_cache
from typing import TYPE_CHECKING
import gradio as gr
import joblib
from app.model import infer_model
if TYPE_CHECKING:
from sklearn.base import BaseEstimator
__all__ = ["launch_gui"]
POSITIVE_LABEL = "Positive 😊"
NEUTRAL_LABEL = "Neutral 😐"
NEGATIVE_LABEL = "Negative 😤"
@lru_cache(maxsize=1)
def load_model() -> BaseEstimator:
"""Load the trained model and cache it.
Returns:
Loaded model
"""
model_path = os.environ.get("MODEL_PATH", None)
if model_path is None:
msg = "MODEL_PATH environment variable not set"
raise ValueError(msg)
return joblib.load(model_path)
def sentiment_analysis(text: str) -> str:
"""Perform sentiment analysis on the provided text.
Args:
text: Input text
Returns:
Predicted sentiment label
"""
prediction = infer_model(load_model(), [text])[0]
if prediction == 0:
return NEGATIVE_LABEL
if prediction == 1:
return POSITIVE_LABEL
return NEUTRAL_LABEL
demo = gr.Interface(
fn=sentiment_analysis,
inputs=gr.Textbox(lines=10, label="Enter text here"),
outputs="label",
title="Sentiment Analysis",
description="Predict the sentiment of a given text.",
examples=[
["I love the weather today!"],
["You are a terrible person."],
["The movie we watched was boring."],
["This website is amazing!"],
],
allow_flagging=False,
)
def launch_gui(share: bool) -> None:
"""Launch the Gradio GUI.
Args:
share: Whether to create a public link
"""
demo.launch(share=share)
if __name__ == "__main__":
demo.launch()
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