app.py

import gradio as gr
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

# Load model and tokenizer
model_name = "Kyudan/distilbert-base-uncased-finetuned-cola"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

def classify_text(text):
    # Tokenize the input text
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)

    # Perform inference
    with torch.no_grad():
        outputs = model(**inputs)

    # Get the predicted class and its probability
    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_class = torch.argmax(probabilities, dim=-1).item()
    confidence = probabilities[0][predicted_class].item()

    # Map the predicted class to a label (assuming binary classification)
    label = "Positive" if predicted_class == 1 else "Negative"

    return f"Classification: {label}\nConfidence: {confidence:.2f}"

# Gradio interface setup
demo = gr.Interface(
    fn=classify_text,
    inputs="text",
    outputs="text",
    title="Text Classification Demo",
    description="Enter a sentence to classify its sentiment (positive/negative)."
)

if __name__ == "__main__":
    demo.launch(share=True)