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)