app.py

from transformers import AuotoTokenizer,AutoModelForSequenceClassification
import gradio as gr

model_name="nebiyu29/hate_classifier"
tokenizer=AuotoTokenizer.from_pretrained(model_name)
model=AutoModelfForSequenceClassification.from_pretrained(model_name)

#this where the model is active
def model_classifier(text):
    if text is "":
        return f"the input text is {text}"
    else:
        encoded_input=tokenizer(text) #this is where the encoding happens
        scores=model(encoded)  #this is is the score for rach values
        return scores




#lets write something that accepts input as text and returns the most likely out come out of 3
demo=gr.Interface(
    fn=model_classifier,
    inputs=gr.Textbox(lines=5,label="Enter you text"),
    outputs=gr.Textbox(lines=5,label="Label scores"),
    title="Hate Classifier Demo App"
)
demo.launch()