app.py

import torch
import gradio as gr
from transformers import GPT2Config
from safetensors.torch import load_file
from model import GPT2LMHeadModel

# ---- LOAD YOUR MODEL ----
MODEL_REPO = "Hai929/The_GuageLLM_12M"

config = GPT2Config.from_pretrained(MODEL_REPO)
model = GPT2LMHeadModel(config)

state = load_file("model.safetensors")
model.load_state_dict(state, strict=False)
model.eval()

# ---- TOKENIZER (CHAR LEVEL) ----
def encode(text):
    return torch.tensor([[ord(c) % 256 for c in text]], dtype=torch.long)

def decode(tokens):
    return "".join(chr(int(t)) for t in tokens)

# ---- GENERATION ----
@torch.no_grad()
def chat(message, history):
    ids = encode(message)

    for _ in range(32):
        logits = model(ids).logits[:, -1, :]
        probs = torch.softmax(logits, dim=-1)
        next_token = torch.multinomial(probs, 1)
        ids = torch.cat([ids, next_token], dim=1)

    text = decode(ids[0])
    return text.split(".")[0] + "."

# ---- UI ----
gr.ChatInterface(
    fn=chat,
    title="GuageLLM",
    description="A small language model trained from scratch."
).launch()