import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
import numpy as np
from scipy.special import softmax
import gradio as gr
torch.cuda.is_available()

model_path = "ltg/norbert3-base_sentence-sentiment"

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForSequenceClassification.from_pretrained(model_path, trust_remote_code=True)

def sentiment_analysis(text):
    encoded_input = tokenizer(text, return_tensors='pt')
    output = model(**encoded_input)
    scores_ = output[0][0].detach().numpy()
    scores_ = softmax(scores_)
    labels = ['Negativ', 'Positiv', 'Nøytral']
    scores = {l: float(s) for (l, s) in zip(labels, scores_)}
    return scores

demo = gr.Interface(
    theme=gr.themes.Base(),
    fn=sentiment_analysis,
    inputs=gr.Textbox(placeholder="Write your text here..."),
    outputs="label",
    examples=[
        ["Woho, jeg fikk meg ny jobb!"],
        ["Jeg skal jobbe med løver i den nye jobben min."],
        ["Oj, en løve spiste den ene armen min.. Snakk om HMS :("],
        ["På vei til sykehus.. Ønsk meg lykke til.."],
        ["Supert! De må pokkern meg amputere hele armen.."],
        ["Våkna opp fra operasjon, fått en robot arm. Im now terminator! Super opplevelse 10 av 10.."]
    ],
    title='Sentiment Analysis App',
    description='This app classifies a positive, neutral, or negative sentiment.'
)
demo.launch()