File size: 3,057 Bytes
80798b1 23b3158 7c1eaf5 80798b1 23b3158 80798b1 23b3158 80798b1 23b3158 80798b1 4759a1e 23b3158 1191c5f 23b3158 4759a1e 23b3158 4759a1e 23b3158 4759a1e 23b3158 4759a1e 23b3158 4759a1e 23b3158 80798b1 4759a1e 80798b1 4759a1e 80798b1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 |
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("mistralai/Mistral-7B-Instruct-v0.3")
# 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
# Model names and their pseudonyms
model_choices = [
("mistralai/Mistral-7B-Instruct-v0.3", "Lake 1 Base")
]
# Convert pseudonyms to model names for the dropdown
pseudonyms = [model[1] for model in model_choices]
# Function to handle model selection and pseudonyms
def respond_with_pseudonym(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
selected_pseudonym
):
# Find the actual model name from the pseudonym
model_name = next(model[0] for model in model_choices if model[1] == selected_pseudonym)
# Call the existing respond function
response = list(respond(message, history, system_message, max_tokens, temperature, top_p, model_name))
# Add pseudonym at the end of the response
response[-1] += f"\n\n[Response generated by: {selected_pseudonym}]"
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_pseudonym,
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(pseudonyms, label="Select Model", value=pseudonyms[0]) # Pseudonym selection dropdown
],
)
if __name__ == "__main__":
demo.launch()
|