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import csv
from datetime import datetime
import os
from typing import Optional
import gradio as gr
from convert import convert
from huggingface_hub import HfApi, Repository
DATASET_REPO_URL = "https://huggingface.co/datasets/safetensors/conversions"
DATA_FILENAME = "data.csv"
DATA_FILE = os.path.join("data", DATA_FILENAME)
HF_TOKEN = os.environ.get("HF_TOKEN")
repo: Optional[Repository] = None
# TODO
if False and HF_TOKEN:
repo = Repository(local_dir="data", clone_from=DATASET_REPO_URL, token=HF_TOKEN)
def run(model_id: str, is_private: bool, token: Optional[str] = None) -> str:
if model_id == "":
return """
### Invalid input 🐞
Please fill a token and model_id.
"""
try:
if is_private:
api = HfApi(token=token)
else:
api = HfApi(token=HF_TOKEN)
hf_is_private = api.model_info(repo_id=model_id).private
if is_private and not hf_is_private:
# This model is NOT private
# Change the token so we make the PR on behalf of the bot.
api = HfApi(token=HF_TOKEN)
print("is_private", is_private)
commit_info, errors = convert(api=api, model_id=model_id)
print("[commit_info]", commit_info)
string = f"""
### Success 🔥
Yay! This model was successfully converted and a PR was open using your token, here:
[{commit_info.pr_url}]({commit_info.pr_url})
"""
if errors:
string += "\nErrors during conversion:\n"
string += "\n".join(f"Error while converting {filename}: {e}, skipped conversion" for filename, e in errors)
return string
except Exception as e:
return f"""
### Error 😢😢😢
{e}
"""
DESCRIPTION = """
The steps are the following:
- Paste a read-access token from hf.co/settings/tokens. Read access is enough given that we will open a PR against the source repo.
- Input a model id from the Hub
- Click "Submit"
- That's it! You'll get feedback if it works or not, and if it worked, you'll get the URL of the opened PR 🔥
⚠️ For now only `pytorch_model.bin` files are supported but we'll extend in the future.
"""
title="Convert any model to Safetensors and open a PR"
allow_flagging="never"
def token_text(visible=False):
return gr.Text(max_lines=1, label="your_hf_token", visible=visible)
with gr.Blocks(title=title) as demo:
description = gr.Markdown(f"""# {title}""")
description = gr.Markdown(DESCRIPTION)
with gr.Row() as r:
with gr.Column() as c:
model_id = gr.Text(max_lines=1, label="model_id")
is_private = gr.Checkbox(label="Private model")
token = token_text()
with gr.Row() as c:
clean = gr.ClearButton()
submit = gr.Button("Submit", variant="primary")
with gr.Column() as d:
output = gr.Markdown()
is_private.change(lambda s: token_text(s), inputs=is_private, outputs=token)
submit.click(run, inputs=[model_id, is_private, token], outputs=output, concurrency_limit=1)
demo.queue(max_size=10).launch(show_api=True)
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