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import gradio as gr
from huggingface_hub import InferenceClient
# Use a pipeline as a high-level helper
import os
from huggingface_hub import login
from transformers import pipeline
login(token=os.getenv("access_key"))
messages1 = [
{"role": "user", "content": "Who are you?"},
]
#pipe = pipeline("text-generation", model="google/recurrentgemma-2b-it")
#print (pipe(messages1) )
"""
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
"""
#client = InferenceClient(model="google/recurrentgemma-2b-it")
client = pipeline("text-generation", model="google/recurrentgemma-2b-it")
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
#messages = [{"role": "system", "content": system_message}]
messages = []
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 = ""
token = client(
messages, max_new_tokens= max_tokens
)
print(token)
response = token
yield 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 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)",
),
],
)
if __name__ == "__main__":
demo.launch() |