from langchain.llms import CTransformers
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
import os
import io
import gradio as gr
import time

custom_prompt_template = """
You are an AI coding assistant and your task is to solve coding problems and 
return code snippets based on the user's query. Below is the user's query.
Query: {query}
You just return the helpful code and related details
Helpful code and related details:
"""

def set_custom_prompt():
    prompt = PromptTemplate(
        template = custom_prompt_template,
        input_variables = ['query']
    )
    return prompt

def load_model():
    llm = CTransformers(
        model = 'TheBloke/CodeLlama-7B-GGUF/codellama-7b.Q4_K_M.gguf',
        model_type = 'llama',
        max_new_tokens = 1096,
        temperature = 0.2,
        repetition_penalty = 1.13,
        gpu_layers = 2
    )
    return llm

def chain_pipeline():
    llm = load_model()
    qa_prompt = set_custom_prompt()
    qa_chain = LLMChain(
        prompt = qa_prompt,
        llm=llm
    )
    return qa_chain

llmcahin = chain_pipeline()

def bot(query):
    llm_response = llmcahin.run({"query":query})
    return llm_response

with gr.Blocks(title="code llama 7b") as demo:
    gr.Markdown("# Code llama")
    chatbot = gr.Chatbot([],elem_id="chatbot",height=700)
    msg = gr.Textbox()
    clear = gr.ClearButton([msg,chatbot])

    def respond(message, chat_history):
        bot_message = bot(message)
        chat_history.append((message, bot_message))
        time.sleep(2)
        return "",chat_history
    
    msg.submit(respond,[msg, chatbot],[msg, chatbot])

demo.launch(share=True)