app.py

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()