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):
    X_test = str(text).lower()
    encoded_input = tokenizer(X_test, return_tensors='pt')
    output = model(**encoded_input)
    scores = output[0][0].detach()
    scores = torch.nn.functional.softmax(scores)
    return {"Severe Reaction": float(scores.numpy()[1]), "Non-severe Reaction": float(scores.numpy()[0])}

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()