app update

app.py

@@ -155,8 +155,8 @@ class BigramLanguageModel(nn.Module):
         self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)])
         self.ln_f = nn.LayerNorm(n_embd)
         self.lm_head = nn.Linear(n_embd, self.vocab_size)
-        self.encode = lambda s: [stoi[c] for c in s] # encoder: take a string, output a list of integers
-        self.decode = lambda l: ''.join([itos[i] for i in l]) # decoder: take a list of integers, output a string
+        self.encode = lambda s: [self.stoi[c] for c in s] # encoder: take a string, output a list of integers
+        self.decode = lambda l: ''.join([self.itos[i] for i in l]) # decoder: take a list of integers, output a string
 
         
     def forward(self, idx, targets=None):
@@ -242,7 +242,7 @@ def generate_shakespeare_outputs(prompt=None, max_new_tokens=2000):
     context = torch.tensor(shakespeare_model.encode(prompt), dtype=torch.long, device=device).view(1, -1)
   else:
     context = torch.zeros((1, 1), dtype=torch.long, device=device)
-  text_output = decode(shakespeare_model.generate(context, max_new_tokens=max_new_tokens)[0].tolist())
+  text_output = shakespeare_model.decode(shakespeare_model.generate(context, max_new_tokens=max_new_tokens)[0].tolist())
   return text_output
 
 
@@ -251,7 +251,7 @@ def generate_wikipedia_outputs(prompt=None, max_new_tokens=2000):
     context = torch.tensor(wikipedia_model.encode(prompt), dtype=torch.long, device=device).view(1, -1)
   else:
     context = torch.zeros((1, 1), dtype=torch.long, device=device)
-  text_output = decode(wikipedia_model.generate(context, max_new_tokens=max_new_tokens)[0].tolist())
+  text_output = wikipedia_model.decode(wikipedia_model.generate(context, max_new_tokens=max_new_tokens)[0].tolist())
   return text_output