from transformers import BertTokenizer, BertForSequenceClassification
from transformers import pipeline
import gradio as gr

finbert = BertForSequenceClassification.from_pretrained('rpratap2102/The_Misfits', num_labels=3)
tokenizer = BertTokenizer.from_pretrained('rpratap2102/The_Misfits')

nlp = pipeline("sentiment-analysis", model=finbert, tokenizer=tokenizer)

c_labels = {
    'Negative': 'This does not look good for the Market',
    'Positive': 'This seems to be good news for the market',
    'Neutral': "This is normal in the market"
}

def predict_sentiment(text):
    result = nlp([text])[0]
    sentiment_label = result['label']
    return c_labels[sentiment_label]



iface = gr.Interface(
    fn=predict_sentiment,
    inputs="text",
    outputs="text",
)

iface.launch()