import gradio as gr
import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer

def greet(name):
    peft_model_id = "sachit-sankhe/openllama7b-lora-adapter2"
    config = PeftConfig.from_pretrained(peft_model_id)
    loaded_model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='auto')
    tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
    # Load the Lora model
    loaded_model = PeftModel.from_pretrained(loaded_model, peft_model_id)
    input_prompt = name
    batch = tokenizer(f"###Human: {input_prompt}### Assistant: ", return_tensors='pt')

    with torch.cuda.amp.autocast():
      output_tokens = loaded_model.generate(**batch,max_new_tokens=300)

    return str('\n\n', tokenizer.decode(output_tokens[0], skip_special_tokens=True))


iface = gr.Interface(fn=greet, inputs="text", outputs="text")
iface.launch(share=True)