app.py

import datetime
import random
import os
import re
from io import StringIO

import gradio as gr
import pandas as pd

from huggingface_hub import Repository, upload_file
from text_generation import Client
from text_generation.errors import UnknownError
from share_btn import (community_icon_html, loading_icon_html, share_btn_css,
                       share_js)

HF_TOKEN = os.environ.get("HF_TOKEN", None)
API_TOKEN = os.environ.get("API_TOKEN", HF_TOKEN)

DIALOGUES_DATASET = "ArmelR/gradio_playground_dialogues"

API_URL_G = "https://api-inference.huggingface.co/models/ArmelR/starcoder-gradio-v0"
API_URL_S = "https://api-inference.huggingface.co/models/HuggingFaceH4/starcoderbase-finetuned-oasst1"
API_URL_B = "https://api-inference.huggingface.co/models/HuggingFaceH4/starchat-beta"

model2endpoint = {
    "starChat-alpha": API_URL_S,
    "starCoder-gradio": API_URL_G,
    "starChat-beta": API_URL_B
}

model_names = list(model2endpoint.keys())

with open("./HHH_prompt_short.txt", "r") as f:
    HHH_PROMPT = f.read() + "\n\n"

with open("./TA_prompt_v0.txt", "r") as f:
    TA_PROMPT = f.read()

NO_PROMPT = ""

def randomize_seed_generator():
    seed = random.randint(0, 1000000)
    return seed


def save_inputs_and_outputs(now, inputs, outputs, generate_kwargs, model):
    buffer = StringIO()
    timestamp = datetime.datetime.now().strftime("%Y-%m-%dT%H:%M:%S.%f")
    file_name = f"prompts_{timestamp}.jsonl"
    data = {"model": model, "inputs": inputs, "outputs": outputs, "generate_kwargs": generate_kwargs}
    pd.DataFrame([data]).to_json(buffer, orient="records", lines=True)

    # Push to Hub
    upload_file(
        path_in_repo=f"{now.date()}/{now.hour}/{file_name}",
        path_or_fileobj=buffer.getvalue().encode(),
        repo_id=DIALOGUES_DATASET,
        token=HF_TOKEN,
        repo_type="dataset",
    )

    # Clean and rerun
    buffer.close()

def get_total_inputs(inputs, chatbot, preprompt, user_name, assistant_name, sep):
    past = []
    for data in chatbot:
        user_data, model_data = data

        if not user_data.startswith(user_name):
            user_data = user_name + user_data
        if not model_data.startswith(sep + assistant_name):
            model_data = sep + assistant_name + model_data

        past.append(user_data + model_data.rstrip() + sep)

    if not inputs.startswith(user_name):
        inputs = user_name + inputs

    total_inputs = preprompt + "".join(past) + inputs + sep + assistant_name.rstrip()

    return total_inputs
    
def wrap_html_code(text):
    pattern = r"<.*?>"
    matches = re.findall(pattern, text)
    if len(matches) > 0:
        return f"```{text}```"
    else:
        return text

def has_no_history(chatbot, history):
    return not chatbot and not history

def get_inference_prompt(messages, model_name):
    if model_name == "starChat-beta" :
        prompt = "<|system|>\n<|endoftext|>\n"
        for message in messages :
            if message["role"] == "user" :
                prompt += f"<|user|>\n{message['content']}<|endoftext|>\n<|assistant|>\n"
            else : # message["role"] == "assistant"
                prompt += f"\n{message['content']}<|endoftext|>\n"
    elif model_name == "starChat-alpha" :
        prompt = "<|system|>\n<|end|>\n"
        for message in messages :
            if message["role"] == "user" :
                prompt += f"<|user|>\n{message['content']}<|end|>\n<|assistant|>\n"
            else : # message["role"] == "assistant"
                prompt += f"\n{message['content']}<|end|>\n"
    else : # starCoder-gradio
        prompt = ""
        for message in messages :
            if message["role"] == "user" :
                prompt += f"Question: {message['content']}\n\nAnswer:"
            else : # message["role"] == "assistant"
                prompt += f" {message['content']}\n\n"
    return prompt
    
def generate(
    RETRY_FLAG,
    model_name,
    system_message,
    user_message,
    chatbot,
    history,
    temperature,
    top_k,
    top_p,
    max_new_tokens,
    repetition_penalty,
    do_save=True,
):
    client = Client(
        model2endpoint[model_name],
        headers={"Authorization": f"Bearer {API_TOKEN}"},
        timeout=60,
    )
    # Don't return meaningless message when the input is empty
    if not user_message:
        print("Empty input")

    if not RETRY_FLAG:
        history.append(user_message)
        seed = 42
    else:
        seed = randomize_seed_generator()

    past_messages = []
    for data in chatbot:
        user_data, model_data = data

        past_messages.extend(
            [{"role": "user", "content": user_data}, {"role": "assistant", "content": model_data.rstrip()}]
        )

    if len(past_messages) < 1:
        prompt = get_inference_prompt(messages=[{"role": "user", "content": user_message}], model_name=model_name)
    else:
        prompt = get_inference_prompt(messages=past_messages + [{"role": "user", "content": user_message}], model_name=model_name)

    generate_kwargs = {
        "temperature": temperature,
        "top_k": top_k,
        "top_p": top_p,
        "max_new_tokens": max_new_tokens,
    }

    temperature = float(temperature)
    if temperature < 1e-2:
        temperature = 1e-2
    top_p = float(top_p)

    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        truncate=4096,
        seed=seed,
        stop_sequences=["<|end|>", "Question:"],
    )

    try :
        stream = client.generate_stream(
            prompt,
            **generate_kwargs,
        )
    
        output = ""
        for idx, response in enumerate(stream):
            if response.token.special:
                continue
            output += response.token.text
            if idx == 0:
                history.append(" " + output)
            else:
                history[-1] = output
    
            chat = [
                (wrap_html_code(history[i].strip()), wrap_html_code(history[i + 1].strip()))
                for i in range(0, len(history) - 1, 2)
            ]
    
            # chat = [(history[i].strip(), history[i + 1].strip()) for i in range(0, len(history) - 1, 2)]
    
            yield chat, history, user_message, ""
    
        if HF_TOKEN and do_save:
            try:
                now = datetime.datetime.now()
                current_time = now.strftime("%Y-%m-%d %H:%M:%S")
                print(f"[{current_time}] Pushing prompt and completion to the Hub")
                save_inputs_and_outputs(now, prompt, output, generate_kwargs, model_name)
            except Exception as e:
                print(e)
    
        return chat, history, user_message, ""
    except UnknownError :
        error_message = "The model is currently loading. Please wait a few seconds and retry."
        return [(user_message, error_message)], history, user_message, ""

examples = [
    "Use the gradio library to create a calculator. It should take into account the 4 basic operations. A user should be able to enter 2 numbers, choose an operation and get the corresponding result.",
    "Write a gradio application to convert an input temperature in celcius to a temperature in fahrenheit",
    "Write a gradio application for a chatbot. The chatbot should use a language model loaded with Hugging face’s transformers , and a user should be able to enter a text request to get the corresponding answer.",
    "Create a basic gradio application.",
    "What is gradio?"
]

def clear_chat():
    return [], []

def delete_last_turn(chat, history):
    if chat and history:
        chat.pop(-1)
        history.pop(-1)
        history.pop(-1)
    return chat, history

def process_example(args):
    for [x, y] in generate(args):
        pass
    return [x, y]

# Regenerate response
def retry_last_answer(
    selected_model,
    system_message,
    user_message,
    chat,
    history,
    temperature,
    top_k,
    top_p,
    max_new_tokens,
    repetition_penalty,
    do_save,
):
    if chat and history:
        # Removing the previous conversation from chat
        chat.pop(-1)
        # Removing bot response from the history
        history.pop(-1)
        # Setting up a flag to capture a retry
        RETRY_FLAG = True
        # Getting last message from user
        user_message = history[-1]

    yield from generate(
        RETRY_FLAG,
        selected_model,
        system_message,
        user_message,
        chat,
        history,
        temperature,
        top_k,
        top_p,
        max_new_tokens,
        repetition_penalty,
        do_save,
    )

title = """<h1 align="center">Chat with Gradio 💫➕</h1>"""
custom_css = """
#banner-image {
    display: block;
    margin-left: auto;
    margin-right: auto;
}
#chat-message {
    font-size: 14px;
    min-height: 300px;
}
"""

with gr.Blocks(analytics_enabled=False, css=custom_css) as demo:
    #gr.HTML(title)

    with gr.Row():
        #with gr.Column():
            #gr.Image("gradio.png", elem_id="banner-image", show_label=False)
        
        #with gr.Column():
        #    gr.Markdown(
        #        """
        #    💻 This demo showcases a series of **[StarChat](https://huggingface.co/models?search=huggingfaceh4/starchat)** language models, which are fine-tuned versions of the StarCoder family to act as helpful coding assistants.  The base model has 16B parameters and was pretrained on one trillion tokens sourced from 80+ programming languages, GitHub issues, Git commits, and Jupyter notebooks (all permissively licensed).
        #    📝 For more details, check out our [blog post](https://huggingface.co/blog/starchat-alpha).
        #    ⚠️ **Intended Use**: this app and its [supporting models](https://huggingface.co/models?search=huggingfaceh4/starchat) are provided as educational tools to explain large language model fine-tuning; not to serve as replacement for human expertise.
        #    ⚠️ **Known Failure Modes**: the alpha and beta version of **StarChat** have not been aligned to human preferences with techniques like RLHF, so they can produce problematic outputs (especially when prompted to do so). Since the base model was pretrained on a large corpus of code, it may produce code snippets that are syntactically valid but semantically incorrect.  For example, it may produce code that does not compile or that produces incorrect results.  It may also produce code that is vulnerable to security exploits.  We have observed the model also has a tendency to produce false URLs which should be carefully inspected before clicking. For more details on the model's limitations in terms of factuality and biases, see the [model card](https://huggingface.co/HuggingFaceH4/starchat-alpha#bias-risks-and-limitations).
        #    ⚠️ **Data Collection**: by default, we are collecting the prompts entered in this app to further improve and evaluate the models. Do **NOT** share any personal or sensitive information while using the app! You can opt out of this data collection by removing the checkbox below.
        #        """
        #    )
        with gr.Column():
            gr.Markdown("""<img src="https://huggingface.co/spaces/codeparrot/gradio-playground/resolve/main/chat_with_gradio.png" height="200">""")
    with gr.Row():
        selected_model = gr.Radio(choices=model_names, value=model_names[1], label="Select a model")

    with gr.Accordion(label="System Prompt", open=False, elem_id="parameters-accordion"):
        system_message = gr.Textbox(
            elem_id="system-message",
            placeholder="Below is a conversation between a human user and a helpful AI coding assistant.",
            show_label=False,
        )
    with gr.Row():
        with gr.Box():
            output = gr.Markdown()
            chatbot = gr.Chatbot(elem_id="chat-message", label="Chat")

    with gr.Row():
        with gr.Column(scale=3):
            user_message = gr.Textbox(placeholder="Enter your message here", show_label=False, elem_id="q-input")
            with gr.Row():
                send_button = gr.Button("Send", elem_id="send-btn", visible=True)

                regenerate_button = gr.Button("Regenerate", elem_id="retry-btn", visible=True)

                delete_turn_button = gr.Button("Delete last turn", elem_id="delete-btn", visible=True)

                clear_chat_button = gr.Button("Clear chat", elem_id="clear-btn", visible=True)

            with gr.Accordion(label="Parameters", open=False, elem_id="parameters-accordion"):
                temperature = gr.Slider(
                    label="Temperature",
                    value=0.2,
                    minimum=0.0,
                    maximum=1.0,
                    step=0.1,
                    interactive=True,
                    info="Higher values produce more diverse outputs",
                )
                top_k = gr.Slider(
                    label="Top-k",
                    value=50,
                    minimum=0.0,
                    maximum=100,
                    step=1,
                    interactive=True,
                    info="Sample from a shortlist of top-k tokens",
                )
                top_p = gr.Slider(
                    label="Top-p (nucleus sampling)",
                    value=0.95,
                    minimum=0.0,
                    maximum=1,
                    step=0.05,
                    interactive=True,
                    info="Higher values sample more low-probability tokens",
                )
                max_new_tokens = gr.Slider(
                    label="Max new tokens",
                    value=512,
                    minimum=0,
                    maximum=1024,
                    step=4,
                    interactive=True,
                    info="The maximum numbers of new tokens",
                )
                repetition_penalty = gr.Slider(
                    label="Repetition Penalty",
                    value=1.2,
                    minimum=0.0,
                    maximum=10,
                    step=0.1,
                    interactive=True,
                    info="The parameter for repetition penalty. 1.0 means no penalty.",
                )
            with gr.Row():
                do_save = gr.Checkbox(
                    value=True,
                    label="Store data",
                    info="You agree to the storage of your prompt and generated text for research and development purposes:",
                )
            # with gr.Group(elem_id="share-btn-container"):
            #     community_icon = gr.HTML(community_icon_html, visible=True)
            #     loading_icon = gr.HTML(loading_icon_html, visible=True)
            # share_button = gr.Button("Share to community", elem_id="share-btn", visible=True)
            with gr.Row():
                gr.Examples(
                    examples=examples,
                    inputs=[user_message],
                    cache_examples=False,
                    fn=process_example,
                    outputs=[output],
                )
            with gr.Row():
                gr.Markdown(
                    """Chat-with-Gradio is a 15.5 billion parameter language model based on [BigCode's StarCoderplus model](https://huggingface.co/bigcode/starcoderplus) that has been trained on a wide variety of data sources. It includes the source code and issues from [gradio's Github repository](https://github.com/gradio-app/gradio) and data from [Hugging Face's spaces](https://huggingface.co/spaces). Its training also involves instruction fine-tuning with a processed subset of [OpenAssistant's oasst1 dataset](https://huggingface.co/datasets/HuggingFaceH4/oasst1_en).
                
                Type in the box below and click the button to generate answers to your most pressing questions!
                ⚠️ **Intended Use**: this app and its [supporting model](https://huggingface.co/bigcode/starcoderplus) are provided as tools to provide assistance when using gradio ; not to serve as replacement for human expertise. For more details on the model's limitations in terms of factuality and biases, see the [model card.](https://huggingface.co/bigcode/starcoderplus#intended-uses--limitations)
                
                ⚠️ **Data Collection**: by default, we are collecting the prompts entered in this app to further improve and evaluate the model. Do not share any personal or sensitive information while using the app! You can opt out of this data collection by removing the checkbox below:)""")

    history = gr.State([])
    RETRY_FLAG = gr.Checkbox(value=False, visible=False)

    # To clear out "message" input textbox and use this to regenerate message
    last_user_message = gr.State("")

    user_message.submit(
        generate,
        inputs=[
            RETRY_FLAG,
            selected_model,
            system_message,
            user_message,
            chatbot,
            history,
            temperature,
            top_k,
            top_p,
            max_new_tokens,
            repetition_penalty,
            do_save,
        ],
        outputs=[chatbot, history, last_user_message, user_message],
    )

    send_button.click(
        generate,
        inputs=[
            RETRY_FLAG,
            selected_model,
            system_message,
            user_message,
            chatbot,
            history,
            temperature,
            top_k,
            top_p,
            max_new_tokens,
            repetition_penalty,
            do_save,
        ],
        outputs=[chatbot, history, last_user_message, user_message],
    )

    regenerate_button.click(
        retry_last_answer,
        inputs=[
            selected_model,
            system_message,
            user_message,
            chatbot,
            history,
            temperature,
            top_k,
            top_p,
            max_new_tokens,
            repetition_penalty,
            do_save,
        ],
        outputs=[chatbot, history, last_user_message, user_message],
    )

    delete_turn_button.click(delete_last_turn, [chatbot, history], [chatbot, history])
    clear_chat_button.click(clear_chat, outputs=[chatbot, history])
    selected_model.change(clear_chat, outputs=[chatbot, history])
    # share_button.click(None, [], [], _js=share_js)

demo.queue(concurrency_count=16).launch(debug=True)