Delete app.py

app.py

@@ -1,119 +0,0 @@
-import warnings
-
-import gradio as gr
-import matplotlib.pyplot as plt
-import seaborn as sns
-import torch
-from torch import cuda, nn
-from torch.nn import functional as F
-
-warnings.filterwarnings("ignore")
-
-device = 'cuda' if cuda.is_available() else 'cpu'
-
-
-class Attention(nn.Module):
-    def __init__(self, in_features, *args, bias=True, **kwargs):
-        super(Attention, self).__init__(*args, **kwargs)
-        self.bias = bias
-        self.W = nn.Parameter(torch.randn(in_features, in_features))
-        if self.bias:
-            self.b = nn.Parameter(torch.randn(in_features))
-        self.u = nn.Parameter(torch.randn(in_features))
-        self.tanh = nn.Tanh()
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x):
-        uit = torch.matmul(x, self.W)
-        if self.bias:
-            uit += self.b
-        ait = torch.matmul(self.tanh(uit), self.u)
-        attention = self.softmax(ait)
-        return attention
-
-
-class GenderClassifierWithAttention(nn.Module):
-    def __init__(self, input_size, hidden_size, embed_size, *args, **kwargs):
-        super(GenderClassifierWithAttention, self).__init__(*args, **kwargs)
-        self.hid_dim = hidden_size
-
-        # embedding layer
-        self.embedding = nn.Embedding(
-            num_embeddings=input_size, embedding_dim=embed_size)
-
-        # attention layer
-        self.attention = Attention(self.hid_dim * 2)
-
-        # LSTM layer
-        self.lstm = nn.LSTM(embed_size, hidden_size=self.hid_dim,
-                            num_layers=2, bidirectional=True, batch_first=True)
-
-        # dropout layers
-        self.dropout1 = nn.Dropout(p=0.4)
-        self.dropout2 = nn.Dropout(p=0.4)
-
-        # normalization layers
-        self.batch_norm1 = nn.BatchNorm1d(num_features=15)
-        self.batch_norm2 = nn.BatchNorm1d(num_features=7)
-        self.layer_norm = nn.LayerNorm(40)
-
-        # linear layers
-        self.fc1 = nn.Linear(in_features=self.hid_dim * 2, out_features=15)
-        self.fc2 = nn.Linear(in_features=15, out_features=7)
-        self.fc3 = nn.Linear(in_features=7, out_features=2)
-
-        # activation functions
-        self.tanh = nn.Tanh()
-        self.relu = nn.ReLU()
-
-    def forward(self, x):
-        x = self.embedding(x).float()
-
-        out, (h, c) = self.lstm(x)
-        out = self.layer_norm(out)
-        attention = self.attention(self.tanh(out))
-        x = torch.einsum('ijk,ij->ik', out, attention)
-
-        x = self.tanh(self.fc1(x))
-        x = self.batch_norm1(x)
-        x = self.dropout1(x)
-
-        x = self.tanh(self.fc2(x))
-        x = self.batch_norm2(x)
-        x = self.dropout2(x)
-
-        x = self.fc3(x)
-        return x, attention
-
-
-def get_attention(model):
-    def inner(name):
-        import string
-        char_stoi = {val: key for key, val in dict(
-            enumerate(["<PAD>"] + list(string.ascii_lowercase))).items()}
-
-        name_mapped = [char_stoi[char] for char in name.lower()]
-
-        probs, attention = model(torch.tensor([name_mapped]).to(device))
-        probs = F.softmax(probs, dim=-1)
-        probs_dict = dict(
-            zip(["female", "male"], probs.cpu().detach().numpy().flatten().tolist()))
-
-        fig, ax = plt.subplots(nrows=1, ncols=1)
-        _ = sns.barplot(x=list(range(attention.shape[1])),
-                        y=attention.squeeze().cpu().detach().numpy(),
-                        color="#1FCECB", ax=ax)
-        _ = ax.set_xticklabels(list(name))
-
-        return probs_dict, fig
-
-    return inner
-
-
-if __name__ == "__main__":
-    model = torch.load("lstm_attention.pt", map_location=device)
-    interface = gr.Interface(get_attention(model),
-                             inputs="text", outputs=[gr.outputs.Label(label="gender"), gr.outputs.Image(type="plot", label="attention")],
-                             title="Visualizing Attention in Gender Classification",
-                             examples=["annabella", "ewart",
-                                       "blancha", "bronson"], allow_flagging="never").launch()