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import gradio as gr
from huggingface_hub import InferenceClient
"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
# Default client with the first model
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
# Function to switch between models based on selection
def switch_client(model_name: str):
return InferenceClient(model_name)
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
model_name # Add this parameter for model selection
):
# Switch client based on model selection
global client
client = switch_client(model_name)
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
# Adding the model name at the end of the response
yield f"\n\n[Response generated by model: {model_name}]"
# Model names and their custom display names
model_choices = [
("HuggingFaceH4/zephyr-7b-beta", "Lake [Test]"),
("google/mt5-base", "Lake 1 Base"),
("google/mt5-large", "Lake 1 Advanced")
]
# Convert model choices into just the model names for the dropdown
model_names = [model[0] for model in model_choices]
# Function to handle model selection and display name for the model
def respond_with_model_name(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
model_name
):
# Find the display name of the selected model
model_display_name = dict(model_choices)[model_name]
# Call the existing respond function
response = list(respond(message, history, system_message, max_tokens, temperature, top_p, model_name))
# Add model name at the end of the response
response[-1] += f"\n\n[Response generated by: {model_display_name}]"
return response
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
respond_with_model_name,
additional_inputs=[
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)",
),
gr.Dropdown(model_names, label="Select Model", value=model_names[0]) # Model selection dropdown
],
)
if __name__ == "__main__":
demo.launch()
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