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import torch
import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from peft import PeftModel, PeftConfig
# Load PEFT adapter config
adapter_name = "shukdevdatta123/twitter-distilbert-base-uncased-sentiment-analysis-lora-text-classification"
config = PeftConfig.from_pretrained(adapter_name)
# Load base model and tokenizer
base_model = AutoModelForSequenceClassification.from_pretrained(config.base_model_name_or_path)
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
# Load the LoRA adapter into the base model
model = PeftModel.from_pretrained(base_model, adapter_name)
# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model.eval()
# Label mapping
id2label = {
0: "Negative",
1: "Positive"
}
# 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]
probs = torch.nn.functional.softmax(logits, dim=1)
confidence = probs[0][predicted_class].item()
return f"{label} (Confidence: {confidence:.2f})"
# Gradio UI
interface = gr.Interface(
fn=predict_sentiment,
inputs=gr.Textbox(lines=2, placeholder="Enter a sentence to analyze sentiment..."),
outputs="text",
title="Twitter Sentiment Classifier (LoRA + DistilBERT)",
description="This app uses a DistilBERT model with LoRA adapters to classify tweet sentiment as Positive or Negative."
)
interface.launch() |