app.py

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")

import gradio as gr

def greet(my_text):
    with torch.no_grad():
        tokens = tokenizer(my_text, padding=True, truncation=True, return_tensors="pt")
        outputs = model(**tokens)
        logits = outputs.logits
        probabilities = torch.softmax(logits, dim=1)
        label_ids = torch.argmax(probabilities, dim=1)
        labels = ['Negative', 'Positive']
        label = labels[label_ids]
    return label  


demo = gr.Interface(fn=greet, inputs="text", outputs="text", title="Sentiment Analysis",description ="Classify a text into either Positive or negative",
article = "hey my nam is pranjal khadka")

#demo.launch(share = True)
demo.launch()