add server.

autocomplete/gpt2.py

@@ -20,14 +20,14 @@ def predict_with_original_gpt2(prompts):
     for prompt in prompts:
         # Generate text using the model. Verbose set to False to prevent logging generated sequences.
         generated = model.generate(prompt, verbose=False)
-
         generated = generated[0]
-        print("=============================================================================")
         print(generated)
-        print("=============================================================================")
+        print("=" * 20)
 
 
-def train(model_dir="outputs/fine-tuned/", train_file="download/train.txt", valid_file="download/valid.txt",
+def train(model_dir="outputs/fine-tuned/",
+          train_file="download/train.txt",
+          valid_file="download/valid.txt",
           num_train_epochs=3):
     train_args = {
         "reprocess_input_data": True,

autocomplete/server.py

@@ -2,6 +2,7 @@
 @author:XuMing([email protected])
 @description: Server
 """
+import argparse
 import uvicorn
 import sys
 import os
@@ -16,8 +17,11 @@
 pwd_path = os.path.abspath(os.path.dirname(__file__))
 use_cuda = torch.cuda.is_available()
 # Use finetuned GPT2 model
-model_dir = os.path.join(pwd_path, "outputs/fine-tuned/")
-gpt2_infer = Infer(model_name="gpt2", model_dir=model_dir, use_cuda=use_cuda)
+parser = argparse.ArgumentParser()
+parser.add_argument("--model_name_or_path", type=str, default="shibing624/code-autocomplete-gpt2-base",
+                    help="Model save dir or model name")
+args = parser.parse_args()
+gpt2_infer = Infer(model_name="gpt2", model_dir=args.model_name_or_path, use_cuda=use_cuda)
 
 # define the app
 app = FastAPI()