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

device_map = {
    "transformer.word_embeddings": 0,
    "transformer.word_embeddings_layernorm": 0,
    "lm_head": "cpu",
    "transformer.h": 0,
    "transformer.ln_f": 0,
}

# config = PeftConfig.from_pretrained("/content/llama-2-7b-medichat")
model = AutoModelForCausalLM.from_pretrained("NousResearch/Llama-2-7b-chat-hf", return_dict=True, load_in_8bit=True, device_map=device_map)
tokenizer = AutoTokenizer.from_pretrained("NousResearch/Llama-2-7b-chat-hf")
model = PeftModel.from_pretrained(model, "maxspin/medichat")

import gradio as gr


iface.launch()

def query_handling(query, conversation):
  if "thanks" in query.lower() or "thank you" in query.lower() or "thank you very much" in query.lower():
    conversation=""
  return conversation

def process_response(input_string):
    # Find the indices of the first [INST] and last [/INST]
    start_index = input_string.find("[INST]")
    end_index = input_string.rfind("[/INST]")

    # If both [INST] and [/INST] are found
    if start_index != -1 and end_index != -1:
        # Extract the substring between [INST] and [/INST]
        inst_substring = input_string[start_index:end_index + len("[/INST]")]
        # Remove the extracted substring from the original string
        cleaned_string = input_string.replace(inst_substring, "")
    else:
        # If [INST] or [/INST] is not found, keep the original string
        cleaned_string = input_string

    # Remove the special characters <s> and </s>
    cleaned_string = cleaned_string.replace("<s>", "").replace("</s>", "").replace("[INST]","").replace("[/INST]","")

    return cleaned_string


conversation=""
def predict(prompt):
    global conversation
    conversation = conversation+f"[INST]{prompt}[/INST]"
    input_sequense = "<s>"+conversation
    batch = tokenizer(f"{input_sequense}", return_tensors='pt')
    batch = batch.to('cuda')
    with torch.cuda.amp.autocast():
      output_tokens = model.generate(**batch, max_new_tokens=4000)
      response =  tokenizer.decode(output_tokens[0], skip_special_tokens=True)
      print('\n\n', tokenizer.decode(output_tokens[0], skip_special_tokens=True))
      response = process_response(response)
      conversation+=response
      conversation = query_handling(prompt,conversation)
      print(conversation)
    return response

iface = gr.Interface(
    fn=predict,
    inputs="text",  # Accepts a single text input
    outputs="text"  # Outputs a single text response
)