Upload app.py

app.py

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+import math
+import zipfile
+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.common.initializer import Uniform, HeUniform
+
+
+def load_glove():
+    embeddings = []
+    tokens = []
+    with open("./lstm/glove.6B.100d.txt", encoding='utf-8') as gf:
+        for glove in gf:
+            word, embedding = glove.split(maxsplit=1)
+            tokens.append(word)
+            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)
+    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.rnn = nn.LSTM(embedding_dim,
+                           hidden_dim,
+                           num_layers=n_layers,
+                           bidirectional=bidirectional,
+                           dropout=dropout,
+                           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.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)
+        return self.sigmoid(output)
+
+score_map = {
+    1: "Positive",
+    0: "Negative"
+}
+
+def predict_sentiment(model, vocab, sentence):
+    model.set_train(False)
+    tokenized = sentence.lower().split()
+    indexed = vocab.tokens_to_ids(tokenized)
+    tensor = mindspore.Tensor(indexed, mindspore.int32)
+    tensor = tensor.expand_dims(0)
+    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("./lstm/sentiment-analysis.ckpt")
+    
+    # 将参数加载到网络中
+    load_param_into_net(net, param_dict)
+    model = Model(net)
+    
+    # 预测
+    pred = predict_sentiment(model, vocab, sentence)
+    result = {
+            "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)