Upload app.py

app.py

@@ -88,7 +88,7 @@ class BigramLanguageModel(nn.Module):
             logits = logits[:, -1, :]  # Get the logits for the last token
             probs = nn.functional.softmax(logits, dim=-1)
             idx_next = torch.multinomial(probs, num_samples=1)
-            idx_next = torch.clamp(idx_next, max=60)  # Cap the generated index to max 60
+            idx_next = torch.clamp(idx_next, min=0, max=60)  # Strictly enforce index range [0, 60]
             idx = torch.cat((idx, idx_next), dim=1)
         return idx
 
@@ -108,7 +108,7 @@ chars = sorted(list(set("abcdefghijklmnopqrstuvwxyz0123456789 .,!?-:;'\"\n")))
 stoi = {ch: i for i, ch in enumerate(chars)}
 itos = {i: ch for i, ch in enumerate(chars)}
 encode = lambda s: [stoi.get(c, stoi.get(c.lower(), -1)) for c in s if c in stoi or c.lower() in stoi]  # Handles both cases
-decode = lambda l: ''.join([itos[i] for i in l])
+decode = lambda l: ''.join([itos[i] for i in l if i < len(itos)])  # Ensures index is within bounds
 
 # Function to generate text using the model
 def generate_text(prompt):
@@ -146,8 +146,7 @@ interface = gr.Interface(
     inputs=gr.Textbox(lines=2, placeholder="Enter a location or prompt..."),
     outputs="text",
     title="Triptuner Model",
-    description="Generate itineraries for locations in Sri Lanka's Central Province.",
-    theme="compact",  # Add a theme for better UI appearance
+    description="Generate itineraries for locations in Sri Lanka's Central Province."
 )
 
 # Launch the interface