Update app.py

app.py

@@ -44,7 +44,14 @@ class model_onxx:
     def __init__(self):
         self.model=None
         self.n_onxx=""
+        self.storage_dir = "uploads"  
         pass
+     
+
+ 
+
+
+   
     def function_change(self,n_model,token,n_onxx,choice):
         if choice=="decoder":
 
@@ -66,12 +73,15 @@ class model_onxx:
     def convert_to_onnx_only_decoder(self,n_model,token,namemodelonxx):
           model=VitsModel.from_pretrained(n_model,token=token)
           x=f"{namemodelonxx}.onnx"
+          if not os.path.exists("uploads"):
+             os.makedirs(storage_dir)
+          file_path = os.path.join("uploads",x)
           vocab_size = model.text_encoder.embed_tokens.weight.size(0)
           example_input = torch.randint(0, vocab_size, (1, 100), dtype=torch.long)
           torch.onnx.export(
               model,  # The model to be exported
               example_input,  # Example input for the model
-              x,  # The filename for the exported ONNX model
+              file_path,  # The filename for the exported ONNX model
               opset_version=11,  # Use an appropriate ONNX opset version
               input_names=['input'],  # Name of the input layer
               output_names=['output'],  # Name of the output layer
@@ -80,7 +90,7 @@ class model_onxx:
                   'output': {0: 'batch_size'}
               }
            )
-          return x
+          return file_path
     def convert_to_onnx_all(self,n_model,token ,namemodelonxx):
 
           model=VitsModel.from_pretrained(n_model,token=token)