Create app.py

app.py

@@ -0,0 +1,101 @@
+import gradio as gr
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import yaml
+
+
+config_path = huggingface_hub.hf_hub_download(
+    "jefsnacker/surname_mlp",
+    "torch_mlp_config.yaml")
+
+weights_path = huggingface_hub.hf_hub_download(
+    "jefsnacker/surname_mlp",
+    "mlp_weights.pt")
+
+with open(config_path, 'r') as file:
+    config = yaml.safe_load(file)
+    
+stoi = config['stoi']
+itos = {s:i for i,s in stoi.items()}
+
+class MLP(nn.Module):
+    def __init__(self, num_char, hidden_nodes, embeddings, window, num_layers):   
+        super(MLP, self).__init__()
+        
+        self.window = window
+        self.hidden_nodes = hidden_nodes
+        self.embeddings = embeddings
+        
+        self.C = nn.Parameter(torch.randn((num_char, embeddings)) * 0.1, requires_grad=True)
+        
+        self.first = nn.Linear(embeddings*window, hidden_nodes)
+
+        self.layers = nn.Sequential()
+        for i in range(num_layers):
+            self.layers = self.layers.extend(nn.Sequential(
+                nn.Linear(hidden_nodes, hidden_nodes, bias=False),
+                nn.BatchNorm1d(hidden_nodes),
+                nn.Tanh()))
+
+        self.final = nn.Linear(hidden_nodes, num_char)
+        
+    def forward(self, x):
+        x = self.C[x]
+        x = self.first(x.view(-1, self.window*self.embeddings))
+        
+        x = self.layers(x)
+
+        x = self.final(x)
+        return x
+    
+    def sample_char(self, x):
+        logits = self(x)
+        probs = F.softmax(logits, dim=1)
+        return torch.multinomial(probs, num_samples=1).item()
+    
+mlp = MLP(config['num_char'], 
+          config['hidden_nodes'], 
+          config['embeddings'], 
+          config['window'], 
+          config['num_layers'])
+
+mlp.load_state_dict(torch.load(weights_path))
+mlp.eval()
+
+def generate_names(name_start, number_of_names):
+    names = ""
+    for _ in range((int)(number_of_names)):
+    
+        # Initialize name with user input
+        name = ""
+        context = [0] * config['window']
+        for c in name_start.lower():
+            name += c
+            context = context[1:] + [stoi[c]]
+
+        # Run inference to finish off the name
+        while True:
+            ix = mlp.sample_char(context)
+
+            context = context[1:] + [ix]
+            name += itos[ix]
+            
+            if ix == 0:
+                break
+            
+        names += name + "\n"
+        
+    return names
+
+app = gr.Interface(
+    fn=generate_names,
+    inputs=[
+        gr.Textbox(placeholder="Start name with..."),
+        gr.Number(value=1)
+    ],
+    outputs="text",
+)
+app.launch()