add vdf lib

app.py

@@ -1,14 +1,52 @@
 import gradio as gr
 import spaces
 import torch
+import vdf_io
 
 zero = torch.Tensor([0]).cuda()
-print(zero.device) # <-- 'cpu' 🤔
+print(zero.device)  # <-- 'cpu' 🤔
+
+print(vdf_io.__version__)
+
 
 @spaces.GPU
 def greet(n):
-    print(zero.device) # <-- 'cuda:0' 🤗
+    print(zero.device)  # <-- 'cuda:0' 🤗
     return f"Hello {zero + n} Tensor"
 
-demo = gr.Interface(fn=greet, inputs=gr.Number(), outputs=gr.Text())
+
+def reembed_dataset():
+    import datasets
+    # model 
+    # embeddings = model.embed(ds)
+    # new_embeddings = model.reembed(embeddings)
+
+    # datasets.save_dataset(new_embeddings)
+
+
+def reembed_main():
+    download_dataset()
+    reembed_dataset()
+
+
+def download_dataset():
+    import datasets
+
+    # ds = datasets.load_dataset()
+
+
+demo = gr.Interface(
+    fn=reembed_main,
+    inputs=[
+        # dataset name
+        gr.inputs.Textbox(label="Dataset name"),
+        # embedding model
+        gr.inputs.Textbox(label="Embedding model"),
+        # output username
+        gr.inputs.Textbox(label="Output username"),
+    ],
+    outputs=gr.outputs.Textbox(label="Output"),
+    title="Re-Embedder",
+    description="Re-embed a dataset using a given model and output to a new username's account",
+)
 demo.launch()