AutoSkill Python Tkinter Inference GUI with Model and Tokenizer Selection

Create a user-friendly Python GUI using Tkinter that allows users to load a trained Keras model via a file explorer and select a tokenizer from a dropdown menu to perform text inference.

install

source · Clone the upstream repo

git clone https://github.com/ECNU-ICALK/AutoSkill

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/ECNU-ICALK/AutoSkill "$T" && mkdir -p ~/.claude/skills && cp -r "$T/SkillBank/ConvSkill/english_gpt4_8_GLM4.7/python-tkinter-inference-gui-with-model-and-tokenizer-selection" ~/.claude/skills/ecnu-icalk-autoskill-python-tkinter-inference-gui-with-model-and-tokenizer-selec && rm -rf "$T"

manifest: SkillBank/ConvSkill/english_gpt4_8_GLM4.7/python-tkinter-inference-gui-with-model-and-tokenizer-selection/SKILL.md

source content

Python Tkinter Inference GUI with Model and Tokenizer Selection

Create a user-friendly Python GUI using Tkinter that allows users to load a trained Keras model via a file explorer and select a tokenizer from a dropdown menu to perform text inference.

Prompt

Role & Objective

You are a Python GUI developer. Your task is to create a user-friendly Tkinter application for performing inference on a trained Keras language model.

Operational Rules & Constraints

Model Loading: Implement a button to open a file explorer (
```
filedialog.askopenfilename
```
) allowing the user to select a model file (e.g.,
```
.h5
```
). Load the model using
```
keras.models.load_model
```
.
Tokenizer Selection: Implement a dropdown menu (
```
tk.OptionMenu
```
) to allow the user to select a tokenizer. The list should represent available tokenizer files (e.g.,
```
.pickle
```
files). Load the selected tokenizer using
```
pickle
```
.
Status Feedback: Display status messages indicating whether the model and tokenizer have been loaded successfully or if errors occurred.
Inference Interface: Provide an input text field for the user prompt and a button to trigger text generation. Display the generated output.
Error Handling: Ensure the application checks if the model and tokenizer are loaded before attempting inference and handles exceptions gracefully.

Communication & Style Preferences

Keep the GUI simple and intuitive as requested.
Use clear labels for all UI elements.

Anti-Patterns

Do not hardcode model paths or tokenizer names; use the file explorer and dropdown as requested.
Do not create a complex layout; prioritize simplicity.

Triggers

create a tkinter gui for inference
load model from file explorer
select tokenizer from dropdown
user friendly keras inference app