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-2-7B-Chat-GGML",
    filename="llama-2-7b-chat.ggmlv3.q4_1.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()