Spaces:
Paused
Paused
File size: 1,893 Bytes
b5aeae8 |
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 |
import gradio as gr
from huggingface_hub import InferenceClient
import os
from dotenv import load_dotenv
load_dotenv()
HF_TOKEN = os.getenv("HF_TOKEN")
model_list = ["google/gemma-2-2b-it", "google/gemma-2-9b-it", "google/gemma-2-27b-it"]
def respond(
message,
history: list[tuple[str, str]],
model_id,
system_message,
max_tokens,
temperature,
top_p,
):
client = InferenceClient(
model_id,
token=HF_TOKEN,
)
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
gemma_chatbot = gr.ChatInterface(
respond,
additional_inputs=[
gr.Dropdown(
choices=model_list,
label="Model",
value="google/gemma-2-27b-it",
),
gr.Textbox(
value="You are a friendly Chatbot.",
label="System message"
),
gr.Slider(
minimum=1,
maximum=4096,
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)",
),
],
)
|