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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',
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) |