Update app.py

app.py

@@ -45,7 +45,10 @@ class model_onxx:
  
 
 
-   
+    def download_file(self,file_path):
+        ff= gr.File(value=file_path, visible=True) 
+        file_url = ff.value['url']  
+        return file_url
     def function_change(self,n_model,token,n_onxx,choice):
         if choice=="decoder":
 
@@ -67,15 +70,13 @@ 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
-              file_path,  # The filename for the exported ONNX model
+              x,  # 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
@@ -84,7 +85,7 @@ class model_onxx:
                   'output': {0: 'batch_size'}
               }
            )
-          return file_path
+          return self.download_file(x)
     def convert_to_onnx_all(self,n_model,token ,namemodelonxx):
 
           model=VitsModel.from_pretrained(n_model,token=token)
@@ -104,7 +105,7 @@ class model_onxx:
                   'output': {0: 'batch_size'}
               }
            )
-          return x
+           return self.download_file(x)
     def starrt(self):
         #with gr.Blocks() as demo:
             with gr.Row():