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import torch
import gradio as gr
from transformers import AutoModelForSequenceClassification, AutoTokenizer
# Load model and tokenizer from Hugging Face Hub
model_name = "shukdevdatta123/twitter-distilbert-base-uncased-sentiment-analysis-lora-text-classification"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the label mapping (binary sentiment: 0 = Negative, 1 = Positive)
id2label = {
0: "Negative",
1: "Positive"
}
# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
# Define the prediction function
def predict_sentiment(text):
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(device)
with torch.no_grad():
logits = model(**inputs).logits
predicted_class = torch.argmax(logits, dim=1).item()
label = id2label[predicted_class]
# Optional: add confidence score
probs = torch.nn.functional.softmax(logits, dim=1)
confidence = probs[0][predicted_class].item()
return f"{label} (Confidence: {confidence:.2f})"
# Create Gradio Interface
interface = gr.Interface(
fn=predict_sentiment,
inputs=gr.Textbox(lines=2, placeholder="Enter a sentence to analyze sentiment..."),
outputs="text",
title="Twitter Sentiment Classifier",
description="This app uses a fine-tuned DistilBERT model with LoRA adapters to predict whether a tweet or sentence is Positive or Negative."
)
# Launch the app
interface.launch() |