app.py

import os 
from typing import Iterator
import gradio as gr 
from text_generation import Client  # Assuming you have a text_generation module
from transformers import AutoModel

# Set Hugging Face API token from environment variable
HF_TOKEN = os.environ.get('chatbot', False)

if not HF_TOKEN:
    raise ValueError("Hugging Face API token is not set. Set the HF_READ_TOKEN environment variable.")

EOS_STRING = '</s>'
EOT_STRING = '<EOT>'

# Load the private model with access token
access_token = os.environ.get('chatbot', False)
if not access_token:
    raise ValueError("Hugging Face model access token is not set. Set the HF_MODEL_ACCESS_TOKEN environment variable.")

# Set protected namespaces to resolve the warning
model_config = AutoModel.config
model_config['protected_namespaces'] = ()

model = AutoModel.from_pretrained("private/model", token=access_token)

def get_prompt(message, chat_history, system_prompt):
    texts = [f'<s>[INST] <<SYS>>\n{system_prompt}\n<</SYS>>\n\n']

    do_strip = False
    for user_input, response in chat_history:
        user_input = user_input.strip() if do_strip else user_input
        do_strip = True
        texts.append(f"{user_input} [/INST\ {response.strip()} </s><s>[INST] ")
    message = message.strip() if do_strip else message
    texts.append(f"{message} [/INST]")
    return ''.join(texts)

def run(model_id, message, chat_history, system_prompt, max_new_tokens=1024, temperature=0.3, top_p=0.9, top_k=50):
    API_URL = "https://api-inference.huggingface.co/models/" + model_id
    client = Client(API_URL, headers={'Authorization': f"Bearer {HF_TOKEN}"})
    prompt = get_prompt(message, chat_history, system_prompt)
    generate_kwargs = dict(
        max_new_tokens=max_new_tokens,
        do_sample=True,
        top_p=top_p,
        top_k=top_k,
        temperature=temperature
        
    )
    stream = client.generate_stream(prompt, **generate_kwargs)
    output = ''
    for response in stream:
        if any([end_token in response.token.text for end_token in [EOS_STRING, EOT_STRING]]):
            return output
        else:
            output += response.token.text
        yield output
    return output

DEFAULT_SYSTEM_PROMPT = """
    You are Jarvis. You are an AI assistant, you are moderately-polite and give only true information.
    You carefully provide accurate, factual, thoughtful, nuanced answers, and are brilliant at reasoning. 
    If you think there might not be a correct answer, you say so. Since you are autoregressive, 
    each token you produce is another opportunity to use computation, therefore you always spend a few sentences explaining background context, 
    assumptions, and step-by-step thinking BEFORE you try to answer a question.
"""
MAX_MAX_NEW_TOKENS = 10240
DEFAULT_MAX_NEW_TOKENS = 4096
MAX_INPUT_TOKEN_LENGTH = 4000

DESCRIPTION = "# <h1>He's just Jarvis. ;)</h1>"

def clear_and_save_textbox(message): return '', message

def display_input(message, history=[]):
    history.append((message, ''))
    return history

def delete_prev_fn(history=[]):
    try:
        message, _ = history.pop()
    except IndexError:
        message = ''
    return history, message or ''

def generate(model_id, message, history_with_input, system_prompt, max_new_tokens, temperature, top_p, top_k):
    if max_new_tokens > MAX_MAX_NEW_TOKENS:
        raise ValueError

    history = history_with_input[:-1]
    generator = run(model_id, message, history, system_prompt, max_new_tokens, temperature, top_p, top_k)

    try:
        first_response = next(generator)
        yield history + [(message, first_response)]
    except StopIteration:
        yield history + [(message, '')]
    for response in generator:
        yield history + [(message, response)]

def process_example(model_id, message):
    generator = generate(model_id, message, [], DEFAULT_SYSTEM_PROMPT, 1024, 1, 0.95, 50)
    for x in generator:
        pass
    return '', x

def check_input_token_length(message, chat_history, system_prompt):
    input_token_length = len(message) + len(chat_history)
    if input_token_length > MAX_INPUT_TOKEN_LENGTH:
        raise gr.Error(f"The accumulated input is too long ({input_token_length} > {MAX_INPUT_TOKEN_LENGTH}). Client your chat history and try again.")

with gr.Blocks(theme='JohnSmith9982/small_and_pretty') as demo:
    gr.Markdown(DESCRIPTION)
    with gr.Group():
        chatbot = gr.Chatbot(label='Jarvis')
        with gr.Row():
            textbox = gr.Textbox(container=False, show_label=False, placeholder='Hey, Jarvis', scale=7)
            model_id = gr.Dropdown(label='LLM',
                                   choices=[
                                       'mistralai/Mistral-7B-Instruct-v0.1', 
                                       'HuggingFaceH4/zephyr-7b-beta', 
                                       'meta-llama/Llama-2-7b-chat-hf'
                                   ],
                                  value='mistralai/Mistral-7B-Instruct-v0.1', scale=3)
            submit_button = gr.Button('Submit', variant='primary', scale=1, min_width=0)

        with gr.Row():
            retry_button = gr.Button('Retry', variant='secondary')
            undo_button = gr.Button('Undo', variant='secondary')
            clear_button = gr.Button('Clear', variant='secondary')

        saved_input = gr.State()

        with gr.Accordion(label='Advanced Options', open=False):
            system_prompt = gr.Textbox(label='System prompt', value=DEFAULT_SYSTEM_PROMPT, lines=5, interactive=False)
            max_new_tokens = gr.Slider(label='Max New Tokens', minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS)
            temperature = gr.Slider(label='Temperatur', minimum=0.1, maximum=4.0, step=0.1, value=0.1)
            top_p = gr.Slider(label='Top-P (nucleus sampling)', minimum=0.05, maximum=1.0, step=0.05, value=0.9)
            top_k = gr.Slider(label='Top-K', minimum=1, maximum=1000, step=1, value=10)

    textbox.submit(
        fn=clear_and_save_textbox,
        inputs=textbox,
        outputs=[textbox, saved_input],
        api_name=False,
        queue=False,
    ).then(
        fn=display_input,
        inputs=[saved_input, chatbot],
        outputs=chatbot,
        api_name=False,
        queue=False,
    ).then(
        fn=check_input_token_length,
        inputs=[saved_input, chatbot, system_prompt],
        api_name=False,
        queue=False,
    ).success(
        fn=generate,
        inputs=[
            model_id,
            saved_input,
            chatbot,
            system_prompt,
            max_new_tokens,
            temperature,
            top_p,
            top_k,
        ],
        outputs=chatbot,
        api_name=False,
    )

    button_event_preprocess = submit_button.click(
        fn=clear_and_save_textbox,
        inputs=textbox,
        outputs=[textbox, saved_input],
        api_name=False,
        queue=False,
    ).then(
        fn=display_input,
        inputs=[saved_input, chatbot],
        outputs=chatbot,
        api_name=False,
        queue=False,
    ).then(
        fn=check_input_token_length,
        inputs=[saved_input, chatbot, system_prompt],
        api_name=False,
        queue=False,
    ).success(
        fn=generate,
        inputs=[
            model_id,
            saved_input,
            chatbot,
            system_prompt,
            max_new_tokens,
            temperature,
            top_p,
            top_k,
        ],
        outputs=chatbot,
        api_name=False,
    )

    retry_button.click(
        fn=delete_prev_fn,
        inputs=chatbot,
        outputs=[chatbot, saved_input],
        api_name=False,
        queue=False,
    ).then(
        fn=display_input,
        inputs=[saved_input, chatbot],
        outputs=chatbot,
        api_name=False,
        queue=False,
    ).then(
        fn=generate,
        inputs=[
            model_id,
            saved_input,
            chatbot,
            system_prompt,
            max_new_tokens,
            temperature,
            top_p,
            top_k,
        ],
        outputs=chatbot,
        api_name=False,
    )

    undo_button.click(
        fn=delete_prev_fn,
        inputs=chatbot,
        outputs=[chatbot, saved_input],
        api_name=False,
        queue=False,
    ).then(
        fn=lambda x: x,
        inputs=[saved_input],
        outputs=textbox,
        api_name=False,
        queue=False,
    )

    clear_button.click(
        fn=lambda: ([], ''),
        outputs=[chatbot, saved_input],
        queue=False,
        api_name=False,
    )

demo.queue(max_size=32).launch(show_api=False)