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##################################### Imports ######################################
# Generic imports
import gradio as gr
import json
import os
########################### Global objects and functions ###########################
def get_json_cfg():
"""Retrieve configuration file"""
config_path = os.getenv('CONFIG_PATH')
with open(config_path, 'r') as file:
config = json.load(file)
return config
conf = get_json_cfg()
def greet(model_name, prompt_template, name, dataset):
return f"Hello {name}!! Using model: {model_name} with template: {prompt_template}"
##################################### App UI #######################################
with gr.Blocks() as demo:
##### Title Block #####
gr.Markdown("# Instruction Tuning with Unsloth")
##### Model Inputs #####
# Select Model
model_name = gr.Dropdown(label="Model", choices=conf['model']['choices'], value="gpt2")
# Prompt template
prompt_template = gr.Textbox(label="Prompt Template", value="Instruction: {0}\nOutput: {1}")
# Prompt Input
name_input = gr.Textbox(label="Your Name")
# Dataset choice
dataset_choice = gr.Radio(label="Choose Dataset", choices=["Predefined Dataset", "Upload Your Own"], value="Predefined Dataset")
dataset_predefined = gr.Dropdown(label="Predefined Dataset", choices=['1', '2', '3'], value='1', visible=True)
dataset_upload = gr.File(label="Upload Dataset", visible=False)
# Function to update visibility based on user choice
def update_dataset_visibility(choice):
if choice == "Predefined Dataset":
dataset_predefined.visible = True
dataset_upload.visible = False
elif choice == "Upload Your Own":
dataset_predefined.visible = False
dataset_upload.visible = True
# Initial call to set visibility based on default choice
update_dataset_visibility(dataset_choice.value)
# Update visibility based on user choice
dataset_choice.change(update_dataset_visibility, inputs=[dataset_choice], outputs=[dataset_predefined, dataset_upload])
##### Model Outputs #####
# Text output
output = gr.Textbox(label="Output")
##### Execution #####
# Setup button
tune_btn = gr.Button("Start Fine Tuning")
# Execute button
tune_btn.click(fn=greet,
inputs=[model_name, prompt_template, name_input, dataset_predefined],
outputs=output)
##################################### Launch #######################################
if __name__ == "__main__":
demo.launch()
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