Create app.py

app.py

@@ -0,0 +1,48 @@
+import gradio as gr
+import torch
+import tiktoken
+from supplementary import GPTModel, generate_text_simple
+
+# Load model configuration
+GPT_CONFIG_124M = {
+    "vocab_size": 50257,
+    "context_length": 1024,
+    "emb_dim": 768,
+    "n_heads": 12,
+    "n_layers": 12,
+    "drop_rate": 0.1,
+    "qkv_bias": False
+}
+
+# Initialize model
+model = GPTModel(GPT_CONFIG_124M)
+
+# Load the trained weights
+model.load_state_dict(torch.load("my_gpt_model.pth", map_location=torch.device('cpu')))
+model.eval()
+
+tokenizer = tiktoken.get_encoding("gpt2")
+
+def generate(prompt, max_new_tokens):
+    token_ids = tokenizer.encode(prompt)
+    input_ids = torch.tensor(token_ids).unsqueeze(0)
+    output_ids = generate_text_simple(
+        model=model,
+        idx=input_ids,
+        max_new_tokens=max_new_tokens,
+        context_size=GPT_CONFIG_124M["context_length"]
+    )
+    return tokenizer.decode(output_ids.squeeze(0).tolist())
+
+iface = gr.Interface(
+    fn=generate,
+    inputs=[
+        gr.Textbox(label="Prompt"),
+        gr.Slider(minimum=1, maximum=100, value=20, step=1, label="Max New Tokens")
+    ],
+    outputs=gr.Textbox(label="Generated Text"),
+    title="SamGPT Text Generation",
+    description="Enter a prompt to generate text with the custom language model."
+)
+
+iface.launch()