Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModel, AutoTokenizer
+import torch
+
+# Load the model and tokenizer from Hugging Face Hub
+model = AutoModel.from_pretrained("comethrusws/finlytic-compliance")
+tokenizer = AutoTokenizer.from_pretrained("comethrusws/finlytic-compliance")
+
+# Define a function to handle inference
+def predict(input_data):
+    inputs = tokenizer(input_data, return_tensors="pt")
+    outputs = model(**inputs)
+    
+    # Assuming the model returns logits (modify this depending on your model's architecture)
+    prediction = torch.argmax(outputs.logits, dim=-1).item()
+    return prediction
+
+# Create a Gradio interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.inputs.Textbox(label="Input Data"),
+    outputs=gr.outputs.Textbox(label="Prediction"),
+    title="Fintlytic Compliance Model",
+    description="Predict using the Finlytic compliance model",
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    interface.launch()