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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] |