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import gradio as gr
# import spaces
import os
import gc
import random
import warnings
warnings.filterwarnings("ignore")
import numpy as np
import pandas as pd
pd.set_option("display.max_rows", 500)
pd.set_option("display.max_columns", 500)
pd.set_option("display.width", 1000)
from tqdm.auto import tqdm
import torch
import torch.nn as nn
import tokenizers
import transformers
print(f"tokenizers.__version__: {tokenizers.__version__}")
print(f"transformers.__version__: {transformers.__version__}")
print(f"torch.__version__: {torch.__version__}")
print(f"torch cuda version: {torch.version.cuda}")
from transformers import AutoTokenizer, AutoConfig
from transformers import BitsAndBytesConfig, AutoModelForCausalLM, MistralForCausalLM
from peft import LoraConfig, get_peft_model
title = "H2O AI Predict the LLM"
#Theme from - https://huggingface.co/spaces/trl-lib/stack-llama/blob/main/app.py
theme = gr.themes.Monochrome(
primary_hue="indigo",
secondary_hue="blue",
neutral_hue="slate",
radius_size=gr.themes.sizes.radius_sm,
font=[gr.themes.GoogleFont("Open Sans"), "ui-sans-serif", "system-ui", "sans-serif"],
)
def do_submit(question, response):
full_text = question + " " + response
# result = do_inference(full_text)
return "result"
with gr.Blocks(title=title) as demo: # theme=theme
sample_examples = pd.read_csv('sample_examples.csv')
example_list = sample_examples[['Question','Response','target']].sample(2).values.tolist()
gr.Markdown(f"## {title}")
with gr.Row():
# with gr.Column(scale=1):
# gr.Markdown("### Question and LLM Response")
question_text = gr.Textbox(lines=2, placeholder="Question:", label="")
response_text = gr.Textbox(lines=2, placeholder="Response:", label="")
target_text = gr.Textbox(lines=1, placeholder="Target:", label="", interactive=False , visible=False)
llm_num = gr.Textbox(value="", label="LLM #")
with gr.Row():
sub_btn = gr.Button("Submit")
sub_btn.click(fn=do_submit, inputs=[question_text, response_text], outputs=[llm_num])
gr.Markdown("## Sample Inputs:")
gr.Examples(
example_list,
[question_text,response_text,target_text],
# cache_examples=True,
)
demo.launch(debug=True) |