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()