import gradio as gr

from datasets import load_dataset
from transformers import AutoTokenizer, DataCollatorWithPadding
from transformers import TrainingArguments
from transformers import AutoModelForSequenceClassification
from transformers import Trainer
from datasets import load_metric
import numpy as np
from transformers import pipeline


model_loader = AutoModelForSequenceClassification.from_pretrained("content/models")
tokenizer_loader = AutoTokenizer.from_pretrained("content/models")
model_loader.eval()
print("loaded")


classifier = pipeline("sentiment-analysis", model=model_loader, tokenizer=tokenizer_loader, device=0)

def greet(twitter):
    pred = classifier(twitter)[0]
    return "twitter is %s with score=%.4f" % (pred['label'], pred['score'])

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