Delete app.py

app.py

@@ -1,28 +0,0 @@
-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.Label(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()