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# app.py
import gradio as gr
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
model_name = "distilbert-base-uncased"
tokenizer = DistilBertTokenizer.from_pretrained(model_name)
model = DistilBertForSequenceClassification.from_pretrained(model_name)
def predict_sentiment(text):
# Tokenize and predict
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
predicted_class = logits.argmax().item()
# Return the predicted class
return {"positive": logits[0][1].item(), "negative": logits[0][0].item()}
iface = gr.Interface(
fn=predict_sentiment,
inputs=gr.Textbox(),
outputs="label",
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