import gradio as gr
import time
import ctypes #to run on C api directly 
import llama_cpp
from llama_cpp import Llama
from huggingface_hub import hf_hub_download #load from huggingfaces 


llm = Llama(model_path= hf_hub_download(repo_id="TheBloke/airoboros-l2-13b-gpt4-m2.0-GGML", filename="airoboros-l2-13b-gpt4-m2.0.ggmlv3.q6_K.bin"), n_ctx=2048) #download model from hf/ n_ctx=2048 for high ccontext length

history = []

def generate_text(input_text, history):
    
    conversation_context = " ".join([f"{pair[0]} {pair[1]}" for pair in history])
    full_conversation = f"{conversation_context} Q: {input_text} \n A:"
    
    output = llm(full_conversation, max_tokens=1024, stop=["Q:", "\n"], echo=True)
    response = output['choices'][0]['text']
    history.append([input_text, response])
    
    return "", history

with gr.Blocks() as demo:
    chatbot = gr.Chatbot()
    msg = gr.Textbox()
    clear = gr.ClearButton([msg, chatbot])

     msg.submit(
        # fn=conversation.user_turn,
        fn=generate_text,
        inputs=[msg, chatbot],
        outputs=[msg, chatbot],
        # queue=True,
        show_progress="full",
        api_name="predict",
    )
    submit.click(
        fn=lambda x, y: ("",) + predict(x, y)[1:],  # clear msg
        inputs=[msg, chatbot],
        outputs=[msg, chatbot],
        queue=True,
        show_progress="full",
    )
    clear.click(lambda: None, None, chatbot, queue=False)
    

demo.queue(concurrency_count=1, max_size=5)
demo.launch()