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.
Scanned 5/30/2026
Install via CLI
openskills install ECNU-ICALK/AutoSkill---
id: "dcb4f8f5-4ece-40a0-a139-95d5f5354564"
name: "Python Tkinter Inference GUI with Model and Tokenizer Selection"
description: "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."
version: "0.1.0"
tags:
- "python"
- "tkinter"
- "keras"
- "gui"
- "inference"
triggers:
- "create a tkinter gui for inference"
- "load model from file explorer"
- "select tokenizer from dropdown"
- "user friendly keras inference app"
---
# 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
1. **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`.
2. **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`.
3. **Status Feedback**: Display status messages indicating whether the model and tokenizer have been loaded successfully or if errors occurred.
4. **Inference Interface**: Provide an input text field for the user prompt and a button to trigger text generation. Display the generated output.
5. **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
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