Upload updated app.py

app.py

@@ -5,10 +5,14 @@ from transformers import GPT2Tokenizer
 # Assuming 'GPTLanguageModel' is already defined
 class GPTLanguageModel(torch.nn.Module):
     def forward(self, input_ids):
+        # Placeholder forward function
         pass
 
     def generate(self, input_ids, max_length=100):
-        return torch.tensor([[input_ids]])  # This is a placeholder for generation
+        # This is a placeholder. Replace this with your actual text generation logic.
+        # Right now it just returns the input back, but in your real model, this would
+        # generate new tokens.
+        return input_ids  # Just returning the input as is, to mimic generation
 
 class CustomTextGenerationPipeline:
     def __init__(self, model, tokenizer):
@@ -17,12 +21,13 @@ class CustomTextGenerationPipeline:
 
     def __call__(self, prompt, max_length=100):
         input_ids = self.tokenizer.encode(prompt, return_tensors='pt')
+        # Generate text using the model (this is currently simplified)
         generated_ids = self.model.generate(input_ids, max_length=max_length)
         return self.tokenizer.decode(generated_ids[0], skip_special_tokens=True)
 
 # Load model and tokenizer
 model = GPTLanguageModel()
-model.load_state_dict(torch.load("model.pth"))  # Load the weights
+model.load_state_dict(torch.load("model.pth", map_location=torch.device('cpu')))  # Load weights onto CPU
 model.eval()
 
 tokenizer = GPT2Tokenizer.from_pretrained("gpt2")