import gradio as gr
import os
os.environ["HF_HUB_ENABLE_HF_TRANSFER"]="1"
from langchain.llms import LlamaCpp
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from huggingface_hub import hf_hub_download

callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])

repo_id="TheBloke/Mistral-7B-OpenOrca-GGUF"
model_name="mistral-7b-openorca.Q5_K_M.gguf"

hf_hub_download(repo_id=repo_id,
                filename=model_name,local_dir =".")


llm = LlamaCpp(
        model_path=model_name,
        n_ctx=4096,
        callback_manager=callback_manager, 
        verbose=True, # Verbose is required to pass to the callback manager
    )
def format_prompt(message, history):
  prompt = "<s>"
  for user_prompt, bot_response in history:
    prompt += f"<|im_start|>user\n {user_prompt} <|im_end|>\n"
    prompt += f"<|im_start|>assistant\n  {bot_response}<|im_end|>\n"
  prompt += f"<|im_start|>user\n {message} <|im_end|>\n<|im_start|>assistant\n"
  return prompt

def generate(
    prompt, history, temperature=0.9, top_p=0.95, max_new_tokens=256,repetition_penalty=1.0,
):
    
    temperature = float(temperature)
    if temperature < 1e-2:
        temperature = 1e-2
    top_p = float(top_p)
    
 
    formatted_prompt = format_prompt(prompt, history)
    

    # stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
    output = ""
    output=llm(formatted_prompt,
               temperature=temperature,
        max_tokens=max_new_tokens,
               repeat_penalty=repetition_penalty,
        top_p=top_p,
               stop=["<|im_end|>","<|im_start|>user"]
              )
    # output=formatted_prompt+"ans:"+output
    # for response in stream:
    #     output += response.token.text
    #     yield output
    return output


additional_inputs=[
    gr.Slider(
        label="Temperature",
        value=0.9,
        minimum=0.0,
        maximum=1.0,
        step=0.05,
        interactive=True,
        info="Higher values produce more diverse outputs",
    ),

    gr.Slider(
        label="Top-p (nucleus sampling)",
        value=0.90,
        minimum=0.0,
        maximum=1,
        step=0.05,
        interactive=True,
        info="Higher values sample more low-probability tokens",
    ),
    gr.Slider(
        label="Max new tokens",
        value=400,
        minimum=0,
        maximum=1048,
        step=64,
        interactive=True,
        info="The maximum numbers of new tokens",
    ),
        gr.Slider(
        label="Repetition penalty",
        value=1.2,
        minimum=1.0,
        maximum=2.0,
        step=0.05,
        interactive=True,
        info="Penalize repeated tokens",
    )

]

css = """
  #mkd {
    height: 500px; 
    overflow: auto; 
    border: 1px solid #ccc; 
  }
"""

with gr.Blocks(css=css) as demo:
    gr.HTML("<h1><center>Mistral 7B Instruct<h1><center>")
    gr.HTML("<h3><center>In this demo, you can chat with <a href='https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1'>Mistral-7B-Instruct</a> model. 💬<h3><center>")
    gr.HTML("<h3><center>Learn more about the model <a href='https://huggingface.co/docs/transformers/main/model_doc/mistral'>here</a>. 📚<h3><center>")
    gr.HTML(f"<h3><center>it's lamacpp running  {model_name} from {repo_id}<h3><center>")
    gr.ChatInterface(
        generate,
        additional_inputs=additional_inputs,
        examples=[["What is the secret to life?"], ["Write me a recipe for pancakes."]]
    )

demo.queue(max_size=None).launch(debug=True)