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from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import gradio as gr
import numpy as np

MODEL_NAME = "cardiffnlp/twitter-xlm-roberta-base-sentiment"

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)

labels = ['αƒœαƒ”αƒ’αƒαƒ’αƒ˜αƒ£αƒ αƒ˜', 'αƒœαƒ”αƒ˜αƒ’αƒ αƒαƒšαƒ£αƒ αƒ˜', 'αƒžαƒαƒ–αƒ˜αƒ’αƒ˜αƒ£αƒ αƒ˜']

def classify_sentiment(text):

    inputs = tokenizer(text, return_tensors="pt", truncation=True)

    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        probs = torch.nn.functional.softmax(logits, dim=1).numpy()[0]
    # Get label and confidence
    top_label = labels[np.argmax(probs)]
    confidence = np.max(probs)
    return {labels[i]: float(probs[i]) for i in range(len(labels))}


iface = gr.Interface(
    fn=classify_sentiment,
    inputs=gr.Textbox(lines=3, placeholder="αƒ¨αƒ”αƒ˜αƒ§αƒ•αƒαƒœαƒ”αƒ— αƒ’αƒ•αƒ˜αƒ’αƒ˜ ..."),
    outputs=gr.Label(num_top_classes=3),
    title="Twitter-αƒ˜αƒ‘ αƒ’αƒαƒœαƒ¬αƒ§αƒαƒ‘αƒ˜αƒ‘ αƒ™αƒšαƒαƒ‘αƒ˜αƒ€αƒ˜αƒ™αƒαƒ’αƒαƒ αƒ˜",
    description="αƒ˜αƒ§αƒ”αƒœαƒ”αƒ‘αƒ‘ CardiffNLP-αƒ˜αƒ‘ αƒ›αƒ αƒαƒ•αƒαƒšαƒ”αƒœαƒαƒ•αƒαƒœ RoBERTa αƒ›αƒαƒ“αƒ”αƒšαƒ‘ αƒ’αƒ•αƒ˜αƒ’αƒ”αƒ‘αƒ˜αƒ‘ αƒ“αƒαƒ“αƒ”αƒ‘αƒ˜αƒ—, αƒœαƒ”αƒ˜αƒ’αƒ αƒαƒšαƒ£αƒ  αƒαƒœ αƒ£αƒαƒ αƒ§αƒαƒ€αƒ˜αƒ—αƒαƒ“ αƒ™αƒšαƒαƒ‘αƒ˜αƒ€αƒ˜αƒͺαƒ˜αƒ αƒ”αƒ‘αƒ˜αƒ‘αƒ—αƒ•αƒ˜αƒ‘."
)

iface.launch(share=True)