Create app.py

app.py

@@ -0,0 +1,55 @@
+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+from peft import PeftModel
+from threading import Thread
+
+BASE_MODEL = "Qwen/Qwen2.5-Coder-7B-Instruct"
+LORA_REPO = "alxstuff/Lumen-7b"
+
+print("Loading tokenizer...")
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
+
+print("Loading base model...")
+model = AutoModelForCausalLM.from_pretrained(
+    BASE_MODEL,
+    torch_dtype=torch.float16,
+    device_map="auto",
+    trust_remote_code=True,
+)
+
+print("Loading LoRA adapter...")
+model = PeftModel.from_pretrained(model, LORA_REPO)
+model.eval()
+print("✅ Lumen ready!")
+
+def chat(message, history):
+    prompt = "<|im_start|>system\nYou are Lumen, an expert AI coding assistant built by TheAlxLabs. You write clean, efficient code and explain it clearly.<|im_end|>\n"
+    for user, assistant in history:
+        prompt += f"<|im_start|>user\n{user}<|im_end|>\n<|im_start|>assistant\n{assistant}<|im_end|>\n"
+    prompt += f"<|im_start|>user\n{message}<|im_end|>\n<|im_start|>assistant\n"
+
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+
+    thread = Thread(target=model.generate, kwargs={
+        **inputs,
+        "streamer": streamer,
+        "max_new_tokens": 1024,
+        "temperature": 0.2,
+        "do_sample": True,
+    })
+    thread.start()
+
+    response = ""
+    for token in streamer:
+        response += token
+        yield response
+
+gr.ChatInterface(
+    fn=chat,
+    title="⚡ Lumen — AI Coding Assistant",
+    description="Local-first AI coding assistant by TheAlxLabs. Ask me to write, fix, or explain code.",
+    examples=["Write a Python function to reverse a linked list", "Explain what this does: `[x for x in range(10) if x % 2 == 0]`", "Fix this bug: TypeError: 'NoneType' object is not subscriptable"],
+    theme=gr.themes.Soft(),
+).launch()