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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() |