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import gradio as gr | |
from huggingface_hub import InferenceClient | |
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") | |
def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p, backend): | |
# Force the AI to generate a website with index.html and specified backend | |
forced_instruction = f""" | |
You must generate a complete website structure including at least an index.html. | |
Use the following backend structure: {backend}. | |
Only use {backend} relevant code and structure, and don't include any other type. | |
""" | |
system_message = forced_instruction + "\n\n" + system_message | |
messages = [{"role": "system", "content": system_message}] | |
for val in history: | |
if val[0]: | |
messages.append({"role": "user", "content": val[0]}) | |
if val[1]: | |
messages.append({"role": "assistant", "content": val[1]}) | |
messages.append({"role": "user", "content": message}) | |
response = "" | |
for message in client.chat_completion( | |
messages, | |
max_tokens=max_tokens, | |
stream=True, | |
temperature=temperature, | |
top_p=top_p, | |
): | |
token = message.choices[0].delta.content | |
response += token | |
yield response | |
demo = gr.ChatInterface( | |
fn=respond, | |
additional_inputs=[ | |
gr.Textbox(value="You are a helpful assistant.", label="System message"), | |
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), | |
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), | |
gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"), | |
gr.Dropdown(choices=["Flask", "Static", "Node.js"], value="Static", label="Website Backend"), | |
], | |
title="WebGen AI", | |
description="Ask the AI to build a website with a specific backend (Flask, Static, or Node.js). It will always include index.html.", | |
) | |
if __name__ == "__main__": | |
demo.launch() |