app.py

import argparse
import os
import requests

import gradio as gr

INTRO = """**Chat with Yoda, Albert Einstein, Elon Musk or Kanye West!**

✨ This demo is powered by HuggingFace Inference API and currently the models [starchat-beta](https://huggingface.co/HuggingFaceH4/starchat-beta) and [falcon-7b](https://huggingface.co/tiiuae/falcon-7b-instruct) are supported. This demo is based on the [falcon-chat demo](https://huggingface.co/spaces/HuggingFaceH4/falcon-chat) by the [HuggingFace H4 team](https://huggingface.co/HuggingFaceH4); major props to them!

🧪 With this demo you can talk to some of your favorite characters and also play with some very powerful models. Although not as powerful as some 40B+ models, the 7B Falcon model and 15.5B starchat-beta models are great chat companions. We intend to add more characters and models in the future.

👀 **Learn more about Falcon LLM:** [falconllm.tii.ae](https://falconllm.tii.ae/)

👀 **Learn more about Starchat LLM:** [starchat-alpha](https://huggingface.co/blog/starchat-alpha)

👀 **Banner images were created with [stable diffusion web](https://stablediffusionweb.com/).**

➡️️ **Intended Use**: this demo is intended to be a fun showcase of what one can do with HuggingFace Inference API and recent chat models.

⚠️ **Limitations**: the model can and will produce factually incorrect information, hallucinating facts and actions. As it has not undergone any advanced tuning/alignment, it can produce problematic outputs, especially if prompted to do so. Finally, this demo is limited to a session length of about 1,000 words.
"""
MODELS = [
    "HuggingFaceH4/starchat-beta",
    "tiiuae/falcon-7b-instruct",
]
HEADERS = {"Authorization": f"Bearer {os.environ['HUB_TOKEN']}"}
TITLE = """<h2 align="center">🚀 TalkToMe</h2>"""
USER_NAME = "User"
INSTRUCTIONS_MAPPING = {
    "Albert Einstein": "The following is a conversation between the highly knowledgeable and intelligent scientist Albert Einstein, and a human user, called User. In the following interactions, User and Albert Einstein will converse in natural language, and Albert Einstein will answer User's questions. Albert Einstein is always eloquent, respectful, polite and inclusive. Albert Einstein invented the theory of Relativity and made important contributions to the theory of Quantum Mechanics. Albert Einstein will never decline to answer a question, and always attempts to give an answer that User would be satisfied with. Albert Einstein knows a lot, and always tells the truth. The conversation begins.\n",
    "Yoda": "The following is a conversation between the highly knowledgeable and intelligent Yoda from Star Wars, and a human user, called User. In the following interactions, User and Yoda will converse in natural language, and Yoda will answer User's questions. Yoda is respectful, polite and inclusive. Yoda is a wise and powerful Jedi Master from the Star Wars universe who speaks as follows: `Speak you must, in his unique and distinctive manner, with wisdom and knowledge to share.`, `Reversed syntax and short phrases, you shall use.`, `May the Force be with you, young Padawan.`. The conversation begins.\n",
    "Elon Musk": "The following is a conversation between entrepeneur and multi-billionair Elon Musk, and a human user, called User. In the following interactions, User and Elon Musk will converse in natural language, and Elon Musk will answer User's questions. Elon Musk is self-centered, arrogant and has a great for business development. Elon Musk owns the electric car company Tesla, the spacecraft engeneering company SpaceX and bought the social media company Twitter. The conversation begins.\n",
    "Kanye West": "The following is a conversation between rapper Kanye West, and a human user, called User. In the following interactions, User and Kanye West will converse in natural language, and Kanye West will answer User's questions. Kanye West is self-centered, arrogant, a self-proclaimed genius and a great musician. Kanye West interrupted an award ceremony for Taylor Swift and ran for president of the united states. The conversation begins.\n",
}
RETRY_COMMAND = "/retry"
STOP_SEQ = [f"\n{USER_NAME}", "<|end|>"]

def run_model(prompt, model, temperature, top_p):
    try:
        api_url = f"https://api-inference.huggingface.co/models/{model}"
        payload = {
            "inputs": prompt,
            "parameters": {
                "max_new_tokens": 128,
                "do_sample": True,
                "temperature": temperature,
                "top_p": top_p
            }
        }
        response = requests.post(api_url, headers=HEADERS, json=payload)
        return response.json()[0]['generated_text']
    except:
        return "I'm sorry, the model is not available right now. Please try again later."

def get_stream(string: str):
    return enumerate(iter(string.split(" ")))

def parameter_accordion():
    with gr.Accordion("Parameters", open=False):
        model = gr.Radio(
            choices = MODELS,
            value = MODELS[0],
            interactive=True,
            label="Model",
        )
        temperature = gr.Slider(
            minimum=0.1,
            maximum=2.0,
            value=0.8,
            step=0.1,
            interactive=True,
            label="Temperature",
        )
        top_p = gr.Slider(
            minimum=0.1,
            maximum=0.99,
            value=0.9,
            step=0.01,
            interactive=True,
            label="p (nucleus sampling)",
        )
    return model, temperature, top_p


def format_chat_prompt(message: str, chat_history, bot_name: str) -> str:
    instructions = INSTRUCTIONS_MAPPING[bot_name].strip(" ").strip("\n")
    prompt = instructions
    for turn in chat_history:
        user_message, bot_message = turn
        prompt = f"{prompt}\n{USER_NAME}: {user_message}\n{bot_name}: {bot_message}"
    prompt = f"{prompt}\n{USER_NAME}: {message}\n{bot_name}:"
    return prompt


def chat():
    gr.HTML(TITLE)
    with gr.Row():
        with gr.Column():
            banner = gr.Image("Albert Einstein.jpeg", elem_id="banner-image", show_label=False)
        with gr.Column():
            gr.Markdown(INTRO)
                
    with gr.Row(elem_id="param_container"):
        with gr.Column():
            model, temperature, top_p = parameter_accordion()
        with gr.Column():
            with gr.Accordion("Character", open=True):
                choices = list(INSTRUCTIONS_MAPPING)
                bot_name = gr.Radio(
                    choices=choices,
                    value=choices[0],
                    interactive=True,
                    label="Character",
                )
                bot_name.change(fn=lambda value: gr.update(value=f"{value}.jpeg"), inputs=bot_name, outputs=banner)
                        
    with gr.Column(elem_id="chat_container"):
        with gr.Row():
            chatbot = gr.Chatbot(elem_id="chatbot")
        with gr.Row():
            inputs = gr.Textbox(
                placeholder=f"Hi there! Tell me something about yourself.",
                label="Type an input and press Enter",
                max_lines=3,
            )

    with gr.Row(elem_id="button_container"):
        with gr.Column():
            retry_button = gr.Button("♻️ Retry last turn")
        with gr.Column():
            delete_turn_button = gr.Button("🧽 Delete last turn")
        with gr.Column():
            clear_chat_button = gr.Button("✨ Delete all history")

    gr.Examples(
        [
            ["Hi Albert! Why did the apple fall from the tree?"],
            ["Hi Yoda! How do I learn the force?"],
            ["Hi Elon! Give me an idea for a new startup."],
            ["Hi Kanye! What will be the theme of your next album?"],
        ],
        inputs=inputs,
        label="Click on any example and press Enter in the input textbox!",
    )

    def run_chat(message: str, chat_history, bot_name: str, model: str, temperature: float, top_p: float):
        if not message or (message == RETRY_COMMAND and len(chat_history) == 0):
            yield chat_history
            return

        if message == RETRY_COMMAND and chat_history:
            prev_turn = chat_history.pop(-1)
            user_message, _ = prev_turn
            message = user_message

        prompt = format_chat_prompt(message, chat_history, bot_name)
        model_output = run_model(
            prompt,
            model=model,
            temperature=temperature,
            top_p=top_p,
        )
        model_output = model_output[len(prompt):]
        for stop in STOP_SEQ:
            model_output = model_output.split(stop)[0]
        chat_history = chat_history + [[message, model_output]]
        print(f"User: {message}")
        print(f"{bot_name}: {model_output}")
        yield chat_history
        return

    def delete_last_turn(chat_history):
        if chat_history:
            chat_history.pop(-1)
        return {chatbot: gr.update(value=chat_history)}

    def run_retry(message: str, chat_history, bot_name, model: str, temperature: float, top_p: float):
        yield from run_chat(RETRY_COMMAND, chat_history, bot_name, model, temperature, top_p)

    def clear_chat():
        return []

    inputs.submit(
        run_chat,
        [inputs, chatbot, bot_name, model, temperature, top_p],
        outputs=[chatbot],
        show_progress=False,
    )
    inputs.submit(lambda: "", inputs=None, outputs=inputs)
    delete_turn_button.click(delete_last_turn, inputs=[chatbot], outputs=[chatbot])
    retry_button.click(
        run_retry,
        [inputs, chatbot, bot_name, model, temperature, top_p],
        outputs=[chatbot],
        show_progress=False,
    )
    clear_chat_button.click(clear_chat, [], chatbot)


def get_demo():
    with gr.Blocks(
        # css=None
        # css="""#chat_container {width: 700px; margin-left: auto; margin-right: auto;}
        #        #button_container {width: 700px; margin-left: auto; margin-right: auto;}
        #        #param_container {width: 700px; margin-left: auto; margin-right: auto;}"""
        css="""#chatbot {
    font-size: 14px;
    min-height: 300px;
}"""
    ) as demo:
        chat()

    return demo


if __name__ == "__main__":
    demo = get_demo()
    demo.queue(max_size=128, concurrency_count=16)
    demo.launch()