Upload app.py

app.py

@@ -0,0 +1,27 @@
+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()