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import gradio as gr
from huggingface_hub import InferenceClient
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
"""
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
"""
#Update: Using a new base model
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
dataset = load_dataset("JustKiddo/KiddosVault")
# Load the tokenizer and model for token display
tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-small") #Google's T5 Model
model = AutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-small")
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
#My custom token generator
def generate_tokens(text):
input = tokenizer(text, return_tensors="pt")
output = model.generate(**input)
input_ids = input["input_ids"].tolist()[0]
output_ids = output.tolist()[0]
input_tokens_str = tokenizer.convert_ids_to_tokens(input_ids)
output_tokens_str = output_ids
return " ".join(input_tokens_str), " ".join(output_tokens_str)
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
#chatInterface = gr.ChatInterface(
# respond,
# additional_inputs=[
# gr.Textbox(value="You are a professional Mental Healthcare Chatbot.", label="System message"),
# gr.Slider(minimum=1, maximum=6144, value=6144, 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)",
# ),
# ],
#)
with gr.Blocks() as demo:
with gr.Column():
gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a professional Mental Healthcare Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=6144, value=6144, 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)",
),
],
)
with gr.Row():
input_text = gr.Textbox(label="Input text")
input_tokens = gr.Textbox(label="Input tokens")
output_tokens = gr.Textbox(label="Output tokens")
def update_tokens(input_text):
input_tokens_str, output_tokens_str = generate_tokens(input_text)
return input_tokens_str, output_tokens_str
input_text.change(update_tokens,
inputs=input_text,
outputs=[input_tokens, output_tokens])
if __name__ == "__main__":
demo.launch(debug=True) |