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| # https://huggingface.co/HannahLin271/nanoGPT_single_conversation/resolve/main/pytorch_model.bin | |
| import os | |
| import torch | |
| from model import GPTConfig, GPT | |
| from huggingface_hub import hf_hub_download | |
| import shutil | |
| import re | |
| import sys | |
| out_dir = "./out" | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| import requests | |
| from pathlib import Path | |
| from tqdm import tqdm | |
| import gradio as gr | |
| def download_file(url, output_path): | |
| response = requests.get(url, stream=True) | |
| response.raise_for_status() | |
| total_size = int(response.headers.get("content-length", 0)) | |
| block_size = 1024 | |
| # Create a progress bar | |
| progress_bar = tqdm(total=total_size, unit="iB", unit_scale=True) | |
| with open(output_path, "wb") as file: | |
| for chunk in response.iter_content(chunk_size=block_size): | |
| progress_bar.update(len(chunk)) | |
| file.write(chunk) | |
| progress_bar.close() | |
| if total_size != 0 and progress_bar.n != total_size: | |
| print("Error: Downloaded file size does not match expected size") | |
| else: | |
| print(f"Download complete: {output_path}") | |
| try: | |
| # Send a GET request to the URL | |
| response = requests.get(url, stream=True) | |
| response.raise_for_status() # Check if the request was successful | |
| if not os.path.exists(output_path): | |
| print("downloading...") | |
| output_path = Path(output_path) | |
| output_path.parent.mkdir(parents=True, exist_ok=True) | |
| with open(output_path, "wb") as file: | |
| for chunk in response.iter_content(chunk_size=8192): | |
| file.write(chunk) | |
| print(f"File downloaded successfully and saved as {output_path}") | |
| except requests.exceptions.RequestException as e: | |
| print(f"An error occurred: {e}") | |
| def init_model_from(url, filename): | |
| # if file not exists, download | |
| ckpt_path = Path(out_dir) / filename | |
| ckpt_path.parent.mkdir(parents=True, exist_ok=True) | |
| if not os.path.exists(ckpt_path): | |
| gr.Info('Downloading model...',duration=10) | |
| download_file(url, ckpt_path) | |
| gr.Info('✅Model downloaded successfully.', duration=2) | |
| checkpoint = torch.load(ckpt_path, map_location=device) | |
| gptconf = GPTConfig(**checkpoint['model_args']) | |
| model = GPT(gptconf) | |
| state_dict = checkpoint['model'] | |
| unwanted_prefix = '_orig_mod.' | |
| for k,v in list(state_dict.items()): | |
| if k.startswith(unwanted_prefix): | |
| state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k) | |
| model.load_state_dict(state_dict) | |
| return model | |
| def respond(input, samples, model, encode, decode, max_new_tokens,temperature, top_k): | |
| input = "<bot> " + input | |
| x = (torch.tensor(encode(input), dtype=torch.long, device=device)[None, ...]) | |
| with torch.no_grad(): | |
| for k in range(samples): | |
| generated = model.generate(x, max_new_tokens, temperature=temperature, top_k=top_k) | |
| output = decode(generated[0].tolist()) | |
| # if input in output: | |
| # output = output.split(input)[-1].strip() # Take the part after `<input>` | |
| match_botoutput = re.search(r'<human>(.*?)<', output, re.DOTALL) | |
| response = '' | |
| if match_botoutput: | |
| try : | |
| response = match_botoutput.group(1).strip() | |
| except: | |
| response = '' | |
| #return response, emotion, context | |
| return [input, response, output] |