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---
title: Refsheet Chat
emoji: 💬
colorFrom: gray
colorTo: green
sdk: gradio
sdk_version: 5.21.0
app_file: app.py
pinned: false
license: mit
short_description: Chat with a character via reference sheet!
---
# RefSheet Chat -- Chat with a character via reference sheet
Upload a reference sheet of a character, RefSheet Chat will try to understand the character through the reference sheet, and talk to you as that character. RefSheet Chat can run locally to ensure privacy.
Website: <https://refsheet.chat>
Tutorial slide (in Chinese) can be found in <https://snowkylin.github.io/talks/>
RefSheet Chat is a demo of [Gemma 3](https://blog.google/technology/developers/gemma-3/), demonstrating its excellent vision and multilingual capability.
## Environment Configuration
Register an account on [HuggingFace](https://huggingface.co)
Submit a Gemma Access Request from <https://huggingface.co/google/gemma-3-4b-it>. The access should be granted immediately with an email notification. After that, the model page will show
> Gated model: You have been granted access to this model
Create conda environment with pip and Python 3.12
```bash
conda create -n transformers_gemma pip python=3.12
conda activate transformers_gemma
```
Install [HuggingFace Transformers for Gemma 3](https://github.com/huggingface/transformers/releases/tag/v4.49.0-Gemma-3):
```bash
pip install git+https://github.com/huggingface/transformers@v4.49.0-Gemma-3
```
Install [PyTorch](https://pytorch.org/get-started/locally/)
On Nvidia GPU (with CUDA 12.6):
```bash
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126
```
On CPU:
```bash
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
```
Create an User Access Token from <https://huggingface.co/docs/hub/security-tokens>, then log in to your HuggingFace account with `huggingface-cli`:
```bash
huggingface-cli login
```
Copy-paste your access token and press enter.
## Packing
See <https://github.com/whitphx/gradio-pyinstaller-example> for more details
Create a hook file `runtime_hook.py` including environment variables
```python
# This is the hook patching the `multiprocessing.freeze_support` function,
# which we must import before calling `multiprocessing.freeze_support`.
import PyInstaller.hooks.rthooks.pyi_rth_multiprocessing # noqa: F401
import os
if __name__ == "__main__":
os.environ['PYINSTALLER'] = "1"
os.environ['HF_ENDPOINT'] = "https://hf-mirror.com" # optional, HF mirror site in China
os.environ['HF_TOKEN'] = "hf_XXXX" # HF token that allow access to Gemma 3
# This is necessary to prevent an infinite app launch loop.
import multiprocessing
multiprocessing.freeze_support()
```
Then
```commandline
pyi-makespec --collect-data=gradio_client --collect-data=gradio --collect-data=safehttpx --collect-data=groovy --runtime-hook=./runtime_hook.py app.py
```
open `app.spec` and add
```python
a = Analysis(
...,
module_collection_mode={
'gradio': 'py', # Collect gradio package as source .py files
}
}
```
then pack the environment
```commandline
pyinstaller --clean app.spec
```
finally copy the `win32ctypes` folder from your conda environment
```commandline
C:\Users\[Your-User-Name]\miniconda3\envs\[Your-Env-Name]\Lib\site-packages
```
to `dist/app/_internal`.
Run `app.exe` in `dist/app` and it should work.
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