app.py

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

# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("MarkAdamsMSBA24/ADRv2024")
model = AutoModelForSequenceClassification.from_pretrained("MarkAdamsMSBA24/ADRv2024")

# Define the prediction function
def get_prediction(text):
    inputs = tokenizer(text, return_tensors="pt", max_length=512, truncation=True, padding=True)
    with torch.no_grad():
        outputs = model(**inputs)
    prediction_scores = outputs.logits
    predicted_class = torch.argmax(prediction_scores, dim=-1).item()
    return f"Predicted Class: {predicted_class}", prediction_scores.tolist()

iface = gr.Interface(
    fn=get_prediction,
    inputs=gr.Textbox(lines=4, placeholder="Type your text..."),
    outputs=[gr.Textbox(label="Prediction"), gr.Dataframe(label="Scores")],
    title="BERT Sequence Classification Demo",
    description="This demo uses a BERT model hosted on Hugging Face to classify text sequences."
)

if __name__ == "__main__":
    iface.launch()