app.py

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

# Specialized imports
#from utilities.modeling import modeling

# server import
from server import submit_weights #, train_model, submit_weights


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

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

conf = get_files.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))
@spaces.GPU
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}"



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



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

        with gr.Tabs():
            with gr.TabItem("About"):
                # About page!!
                gr.Markdown(get_files.load_markdown_file("README.md"))

            with gr.TabItem("Basic Setup"):
                gr.Markdown("# Select Model and Input details")
                # Select Model
                modelnames = conf['model']['choices']
                model_name = gr.Dropdown(label="Supported Models", 
                                         choices=modelnames, 
                                         value=modelnames[0])
                # Select Generic Model parameters
                repository = gr.Textbox(label="Your User Name",
                                        value=conf['model']['general']["repository"])   
                model_out_name = gr.Textbox(label="Your Model Output Name",
                                        value=conf['model']['general']["model_name"])
                hf_token = gr.Textbox(label="Your Huggingface Token",
                                      type='password',
                                      value='')
                

            with gr.TabItem("Upload Data"):
                # Toggle dataset load types
                gr.Markdown("# Dataset Selection and Upload")
                
                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)
                # Safety output to show if upload succeeded.
                data_snippet = gr.Markdown()

                # Visibility toggler
                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)
                # Prompt template
                inject_prompt = gr.Textbox(label="Prompt Template", 
                                             value=prompt_template())
                # Dataset buttons
                dataset_predefined_load.click(fn=get_files.predefined_dataset,
                                          inputs=dataset_predefined,
                                          outputs=data_snippet)
        
                dataset_uploaded_load.click(fn=get_files.uploaded_dataset,
                                         inputs=dataset_uploaded_load,
                                         outputs=data_snippet)
         
            
            with gr.TabItem("Train Model"):
                ##### 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"])    
        
                # 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()