app.py

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

# Specialized imports
#from utilities.modeling import modeling
from datasets import load_dataset


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

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

conf = get_json_cfg()

class update_visibility:

    def textbox_vis(radio):
        value = radio
        if value == "Hugging Face Hub Dataset":
            return gr.Dropdown(visible=bool(1))
        else:
            return gr.Dropdown(visible=bool(0))
    
    def textbox_button_vis(radio):
        value = radio
        if value == "Hugging Face Hub Dataset":
            return gr.Button(visible=bool(1))
        else:
            return gr.Button(visible=bool(0))
    
    def upload_vis(radio):
        value = radio
        if value == "Upload Your Own":
            return gr.UploadButton(visible=bool(1)) #make it visible
        else:
            return gr.UploadButton(visible=bool(0))

class get_datasets:

    def predefined_dataset(dataset_name):
        global dataset # bad practice, I know... But just bear with me. Will later update to state dict.
        dataset = load_dataset(dataset_name, split = "train")
        return 'Successfully loaded dataset'
    
    def uploaded_dataset(file):
        global dataset # bad practice, I know... But just bear with me. Will later update to state dict.
        dataset = []
        if file is None:
            return "File not found. Please upload the file again."
        try:
            with open(file,'r') as file:
                for line in file:
                    dataset.append(json.loads(line.strip()))
            return "File retrieved."
        except FileNotFoundError:
            return "File not found. Please upload the file again."



def about_page():
    return "## About\n\nThis is an application for uploading datasets. You can upload files in .csv, .jsonl, or .txt format. The app will process the file and provide feedback."





def show_about():
    return "## About\n\nThis is an application for uploading datasets. You can upload files in .csv, .jsonl, or .txt format. The app will process the file and provide feedback."

def show_upload():
    return "## Upload\n\nUse the button below to upload your dataset."



def train(model_name, 
          inject_prompt, 
          dataset_predefined,
          peft,
          sft,
          max_seq_length,
          random_seed,
          num_epochs,
          max_steps,
          data_field,
          repository,
          model_out_name):
    """The model call"""

    # Get models
    # trainer = modeling(model_name, max_seq_length, random_seed,
    #                    peft, sft, dataset, data_field)
    # trainer_stats = trainer.train()

    # Return outputs of training.
    
    return f"Hello!! Using model: {model_name} with template: {inject_prompt}"


def submit_weights(model, repository, model_out_name, token):
    """submits model to repository"""
    repo = repository + '/' + model_out_name
    
    model.push_to_hub(repo, token = token)
    tokenizer.push_to_hub(repo, token = token)
    return 0

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



def main():
    with gr.Blocks() as demo:


        with gr.Tabs():
            with gr.TabItem("About"):
                gr.Markdown("## About\n\nThis is an application for uploading datasets. You can upload files in .csv, .jsonl, or .txt format. The app will process the file and provide feedback.")
            with gr.TabItem("Train Model"):
                ##### Title Block #####
                gr.Markdown("# SLM Instruction Tuning with Unsloth")
            
                ##### Initial 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 Training Dataset", 
                                                value='yahma/alpaca-cleaned', 
                                                visible=True)
                
                dataset_predefined_load = gr.Button("Upload Dataset (.csv, .jsonl, or .txt)")
        
                dataset_uploaded_load = gr.UploadButton(label="Upload Dataset (.csv, .jsonl, or .txt)", 
                                                 file_types=[".csv",".jsonl", ".txt"], 
                                                 visible=False)
                data_snippet = gr.Markdown()
                
                dataset_choice.change(update_visibility.textbox_vis, 
                                      dataset_choice, 
                                      dataset_predefined)
                dataset_choice.change(update_visibility.upload_vis, 
                                      dataset_choice,
                                      dataset_uploaded_load)
                dataset_choice.change(update_visibility.textbox_button_vis,
                                      dataset_choice,
                                      dataset_predefined_load)
                
                # Dataset button
                dataset_predefined_load.click(fn=get_datasets.predefined_dataset,
                                          inputs=dataset_predefined,
                                          outputs=data_snippet)
        
                dataset_uploaded_load.click(fn=get_datasets.uploaded_dataset,
                                         inputs=dataset_uploaded_load,
                                         outputs=data_snippet)
        
        
        
                
        
                ##### Model Parameter Inputs #####
                gr.Markdown("### Model Parameter Selection")
                # Parameters
                data_field = gr.Textbox(label="Dataset Training Field Name",
                                        value=conf['model']['general']["dataset_text_field"])
                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 buttons
                tune_btn = gr.Button("Start Fine Tuning")
                gr.Markdown("### Model Progress")
                # Text output (for now)
                output = gr.Textbox(label="Output") 
                
                
                # Data retrieval
                
                
                # Execute buttons
                tune_btn.click(fn=train, 
                               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)
                # stop button
        
                # submit button
        
        # Launch baby
        demo.launch()

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

if __name__ == "__main__":
    main()