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| import argparse | |
| import itertools | |
| import math | |
| import os | |
| from pathlib import Path | |
| from typing import Optional | |
| import subprocess | |
| import sys | |
| import torch | |
| from spanish_medica_llm import run_training, run_training_process | |
| import gradio as gr | |
| #def greet(name): | |
| # return "Hello " + name + "!!" | |
| #iface = gr.Interface(fn=greet, inputs="text", outputs="text") | |
| #iface.launch() | |
| def generate_model(name): | |
| return f"Welcome to Gradio HF_ACCES_TOKEN, {os.environ.get('HG_FACE_TOKEN')}!" | |
| def generate(prompt): | |
| #from diffusers import StableDiffusionPipeline | |
| #pipe = StableDiffusionPipeline.from_pretrained("./output_model", torch_dtype=torch.float16) | |
| pipe = pipe.to("cuda") | |
| image = pipe(prompt).images[0] | |
| return(image) | |
| def evaluate_model(): | |
| #from diffusers import StableDiffusionPipeline | |
| #pipe = StableDiffusionPipeline.from_pretrained("./output_model", torch_dtype=torch.float16) | |
| #pipe = pipe.to("cuda") | |
| #image = pipe(prompt).images[0] | |
| return(f"Evaluate Model {os.environ.get('HF_LLM_MODEL_ID')} from dataset {os.environ.get('HF_LLM_DATASET_ID')}") | |
| def train_model(*inputs): | |
| if "IS_SHARED_UI" in os.environ: | |
| raise gr.Error("This Space only works in duplicated instances") | |
| # args_general = argparse.Namespace( | |
| # image_captions_filename = True, | |
| # train_text_encoder = True, | |
| # #stop_text_encoder_training = stptxt, | |
| # save_n_steps = 0, | |
| # #pretrained_model_name_or_path = model_to_load, | |
| # instance_data_dir="instance_images", | |
| # #class_data_dir=class_data_dir, | |
| # output_dir="output_model", | |
| # instance_prompt="", | |
| # seed=42, | |
| # resolution=512, | |
| # mixed_precision="fp16", | |
| # train_batch_size=1, | |
| # gradient_accumulation_steps=1, | |
| # use_8bit_adam=True, | |
| # learning_rate=2e-6, | |
| # lr_scheduler="polynomial", | |
| # lr_warmup_steps = 0, | |
| # #max_train_steps=Training_Steps, | |
| # ) | |
| # run_training(args_general) | |
| # torch.cuda.empty_cache() | |
| # #convert("output_model", "model.ckpt") | |
| # #shutil.rmtree('instance_images') | |
| # #shutil.make_archive("diffusers_model", 'zip', "output_model") | |
| # #with zipfile.ZipFile('diffusers_model.zip', 'w', zipfile.ZIP_DEFLATED) as zipf: | |
| # # zipdir('output_model/', zipf) | |
| # torch.cuda.empty_cache() | |
| # return [gr.update(visible=True, value=["diffusers_model.zip"]), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)] | |
| run_training_process() | |
| return f"Train Model Sucessful!!!" | |
| def stop_model(*input): | |
| return f"Model with Gradio!" | |
| with gr.Blocks() as demo: | |
| gr.Markdown("Start typing below and then click **Run** to see the output.") | |
| with gr.Row(): | |
| inp = gr.Textbox(placeholder="What is your name?") | |
| out = gr.Textbox() | |
| btn_response = gr.Button("Generate Response") | |
| btn_response.click(fn=generate_model, inputs=inp, outputs=out) | |
| btn_train = gr.Button("Train Model") | |
| btn_train.click(fn=train_model, inputs=[], outputs=out) | |
| btn_evaluate = gr.Button("Evaluate Model") | |
| btn_evaluate.click(fn=evaluate_model, inputs=[], outputs=out) | |
| btn_stop = gr.Button("Stop Model") | |
| btn_stop.click(fn=stop_model, inputs=[], outputs=out) | |
| demo.launch() |