from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from peft import PeftModel

model = AutoModelForCausalLM.from_pretrained("DAMO-NLP-MT/polylm-1.7b")
model = PeftModel.from_pretrained(model, "fadliaulawi/polylm-1.7b-finetuned")
tokenizer = AutoTokenizer.from_pretrained("DAMO-NLP-MT/polylm-1.7b", padding_side="left", use_fast = False)

def generate_prompt(
    instruction, input, label
):
#   template = {
#       "description": "Template used by Alpaca-LoRA.",
#       "prompt_input": "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n",
#       "prompt_no_input": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:\n",
#       "response_split": "### Response:"
#   }
# <s>[INST] <<SYS>>
# {{ system_prompt }}
# <</SYS>>

# {{ user_message }} [/INST]
#   return '''<s>[INST] <<SYS>>\n{0}\n<</SYS>>\n\n{1} {2} [/INST]'''.format(template['prompt_input'].format(instruction=instruction, input=input), template['response_split'], label)
    template = {
      "description": "Template used by Alpaca-LoRA.",
      "prompt_input": "Di bawah ini adalah instruksi yang menjelaskan tugas, dipasangkan dengan masukan yang memberikan konteks lebih lanjut. Tulis tanggapan yang melengkapi permintaan dengan tepat.\n\n### Instruksi:\n{instruction}\n\n### Masukan:\n{input}",
      #"prompt_no_input": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:\n",
      "response_split": "### Tanggapan:"
    }

    if input:
      res = template["prompt_input"].format(instruction=instruction, input=input)
    #else:
    #  res = template["prompt_no_input"].format(instruction=instruction)

    res = f"{res} \n\n### Tanggapan:\n"
    if label:
      res = f"{res}{label}"

    return res

def user(message, history):
    return "", history + [[message, None]]

def generate_and_tokenize_prompt(data_point):
    full_prompt = generate_prompt(
        data_point["instruction"],
        data_point["input"],
        data_point["output"],
    )
#     print(full_prompt)
#     return
    cutoff_len = 256
    tokenizer.pad_token = tokenizer.eos_token
    result = tokenizer(
        full_prompt,
        truncation=True,
        max_length=cutoff_len,
        padding=True,
        return_tensors=None,
    )

    if (result["input_ids"][-1] != tokenizer.eos_token_id and len(result["input_ids"]) < cutoff_len):
        result["input_ids"].append(tokenizer.eos_token_id)
        result["attention_mask"].append(1)

#     result["labels"] = result["input_ids"].copy()
    return result

def chatbot(history,temperature, max_new_tokens, top_p,top_k):
    user_message = history[-1][0]
    data = {
        'instruction': "Jika Anda seorang dokter, silakan menjawab pertanyaan medis berdasarkan deskripsi pasien.",
        'input': user_message,
        'output': ''
    }
    
    new_user_input_ids = generate_and_tokenize_prompt(data)

    # append the new user input tokens to the chat history
    bot_input_ids = torch.LongTensor([new_user_input_ids['input_ids']])

    # generate a response
    response = model.generate(
        input_ids=bot_input_ids,
        pad_token_id=tokenizer.eos_token_id,
        temperature = float(temperature),
        max_new_tokens=max_new_tokens,
        top_p=float(top_p),
        top_k=top_k,
        do_sample=True
    )

    # convert the tokens to text, and then split the responses into lines
    response = tokenizer.batch_decode(response, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
    #response = response.split("### Tanggapan:")
    #response = response[1].strip() if len(response) > 1 else ""
    sections = response.split("###")

    # Ambil potongan yang berisi "Tanggapan" yang pertama
    response = sections[3]

    #return response.split("Tanggapan:")[1].strip()
    history[-1] = response.split("Tanggapan:")[1].strip()
    return history

with gr.Blocks() as demo:
    temperature = gr.Slider(0, 5, value=0.8, step=0.1, label='Temperature')
    max_length = gr.Slider(0, 8192, value=256, step=1, label='Max Length')
    top_p = gr.Slider(0, 1, value=0.8, step=0.1, label='Top P')
    top_k = gr.Slider(0, 50, value=50, step=1, label='Top K')

    chatbot = gr.Chatbot()
    msg = gr.Textbox()
    submit = gr.Button("Submit")
    clear = gr.Button("Clear")

    examples = gr.Examples(examples=["Halo dokter", "Dokter aku flu, aku harus apa?"],inputs=[msg])

    #submit.click(bot,[msg,chatbot,temperature, max_length, top_p,top_k],chatbot)
    submit.click(user, [msg, chatbot], [msg, chatbot], queue=False).then(
        bot, [chatbot,temperature,max_length,top_p,top_k], chatbot
    )
    clear.click(lambda: None, None, chatbot, queue=False)

demo.launch()