Create app.py

app.py

@@ -0,0 +1,50 @@
+import gradio as gr
+import torch
+import json
+from tokenizers import Tokenizer
+from huggingface_hub import hf_hub_download
+from ModelArchitecture import Transformer, ModelConfig, generate
+from safetensors.torch import load_file
+
+# Load model
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+REPO_ID = "VirtualInsight/Lumen"
+
+model_path = hf_hub_download(repo_id=REPO_ID, filename="model.safetensors")
+tokenizer_path = hf_hub_download(repo_id=REPO_ID, filename="tokenizer.json")
+config_path = hf_hub_download(repo_id=REPO_ID, filename="config.json")
+
+tokenizer = Tokenizer.from_file(tokenizer_path)
+with open(config_path) as f:
+    config = ModelConfig(**json.load(f))
+
+model = Transformer(config).to(device)
+model.load_state_dict(load_file(model_path, device=str(device)), strict=False)
+model.eval()
+
+@torch.no_grad()
+def generate_text(prompt, max_tokens=100, temperature=0.7, top_p=0.9):
+    input_ids = torch.tensor(tokenizer.encode(prompt).ids).unsqueeze(0).to(device)
+    output_ids = generate(model, input_ids, max_tokens, temperature, top_p=top_p, device=device)
+    return tokenizer.decode(output_ids[0, input_ids.size(1):].cpu().tolist())
+
+# Gradio Interface
+demo = gr.Interface(
+    fn=generate_text,
+    inputs=[
+        gr.Textbox(label="Prompt", placeholder="Once upon a time...", lines=3),
+        gr.Slider(10, 500, value=100, label="Max Tokens"),
+        gr.Slider(0.1, 2.0, value=0.7, label="Temperature"),
+        gr.Slider(0.1, 1.0, value=0.9, label="Top-p"),
+    ],
+    outputs=gr.Textbox(label="Generated Text", lines=10),
+    title="🌟 Lumen Language Model",
+    description="Generate text using the Lumen language model",
+    examples=[
+        ["Once upon a time", 100, 0.7, 0.9],
+        ["The future of AI is", 150, 0.8, 0.95],
+    ]
+)
+
+if __name__ == "__main__":
+    demo.launch()