add gradio app (#2161)

modules/text/machine_translation/transformer/zh-en/README.md

@@ -132,6 +132,9 @@
 
   - 关于PaddleHub Serving更多信息参考：[服务部署](../../../../docs/docs_ch/tutorial/serving.md)
 
+- ### Gradio APP 支持
+  从 PaddleHub 2.3.1 开始支持使用链接 http://127.0.0.1:8866/gradio/transformer_zh-en 在浏览器中访问 transformer_zh-en 的 Gradio APP。
+
 ## 五、更新历史
 
 * 1.0.0
@@ -141,6 +144,11 @@
 * 1.0.1
 
   修复模型初始化的兼容性问题
+
+* 1.1.0
+
+  添加 Gradio APP 支持
+
   - ```shell
-    $ hub install transformer_zh-en==1.0.1
+    $ hub install transformer_zh-en==1.1.0
     ```

modules/text/machine_translation/transformer/zh-en/module.py

@@ -11,24 +11,26 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 import os
 from typing import List
 
 import paddle
 import paddle.nn as nn
-from paddlehub.env import MODULE_HOME
-from paddlehub.module.module import moduleinfo, serving
-import paddlenlp
-from paddlenlp.data import Pad, Vocab
-from paddlenlp.transformers import InferTransformerModel, position_encoding_init
+from paddlenlp.data import Pad
+from paddlenlp.data import Vocab
+from paddlenlp.transformers import InferTransformerModel
+from paddlenlp.transformers import position_encoding_init
+from transformer_zh_en.utils import MTTokenizer
+from transformer_zh_en.utils import post_process_seq
 
-from transformer_zh_en.utils import MTTokenizer, post_process_seq
+from paddlehub.env import MODULE_HOME
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.module import serving
 
 
 @moduleinfo(
     name="transformer_zh-en",
-    version="1.0.1",
+    version="1.1.0",
     summary="",
     author="PaddlePaddle",
     author_email="",
@@ -85,15 +87,14 @@ class MTTransformer(nn.Layer):
 
         self.max_length = max_length
         self.beam_size = beam_size
-        self.tokenizer = MTTokenizer(
-            bpe_codes_file=bpe_codes_file, lang_src=self.lang_config['source'], lang_trg=self.lang_config['target'])
-        self.src_vocab = Vocab.load_vocabulary(
-            filepath=src_vocab_file,
+        self.tokenizer = MTTokenizer(bpe_codes_file=bpe_codes_file,
+                                     lang_src=self.lang_config['source'],
+                                     lang_trg=self.lang_config['target'])
+        self.src_vocab = Vocab.load_vocabulary(filepath=src_vocab_file,
                                                unk_token=self.vocab_config['unk_token'],
                                                bos_token=self.vocab_config['bos_token'],
                                                eos_token=self.vocab_config['eos_token'])
-        self.trg_vocab = Vocab.load_vocabulary(
-            filepath=trg_vocab_file,
+        self.trg_vocab = Vocab.load_vocabulary(filepath=trg_vocab_file,
                                                unk_token=self.vocab_config['unk_token'],
                                                bos_token=self.vocab_config['bos_token'],
                                                eos_token=self.vocab_config['eos_token'])
@@ -101,8 +102,7 @@ class MTTransformer(nn.Layer):
             // self.vocab_config['pad_factor'] * self.vocab_config['pad_factor']
         self.trg_vocab_size = (len(self.trg_vocab) + self.vocab_config['pad_factor'] - 1) \
             // self.vocab_config['pad_factor'] * self.vocab_config['pad_factor']
-        self.transformer = InferTransformerModel(
-            src_vocab_size=self.src_vocab_size,
+        self.transformer = InferTransformerModel(src_vocab_size=self.src_vocab_size,
                                                  trg_vocab_size=self.trg_vocab_size,
                                                  bos_id=self.vocab_config['bos_id'],
                                                  eos_id=self.vocab_config['eos_id'],
@@ -184,3 +184,20 @@ class MTTransformer(nn.Layer):
                     results.append(trg_sample_text)
 
         return results
+
+    def create_gradio_app(self):
+        import gradio as gr
+
+        def inference(text):
+            results = self.predict(data=[text])
+            return results[0]
+
+        examples = [['今天是个好日子']]
+
+        interface = gr.Interface(inference,
+                                 "text", [gr.outputs.Textbox(label="Translation")],
+                                 title="transformer_zh-en",
+                                 examples=examples,
+                                 allow_flagging='never')
+
+        return interface

modules/text/machine_translation/transformer/zh-en/utils.py

@@ -11,25 +11,24 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
+import codecs
 import logging
 import re
 from typing import List
 
-import codecs
 import jieba
 
 jieba.setLogLevel(logging.INFO)
 
-from sacremoses import MosesTokenizer, MosesDetokenizer
+from sacremoses import MosesDetokenizer
 from subword_nmt.apply_bpe import BPE
 
 
 class MTTokenizer(object):
+
     def __init__(self, bpe_codes_file: str, lang_src: str = 'zh', lang_trg: str = 'en', separator='@@'):
         self.moses_detokenizer = MosesDetokenizer(lang=lang_trg)
-        self.bpe_tokenizer = BPE(
-            codes=codecs.open(bpe_codes_file, encoding='utf-8'),
+        self.bpe_tokenizer = BPE(codes=codecs.open(bpe_codes_file, encoding='utf-8'),
                                  merges=-1,
                                  separator=separator,
                                  vocab=None,