Delete app.py

app.py

@@ -1,27 +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):
-    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()