app.py

import gradio as gr
from transformers import AutoTokenizer
import json

tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")

demo_conversation = """[
    {"role": "system", "content": "You are a helpful chatbot."},
    {"role": "user", "content": "Hi there!"},
    {"role": "assistant", "content": "Hello, human!"},
    {"role": "user", "content": "Can I ask a question?"}
]"""

default_template = """{% for message in messages %}
    {{ "<|im_start|>" + message["role"] + "\\n" + message["content"] + "<|im_end|>\\n" }}
{% endfor %}
{% if add_generation_prompt %}
    {{ "<|im_start|>assistant\\n" }}
{% endif %}"""

description_text = """### This space is a helper app for writing [Chat Templates](https://huggingface.co/docs/transformers/main/en/chat_templating).

### When you're happy with the outputs from your template, you can use the code block at the end to add it to a PR!"""

def apply_chat_template(template, test_conversation, add_generation_prompt, cleanup_whitespace):
    if cleanup_whitespace:
        template = "".join([line.strip() for line in template.split('\n')])
    tokenizer.chat_template = template
    outputs = []
    conversation = json.loads(test_conversation)
    return tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=add_generation_prompt)

iface = gr.Interface(
    description=description_text,
    fn=apply_chat_template,
    inputs=[
        gr.TextArea(value=default_template, lines=10, max_lines=30, label="Chat Template"),
        gr.TextArea(value=demo_conversation, lines=6, label="Conversation"),
        gr.Checkbox(value=False, label="Add generation prompt"),
        gr.Checkbox(value=True, label="Cleanup template whitespace"),
    ],
    outputs=gr.TextArea(label="Formatted conversation"))
iface.launch()