Update app.py

app.py

@@ -1,39 +1,60 @@
+# Copyright 2022 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+# ============================================================================
+""" LSTM inference """
+
+
 import math
 import numpy as np
 
 import gradio as gr
 import mindspore
-import mindspore.nn as nn
 import mindspore.numpy as mnp
-import mindspore.ops as ops
-import mindspore.dataset as dataset
-from mindspore import Tensor
-from mindspore import load_checkpoint, load_param_into_net
+from mindspore import Tensor, nn, \
+    load_checkpoint, load_param_into_net, ops, dataset
 from mindspore.common.initializer import Uniform, HeUniform
 
 
 def load_glove():
     embeddings = []
     tokens = []
-    with open("./glove.6B.100d.txt", encoding='utf-8') as gf:
-        for glove in gf:
+    with open("./glove.6B.100d.txt", encoding='utf-8') as file:
+        for glove in file:
             word, embedding = glove.split(maxsplit=1)
             tokens.append(word)
-            embeddings.append(np.fromstring(embedding, dtype=np.float32, sep=' '))
+            embeddings.append(np.fromstring(embedding,
+                                            dtype=np.float32,
+                                            sep=' '))
     # 添加 <unk>, <pad> 两个特殊占位符对应的embedding
     embeddings.append(np.random.rand(100))
     embeddings.append(np.zeros((100,), np.float32))
 
-    vocab = dataset.text.Vocab.from_list(tokens, special_tokens=["<unk>", "<pad>"], special_first=False)
+    vocab = dataset.text.Vocab.from_list(tokens,
+                                         special_tokens=["<unk>", "<pad>"],
+                                         special_first=False)
     embeddings = np.array(embeddings).astype(np.float32)
     return vocab, embeddings
 
+
 class RNN(nn.Cell):
     def __init__(self, embeddings, hidden_dim, output_dim, n_layers,
                  bidirectional, dropout, pad_idx):
         super().__init__()
         vocab_size, embedding_dim = embeddings.shape
-        self.embedding = nn.Embedding(vocab_size, embedding_dim, embedding_table=Tensor(embeddings), padding_idx=pad_idx)
+        self.embedding = nn.Embedding(vocab_size, embedding_dim,
+                                      embedding_table=Tensor(embeddings),
+                                      padding_idx=pad_idx)
         self.rnn = nn.LSTM(embedding_dim,
                            hidden_dim,
                            num_layers=n_layers,
@@ -42,17 +63,22 @@ class RNN(nn.Cell):
                            batch_first=True)
         weight_init = HeUniform(math.sqrt(5))
         bias_init = Uniform(1 / math.sqrt(hidden_dim * 2))
-        self.fc = nn.Dense(hidden_dim * 2, output_dim, weight_init=weight_init, bias_init=bias_init)
+        self.fc_layer = nn.Dense(hidden_dim * 2, output_dim,
+                           weight_init=weight_init,
+                           bias_init=bias_init)
         self.dropout = nn.Dropout(1 - dropout)
         self.sigmoid = ops.Sigmoid()
 
     def construct(self, inputs):
         embedded = self.dropout(self.embedding(inputs))
         _, (hidden, _) = self.rnn(embedded)
-        hidden = self.dropout(mnp.concatenate((hidden[-2, :, :], hidden[-1, :, :]), axis=1))
-        output = self.fc(hidden)
+        hidden = self.dropout(mnp.concatenate((hidden[-2, :, :],
+                                               hidden[-1, :, :]),
+                                              axis=1))
+        output = self.fc_layer(hidden)
         return self.sigmoid(output)
 
+
 def predict_sentiment(model, vocab, sentence):
     model.set_train(False)
     tokenized = sentence.lower().split()
@@ -62,44 +88,44 @@ def predict_sentiment(model, vocab, sentence):
     prediction = model(tensor)
     return prediction.asnumpy()
 
-def prefict_emotion(sentence):
-    # 加载网路
-    hidden_size = 256
-    output_size = 1
-    num_layers = 2
-    bidirectional = True
-    dropout = 0.5
-    lr = 0.00
-    
-    vocab, embeddings = load_glove()
-    pad_idx = vocab.tokens_to_ids('<pad>')
-    net = RNN(embeddings, hidden_size, output_size, num_layers, bidirectional, dropout, pad_idx)
-    
-    # 将模型参数存入parameter的字典中
-    param_dict = load_checkpoint("./sentiment-analysis.ckpt")
-    
-    # 将参数加载到网络中
-    load_param_into_net(net, param_dict)
-    
+
+vocab, embeddings = load_glove()
+
+net = RNN(embeddings,
+          hidden_dim=256,
+          output_dim=1,
+          n_layers=2,
+          bidirectional=True,
+          dropout=0.5,
+          pad_idx=vocab.tokens_to_ids('<pad>'))
+# 将模型参数存入parameter的字典中
+param_dict = load_checkpoint("./sentiment-analysis.ckpt")
+
+# 将参数加载到网络中
+load_param_into_net(net, param_dict)
+
+
+def predict_emotion(sentence):
     # 预测
     pred = predict_sentiment(net, vocab, sentence).item()
     result = {
-            "Positive 🙂": pred,
-            "Negative 🙃": 1-pred,
-        }
+        "Positive 🙂": pred,
+        "Negative 🙃": 1 - pred,
+    }
     return result
-    
+
+
 gr.Interface(
-        fn=prefict_emotion,
-        inputs=gr.inputs.Textbox(
-            lines=3,
-            placeholder="Type a phrase that has some emotion",
-            label="Input Text",
-        ),
-        outputs="label",
-        title="Sentiment Analysis",
-        examples=[
-            "This film is terrible",
-            "This film is great",
-        ],
-    ).launch(share=True)
+    fn=predict_emotion,
+    inputs=gr.inputs.Textbox(
+        lines=3,
+        placeholder="Type a phrase that has some emotion",
+        label="Input Text",
+    ),
+    outputs="label",
+    title="基于LSTM的文本情感分类任务",
+    examples=[
+        "This film is terrible",
+        "This film is great",
+    ],
+).launch(share=True)