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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, run_finnetuning_process, generate_response
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(input):
#from diffusers import StableDiffusionPipeline
#pipe = StableDiffusionPipeline.from_pretrained("./output_model", torch_dtype=torch.float16)
#pipe = pipe.to("cuda")
#image = pipe(prompt).images[0]
output = generate_response(input)
return output
def train_model(*inputs):
if "IS_SHARED_UI" in os.environ:
raise gr.Error("This Space only works in duplicated instances")
run_training_process()
return f"Train Model Sucessful!!!"
def finnetuning_model(*inputs):
if "IS_SHARED_UI" in os.environ:
raise gr.Error("This Space only works in duplicated instances")
run_finnetuning_process()
return f"Finnetuning 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_finnetuning = gr.Button("Finnetuning Model")
# btn_finnetuning.click(fn=finnetuning_model, inputs=[], outputs=out)
btn_evaluate = gr.Button("Evaluate Model")
btn_evaluate.click(fn=evaluate_model, inputs=inp, outputs=out)
# btn_stop = gr.Button("Stop Model")
# btn_stop.click(fn=stop_model, inputs=[], outputs=out)
demo.launch() |