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import os
import shutil
import subprocess
import streamlit as st
import uuid
from git import Repo
import huggingface_hub
BACKEND_REPO_URL = "https://github.com/vodkaslime/ctranslate2-converter-backend"
HOME_DIR = os.path.expanduser("~")
BACKEND_DIR = os.path.join(HOME_DIR, "backend")
BACKEND_SCRIPT = os.path.join(BACKEND_DIR, "main.py")
MODEL_ROOT_DIR = os.path.join(HOME_DIR, "models")
st.title(":wave: Tabby Model Converter")
@st.cache_resource
def init():
if os.path.exists(BACKEND_DIR):
return
try:
Repo.clone_from(BACKEND_REPO_URL, BACKEND_DIR)
subprocess.check_call(
[
"pip",
"install",
"-r",
os.path.join(BACKEND_DIR, "requirements.txt"),
]
)
except Exception as e:
shutil.rmtree(BACKEND_DIR)
st.error(f"error initializing backend: {e}")
def convert_and_upload_model(
model,
output_dir,
inference_mode,
prompt_template,
huggingface_token,
upload_mode,
new_model,
):
# Verify parameters
if not model:
st.error("Must provide a model name")
return
if not new_model:
st.error("Must provide a new model name where the conversion will upload to")
return
if not huggingface_token:
st.error("Must provide a huggingface token")
return
command = ["python", BACKEND_SCRIPT]
command += ["--model", model]
command += ["--output_dir", output_dir]
command += ["--inference_mode", inference_mode]
if prompt_template:
command += ["--prompt_template", prompt_template]
# Login on behalf of user
huggingface_hub.login(huggingface_token)
# Handle model conversion
try:
with st.spinner("Converting model"):
subprocess.check_call(command)
except subprocess.CalledProcessError as e:
st.error(f"Error converting model to Tabby compatible format: {e}")
link = f"https://huggingface.co/{model}"
st.warning(
f"Note: do you have access to the model? If not, visit the model page at {link} and request for access"
)
return
st.success("Model successfully converted")
# Handle model upload
try:
with st.spinner("Uploading converted model"):
api = huggingface_hub.HfApi()
if upload_mode == "new repo":
api.create_repo(new_model)
api.upload_folder(folder_path=output_dir, repo_id=new_model)
except Exception as e:
st.error(f"Error uploading model: {e}")
return
st.success("Model successfully uploaded.")
def clean_up(output_dir):
try:
with st.spinner("Cleaning up"):
shutil.rmtree(output_dir)
except Exception as e:
st.error(f"Error removing work dir: {e}")
st.success("Cleaning up finished")
init()
model = st.text_input("Model name", placeholder="Salesforce/codet5p-220m")
inference_mode = st.radio(
"Inference mode",
("causallm", "seq2seq"),
)
prompt_template = st.text_input("Prompt template")
huggingface_token = st.text_input(
"Hugging face token (must be writable token)", type="password"
)
upload_mode = st.radio(
"Choose if you want to create a new model repo or push a commit to existing repo",
("new repo", "existing repo"),
)
new_model = st.text_input(
"The new model name that the model is going to be converted to",
placeholder="TabbyML/T5P-220M",
)
convert_button = st.button("Convert model", use_container_width=True)
if convert_button:
id = uuid.uuid4()
output_dir = os.path.join(MODEL_ROOT_DIR, str(id))
# Try converting and uploading model
convert_and_upload_model(
model,
output_dir,
inference_mode,
prompt_template,
huggingface_token,
upload_mode,
new_model,
)
# Clean up the conversion
clean_up(output_dir)
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