app.py

import gradio as gr
import copy
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/Llama-2-70B-GGML", filename="llama-2-70b.ggmlv3.q4_K_M.bin"), n_ctx=2048) #download model from hf/ n_ctx=2048 for high ccontext length

history = []

pre_prompt = " The user and the AI are having a conversation : <|endoftext|> \n "

def generate_text(input_text, history):
    print("history ",history)
    print("input ", input_text)
    temp =""
    if history == []:
        input_text_with_history = f"SYSTEM:{pre_prompt}"+ "\n" + f"USER: {input_text} " + "\n" +" ASSISTANT:"
    else:
        input_text_with_history = f"{history[-1][1]}"+ "\n"
        input_text_with_history += f"USER: {input_text}" + "\n" +" ASSISTANT:"
    print("new input", input_text_with_history)
    output = llm(input_text_with_history, max_tokens=1024, stop=["<|prompter|>", "<|endoftext|>", "<|endoftext|> \n","ASSISTANT:","USER:","SYSTEM:"], stream=True)
    for out in output:
     stream = copy.deepcopy(out)
     print(stream["choices"][0]["text"])
     temp += stream["choices"][0]["text"]
     yield temp


    history =["init",input_text_with_history]
        


demo = gr.ChatInterface(generate_text,
    title="LLM on CPU",
    description="Running LLM with https://github.com/abetlen/llama-cpp-python. btw the text streaming thing was the hardest thing to impliment",
    examples=["Hello", "Am I cool?", "Are tomatoes vegetables?"],
    cache_examples=True,
    retry_btn=None,
    undo_btn="Delete Previous",
    clear_btn="Clear",)
demo.queue(concurrency_count=1, max_size=5)
demo.launch()