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import gradio as gr
from huggingface_hub import InferenceClient
# Define available models.
# Each model has a name, description, model ID, and an "enabled" flag.
models = [
{
"name": "Tiny Model",
"description": "A small chat model.",
"id": "amusktweewt/tiny-model-500M-chat-v2",
"enabled": True
},
{
"name": "Another Model",
"description": "A bigger chat model (disabled).",
"id": "another-model",
"enabled": False
}
]
# Build the HTML for the custom dropdown.
dropdown_options = ""
for model in models:
# If a model is disabled, add the "disabled" attribute and modify its label.
disabled_attr = "disabled" if not model["enabled"] else ""
label = f"{model['name']}: {model['description']}"
if not model["enabled"]:
label = f"{model['name']} (Disabled): {model['description']}"
dropdown_options += f'<option value="{model["id"]}" {disabled_attr}>{label}</option>\n'
# This HTML dropdown will be displayed. When the user selects an option,
# an inline JavaScript updates the value of the hidden textbox with the chosen model ID.
dropdown_html = f"""
<div>
<label for="model_select"><strong>Select Model:</strong></label>
<select id="model_select" onchange="document.getElementById('hidden_model').value = this.value;">
{dropdown_options}
</select>
</div>
"""
# The respond function now accepts the model_id as one of its inputs.
def respond(message, history: list[tuple[str, str]], model_id, system_message, max_tokens, temperature, top_p):
# Instantiate the InferenceClient with the selected model.
client = InferenceClient(model_id)
messages = []
if system_message:
messages.append({"role": "system", "content": system_message})
if history:
for user_msg, bot_msg in history:
messages.append({"role": "user", "content": user_msg})
messages.append({"role": "assistant", "content": bot_msg})
messages.append({"role": "user", "content": message})
messages.append({"role": "assistant", "content": ""})
response_text = ""
# Stream the response token-by-token.
for resp in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = resp.choices[0].delta.content
response_text += token
yield response_text
# Create Gradio components.
# The HTML component displays our custom dropdown.
html_dropdown = gr.HTML(value=dropdown_html)
# The hidden textbox will hold the selected model ID.
hidden_model = gr.Textbox(value=models[0]["id"], visible=False, elem_id="hidden_model")
# Create the Gradio ChatInterface.
# Note: Only components that supply a value are passed to the function.
# The HTML component is for display only.
demo = gr.ChatInterface(
fn=respond,
additional_inputs=[
# hidden_model supplies the model_id.
hidden_model,
gr.Textbox(value="You are a friendly Chatbot.", 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)")
],
# You can include the HTML dropdown in a layout so that it is visible to the user.
layout=[html_dropdown]
)
if __name__ == "__main__":
demo.launch()
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