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import gradio as gr
from huggingface_hub import InferenceClient
from datasets import load_dataset
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
dataset = load_dataset("JustKiddo/KiddosVault")
## A translation dataset for Vietnamese responses ##
translation = load_dataset("IWSLT/mt_eng_vietnamese")
trust_remote_code=True ## Trust remote code ##
def translate_text(text, translation_dataset):
# Assuming the translation dataset has a method to translate text
translated_text = translation_dataset['train'][0]['translation']['vi'] # Example translation
return translated_text
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
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
translated_response = translate_text(response, translation)
yield response + "\n\nTranslation: " + translated_response
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a professional Mental Healthcare Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=2048, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=1, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
)
if __name__ == "__main__":
demo.launch() |