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import gradio as gr
import requests
import json
import os
#os.system(f"pip install torch torchvision")
os.system(f"pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116")
os.system(f"pip install git+https://github.com/huggingface/transformers")
#os.system(f"git clone https://github.com/camenduru/stable-diffusion-webui /home/user/app/stable-diffusion-webui")
#Import Hugging Face's Transformers
from transformers import pipeline
# This is to log our outputs in a nicer format
from pprint import pprint
# from transformers import GPTJForCausalLM
# import torch
# model = GPTJForCausalLM.from_pretrained(
# "EleutherAI/gpt-j-6B", revision="float16", torch_dtype=torch.float16, low_cpu_mem_usage=True
# )
generator = pipeline('text-generation', model='EleutherAI/gpt-neo-1.3B')
# from transformers import GPTJForCausalLM, AutoTokenizer
# import torch
# model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", torch_dtype=torch.float16, low_cpu_mem_usage=True)
# tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
# prompt = (
# "In a shocking finding, scientists discovered a herd of unicorns living in a remote, "
# "previously unexplored valley, in the Andes Mountains. Even more surprising to the "
# "researchers was the fact that the unicorns spoke perfect English."
# )
# input_ids = tokenizer(prompt, return_tensors="pt").input_ids
# gen_tokens = model.generate(
# input_ids,
# do_sample=True,
# temperature=0.9,
# max_length=100,
# )
# gen_text = tokenizer.batch_decode(gen_tokens)[0]
def run(prompt, max_len, temp):
min_len = 1
output = generator(prompt, do_sample=True, min_length=min_len, max_length=max_len, temperature=temp)
return (output[0]['generated_text'],"")
if __name__ == "__main__":
demo = gr.Blocks()
with demo:
with gr.Row():
with gr.Column():
text = gr.Textbox(
label="Input",
value=" ", # should be set to " " when plugged into a real API
)
tokens = gr.Slider(1, 250, value=50, step=1, label="Tokens to generate")
temp = gr.Slider(0.1, 1.0, value=0.7, step=0.1, label="Temperature")
with gr.Row():
submit = gr.Button("Submit")
with gr.Column():
text_error = gr.Markdown(label="Log information")
text_out = gr.Textbox(label="Output")
submit.click(
run,
inputs=[text, tokens, temp],
outputs=[text_out, text_error],
)
demo.launch()
#gr.Interface.load("models/EleutherAI/gpt-j-6B").launch() |