Simplify

app.py

@@ -1,26 +1,15 @@
-import csv
-from datetime import datetime
 import os
 from typing import Optional
 import gradio as gr
 
 import quantize
-from huggingface_hub import HfApi, Repository, login
-
-
-DATASET_REPO_URL = "https://huggingface.co/datasets/safetensors/conversions"
-DATA_FILENAME = "data.csv"
-DATA_FILE = os.path.join("data", DATA_FILENAME)
+from huggingface_hub import HfApi, login
 
 HF_TOKEN = os.environ.get("HF_TOKEN")
 
 def run(model_id: str, model_version: str, additional_args: str, is_private: bool, token: Optional[str] = None) -> str:
     if model_id == "":
-        return """
-        ### Invalid input 🐞
-        
-        Please fill a token and model_id.
-        """
+        return "Please fill a token and model_id."
     login(token=token)
     if is_private:
         api = HfApi(token=token)
@@ -28,8 +17,6 @@ def run(model_id: str, model_version: str, additional_args: str, is_private: boo
         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)
@@ -38,12 +25,7 @@ def run(model_id: str, model_version: str, additional_args: str, is_private: boo
 
 
 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.
+Simple utility tool to convert automatically quantize diffusion models and convert them to CoreML.
 """
 
 title="Quantize model and convert to CoreML"