app.py

##################################### Imports ######################################
# Generic imports
import gradio as gr
import json


# Module imports
from utilities.setup import get_json_cfg
from utilities.templates import prompt_template

########################### Global objects and functions ###########################

conf = get_json_cfg()

def textbox_visibility(radio):
    value = radio
    if value == "Hugging Face Hub Dataset":
        return gr.Dropdown(visible=bool(1))
    else:
        return gr.Dropdown(visible=bool(0))

def upload_visibility(radio):
    value = radio
    if value == "Upload Your Own":
        return gr.UploadButton(visible=bool(1)) #make it visible
    else:
        return gr.UploadButton(visible=bool(0))

def greet(model_name, inject_prompt, dataset, pefts):
    """The model call"""
    return f"Hello!! Using model: {model_name} with template: {inject_prompt}"

##################################### App UI #######################################

def main():
    with gr.Blocks() as demo:
    
        ##### Title Block #####
        gr.Markdown("# Instruction Tuning with Unsloth")
    
        ##### Model Inputs #####
        gr.Markdown("### Model Inputs")
        
        # Select Model
        modelnames = conf['model']['choices']
        model_name = gr.Dropdown(label="Supported Models", 
                                 choices=modelnames, 
                                 value=modelnames[0])
        # Prompt template
        inject_prompt = gr.Textbox(label="Prompt Template", 
                                     value=prompt_template())
        # Dataset choice
        dataset_choice = gr.Radio(label="Choose Dataset", 
                                  choices=["Hugging Face Hub Dataset", "Upload Your Own"], 
                                  value="Hugging Face Hub Dataset")
        dataset_predefined = gr.Textbox(label="Hugging Face Hub Dataset", 
                                        value='yahma/alpaca-cleaned', 
                                        visible=True)
        dataset_upload = gr.UploadButton(label="Upload Dataset (csv, jsonl, or txt)", 
                                         file_types=[".csv",".jsonl", ".txt"], 
                                         visible=False)
        data_field = gr.Textbox(label="Dataset Training Field",
                                value=conf['model']['general']["dataset_text_field"])
        dataset_choice.change(textbox_visibility, 
                              dataset_choice, 
                              dataset_predefined)
        dataset_choice.change(upload_visibility, 
                              dataset_choice, 
                              dataset_upload)

        gr.Markdown("### Model Parameter Selection")
        # Parameters
        max_seq_length = gr.Textbox(label="Maximum sequence length", 
                                     value=conf['model']['general']["max_seq_length"])
        random_seed = gr.Textbox(label="Seed",
                                value=conf['model']['general']["seed"])
        num_epochs = gr.Textbox(label="Training Epochs",
                                value=conf['model']['general']["num_train_epochs"])
        max_steps = gr.Textbox(label="Maximum steps",
                                value=conf['model']['general']["max_steps"])   
        repository = gr.Textbox(label="Repository Name",
                                value=conf['model']['general']["repository"])   
        model_out_name = gr.Textbox(label="Model Output Name",
                                value=conf['model']['general']["model_name"])   

        # Hyperparameters (allow selection, but hide in accordion.)
        with gr.Accordion("Advanced Tuning", open=False):

            sftparams = conf['model']['general']
            # accordion container content
            dict_string = json.dumps(dict(conf['model']['peft']), indent=4)
            peft = gr.Textbox(label="PEFT Parameters (json)", value=dict_string)
            
            dict_string = json.dumps(dict(conf['model']['sft']), indent=4)
            sft = gr.Textbox(label="SFT Parameters (json)", value=dict_string)            
        
        ##### Execution #####
    
        # Setup button
        tune_btn = gr.Button("Start Fine Tuning")
        gr.Markdown("### Model Progress")
        # Text output (for now)
        output = gr.Textbox(label="Output") 
        # Execute button
        tune_btn.click(fn=greet, 
                       inputs=[model_name, 
                               inject_prompt, 
                               dataset_predefined,
                               peft,
                               sft,
                               max_seq_length,
                               random_seed,
                               num_epochs,
                               max_steps,
                               data_field,
                               repository,
                               model_out_name
                              ],
                       outputs=output)
        # Launch baby
        demo.launch()

##################################### Launch #######################################

if __name__ == "__main__":
    main()