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import os
import requests
# from IPython.display import Markdown, display, update_display
# from openai import OpenAI
# from google.colab import drive
from huggingface_hub import login
# from google.colab import userdata
from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer, BitsAndBytesConfig
import torch
from dotenv import load_dotenv
import gradio as gr
load_dotenv()
LLAMA = "meta-llama/Meta-Llama-3.1-8B-Instruct"
hf_token = os.environ["HF_TOKEN"]
login(hf_token, add_to_git_credential=True)
system_message = "You are an assistant that produces datasets based on description provided."
user_input = "Film critics of 1900s"
user_prompt = f"Below is the description for which you need to generate dataset.\n{user_input}"
messages = [
{"role": "system", "content": system_message},
{"role": "user", "content": user_prompt}
]
quant_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_quant_type="nf4"
)
tokenizer = None
model = None
streamer = None
def run_llama(python):
if tokenizer is None:
tokenizer = AutoTokenizer.from_pretrained(LLAMA)
tokenizer.pad_token = tokenizer.eos_token
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
# streamer = TextStreamer(tokenizer)
if model is None:
model = AutoModelForCausalLM.from_pretrained(LLAMA, device_map="auto", quantization_config=quant_config)
outputs = model.generate(inputs, max_new_tokens=2000, streamer=streamer)
response = tokenizer.decode(outputs[0])
return response
# Gradio Interface
iface = gr.Interface(
fn=run_llama,
inputs=gr.Textbox(label="Enter dataset description"),
outputs=gr.Markdown(label="Generated Dataset"),
title="Dataset Generator",
description="Describe the dataset you want to generate."
)
iface.launch(share=True, debug=True)
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