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import gradio as gr
from transformers import AutoTokenizer
import json
import os
from huggingface_hub import login
HUGGINGFACEHUB_API_TOKEN = os.environ.get("HF_TOKEN")
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?"}
]"""
description_text = """# Chat Template Viewer
### This space is a helper to learn more about [Chat Templates](https://huggingface.co/docs/transformers/main/en/chat_templating).
"""
default_tools = [{"type": "function", "function": {"name":"get_current_weather", "description": "Get▁the▁current▁weather", "parameters": {"type": "object", "properties": {"location": {"type": "string", "description": "The city and state, e.g. San Francisco, CA"}, "format": {"type": "string", "enum": ["celsius", "fahrenheit"], "description": "The temperature unit to use. Infer this from the users location."}},"required":["location","format"]}}}]
# render the tool use prompt as a string:
def get_template_names(model_name):
try:
tokenizer = AutoTokenizer.from_pretrained(model_name)
if isinstance(tokenizer.chat_template, dict):
return list(tokenizer.chat_template.keys())
else:
return []
except Exception as e:
return "None"
def update_template_dropdown(model_name):
template_names = get_template_names(model_name)
if template_names:
return gr.update(choices=template_names, value=None)
def apply_chat_template(model_name, test_conversation, add_generation_prompt, cleanup_whitespace, template_name, hf_token, kwargs):
try:
login(token=hf_token)
tokenizer = AutoTokenizer.from_pretrained(model_name)
except:
return f"model {model_name} could not be loaded or invalid HF token"
try:
outputs = []
conversation = json.loads(test_conversation)
template = tokenizer.chat_template.get(template_name) if template_name else None
print(kwargs)
formatted = tokenizer.apply_chat_template(conversation, chat_template=template, tokenize=False, add_generation_prompt=add_generation_prompt, tools=default_tools)
return formatted
except Exception as e:
return str(e)
with gr.Blocks() as demo:
model_name_input = gr.Textbox(label="Model Name", placeholder="Enter model name")
template_dropdown = gr.Dropdown(label="Template Name", choices=[], interactive=True)
conversation_input = gr.TextArea(value=demo_conversation, lines=6, label="Conversation")
add_generation_prompt_checkbox = gr.Checkbox(value=False, label="Add generation prompt")
cleanup_whitespace_checkbox = gr.Checkbox(value=True, label="Cleanup template whitespace")
hf_token_input = gr.Textbox(label="Hugging Face Token (optional)", placeholder="Enter your HF token")
kwargs_input = gr.JSON(label="Additional kwargs", value=default_tools, render=False)
output = gr.TextArea(label="Formatted conversation")
model_name_input.change(fn=update_template_dropdown, inputs=model_name_input, outputs=template_dropdown)
gr.Interface(
description=description_text,
fn=apply_chat_template,
inputs=[
model_name_input,
conversation_input,
add_generation_prompt_checkbox,
cleanup_whitespace_checkbox,
template_dropdown,
hf_token_input,
kwargs_input
],
outputs=output
)
demo.launch() |