Update app.py

app.py

@@ -4,17 +4,11 @@ import gradio as gr
 import os
 
 def convert_embedding(sd15_embedding):
-    # Temporary file paths
-    input_path = "temp_input.pt"
-    output_path = "temp_output.safetensors"
-    
-    # Save uploaded file to disk to be processed
-    with open(input_path, "wb") as f:
-        f.write(sd15_embedding)
+    output_path = "embedding.safetensors"
 
-    # Your existing conversion logic
-    sd15_embedding = torch.load(input_path, map_location=torch.device('cpu'))
+    sd15_embedding = torch.load(sd15_embedding.name, weights_only=True)
     sd15_tensor = sd15_embedding['string_to_param']['*']
+    
     num_vectors = sd15_tensor.shape[0]
     clip_g_shape = (num_vectors, 1280)
     clip_l_shape = (num_vectors, 768)
@@ -23,18 +17,15 @@ def convert_embedding(sd15_embedding):
     clip_l[:sd15_tensor.shape[0], :sd15_tensor.shape[1]] = sd15_tensor.to(dtype=torch.float16)
     save_file({"clip_g": clip_g, "clip_l": clip_l}, output_path)
 
-    # Remove the temporary input file
-    os.remove(input_path)
-
     # Return the path to the converted file for download
     return output_path
 
 iface = gr.Interface(
     fn=convert_embedding,
-    inputs=gr.File(label="Upload SD1.5 Embedding"),
-    outputs=gr.File(label="Download Converted SDXL Embedding"),
+    inputs=gr.File(label="Upload SD1.5 pt Embedding"),
+    outputs=gr.File(label="Download Converted SDXL safetensors Embedding"),
     title="SD1.5 to SDXL Embedding Converter",
-    description="Upload an SD1.5 embedding file to convert it to SDXL format."
+    description="Upload an SD1.5 embedding file in pt format to convert it to SDXL."
 )
 
 if __name__ == "__main__":