Modification of output path

onnxt5/api.py

@@ -15,13 +15,11 @@ def get_encoder_decoder_tokenizer():
 
     # Checks if encoder is already expanded
     if not os.path.exists(path_t5_encoder):
-        download_generation_model(os.path.join(package_path, 'model_data', 't5-encoder.tar.gz'), 't5-encoder.tar.gz',
-                                  package_path)
+        download_generation_model(os.path.join(package_path, 'model_data'), 't5-encoder.tar.gz')
 
     # Checks if decoder is already expanded
     if not os.path.exists(path_t5_decoder):
-        download_generation_model(os.path.join(package_path, 'model_data', 't5-decoder-with-lm-head.tar.gz'),
-                                  't5-decoder-with-lm-head.tar.gz', package_path)
+        download_generation_model(os.path.join(package_path, 'model_data'), 't5-decoder-with-lm-head.tar.gz')
 
     # Loading the model_data
     decoder_sess = InferenceSession(path_t5_decoder)
@@ -44,10 +42,11 @@ def run_embeddings_text(encoder, decoder, tokenizer, prompt):
 
     return encoder_output, decoder_output
 
-def download_generation_model(path, object, output_dir):
+def download_generation_model(output_dir, object):
     url = f'https://t5-onnx-models.s3.amazonaws.com/{object}'
     r = requests.get(url, allow_redirects=True)
-    open(path, 'wb').write(r.content)
-    tar = tarfile.open(path, "r:gz")
+    downloaded_file_path = os.path.join(output_dir, object)
+    open(downloaded_file_path, 'wb').write(r.content)
+    tar = tarfile.open(downloaded_file_path, "r:gz")
     tar.extractall(path=output_dir)
     tar.close()