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| import torch | |
| from huggingface_hub import hf_hub_download | |
| from bot_model import GPT, decode, greedy_until_eos, extract_numbers, patch_final_answer, render_add_steps | |
| # Step 1: Download file (returns local path) | |
| ckpt_path = hf_hub_download( | |
| repo_id="codeninja3d/minigpt-model", | |
| filename="model_latest.pt", | |
| repo_type="model" | |
| ) | |
| # Step 2: Load checkpoint | |
| checkpoint = torch.load(ckpt_path, map_location="cpu") | |
| # 🔎 Sanity check | |
| if not isinstance(checkpoint, dict): | |
| raise RuntimeError( | |
| f"❌ Checkpoint format invalid! Expected dict, got {type(checkpoint)}. " | |
| f"Value preview: {str(checkpoint)[:200]}" | |
| ) | |
| missing_keys = [k for k in ["model", "vocab", "block_size"] if k not in checkpoint] | |
| if missing_keys: | |
| raise RuntimeError( | |
| f"❌ Checkpoint missing keys: {missing_keys}. Found keys: {list(checkpoint.keys())}" | |
| ) | |
| # Step 3: Restore vocab + block size | |
| stoi, itos = checkpoint["vocab"] | |
| block_size = checkpoint["block_size"] | |
| # Step 4: Build model + load weights | |
| model = GPT(len(stoi), max_len=block_size) | |
| model.load_state_dict(checkpoint["model"]) | |
| model.eval() | |
| def chatbot_reply(user_text: str): | |
| nums = extract_numbers(user_text) | |
| if len(nums) < 2: | |
| return "Tell me two or more numbers to add." | |
| if len(nums) >= 3: | |
| return render_add_steps(nums) | |
| # Exactly 2 numbers → GPT inference | |
| expr = " + ".join(str(n) for n in nums) | |
| prompt = f"q: {expr} a:" | |
| space_id = stoi.get(" ", next(iter(stoi.values()))) # safe fallback | |
| idx = torch.tensor([[stoi.get(c, space_id) for c in prompt]], dtype=torch.long) | |
| out = greedy_until_eos(model, idx, itos) | |
| resp = decode(out[0].tolist(), itos) | |
| if "<EOS>" in resp: | |
| resp = resp.split("<EOS>")[0] | |
| resp = patch_final_answer(resp, nums) | |
| return resp.split("a:", 1)[-1].strip() | |