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

model_name="nebiyu29/hate_classifier"
tokenizer=AutoTokenizer.from_pretrained(model_name)
model=AutoModelForSequenceClassification.from_pretrained(model_name)

#this where the model is active and we need to make the gradiends in active

def model_classifier(text):
    model.eval()
    with torch.no_grad():
        if len(text)==0:
            return f"the input text is {text}"
        else:
            encoded_input=tokenizer(text) #this is where the encoding happens
            scores=model(encoded)[0]  #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(share=True)